Vector processor - Knowledge

313:, which held sixty-four 64-bit words each. The vector instructions were applied between registers, which is much faster than talking to main memory. Whereas the STAR-100 would apply a single operation across a long vector in memory and then move on to the next operation, the Cray design would load a smaller section of the vector into registers and then apply as many operations as it could to that data, thereby avoiding many of the much slower memory access operations. 5108: 1289:

This way, significantly more work can be done in each batch; the instruction encoding is much more elegant and compact as well. The only drawback is that in order to take full advantage of this extra batch processing capacity, the memory load and store speed correspondingly had to increase as well. This is sometimes claimed to be a disadvantage of Cray-style vector processors: in reality it is part of achieving high performance throughput, as seen in

331: 719:, the consequences are that the operations now take longer to complete. If multi-issue is not possible, then the operations take even longer because the LD may not be issued (started) at the same time as the first ADDs, and so on. If there are only 4-wide 64-bit SIMD ALUs, the completion time is even worse: only when all four LOADs have completed may the SIMD operations start, and only when all ALU operations have completed may the STOREs begin. 3330:– either by way of algorithmically loading data from memory, or reordering (remapping) the normally linear access to vector elements, or providing "Accumulators", arbitrary-sized matrices may be efficiently processed. IBM POWER10 provides MMA instructions although for arbitrary Matrix widths that do not fit the exact SIMD size data repetition techniques are needed which is wasteful of register file resources. NVidia provides a high-level Matrix 3238:– Vector architectures with a register-to-register design (analogous to load–store architectures for scalar processors) have instructions for transferring multiple elements between the memory and the vector registers. Typically, multiple addressing modes are supported. The unit-stride addressing mode is essential; modern vector architectures typically also support arbitrary constant strides, as well as the scatter/gather (also called 217: 697: 743:

implementation things are rarely that simple. The data is rarely sent in raw form, and is instead "pointed to" by passing in an address to a memory location that holds the data. Decoding this address and getting the data out of the memory takes some time, during which the CPU traditionally would sit idle waiting for the requested data to show up. As CPU speeds have increased, this

1301:) combine both, by issuing multiple data to multiple internal pipelined SIMD ALUs, the number issued being dynamically chosen by the vector program at runtime. Masks can be used to selectively load and store data in memory locations, and use those same masks to selectively disable processing element of SIMD ALUs. Some processors with SIMD ( 3208:, SIMD by definition avoids inter-lane operations entirely (element 0 can only be added to another element 0), vector processors tackle this head-on. What programmers are forced to do in software (using shuffle and other tricks, to swap data into the right "lane") vector processors must do in hardware, automatically. 2545:

hard-coded constant 16, n is decremented by a hard-coded 4, so initially it is hard to appreciate the significance. The difference comes in the realisation that the vector hardware could be capable of doing 4 simultaneous operations, or 64, or 10,000, it would be the exact same vector assembler for all of them

455:, which is strictly limited to execution of parallel pipelined arithmetic operations only. Although the exact internal details of today's commercial GPUs are proprietary secrets, the MIAOW team was able to piece together anecdotal information sufficient to implement a subset of the AMDGPU architecture. 3472:

The above SIMD example could potentially fault and fail at the end of memory, due to attempts to read too many values: it could also cause significant numbers of page or misaligned faults by similarly crossing over boundaries. In contrast, by allowing the vector architecture the freedom to decide how

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Contrast this situation with SIMD, which is a fixed (inflexible) load width and fixed data processing width, unable to cope with loads that cross page boundaries, and even if they were they are unable to adapt to what actually succeeded, yet, paradoxically, if the SIMD program were to even attempt to

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For Cray-style vector ISAs such as RVV, an instruction called "setvl" (set vector length) is used. The hardware first defines how many data values it can process in one "vector": this could be either actual registers or it could be an internal loop (the hybrid approach, mentioned above). This maximum

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to hold vector data in batches. The batch lengths (vector length, VL) could be dynamically set with a special instruction, the significance compared to Videocore IV (and, crucially as will be shown below, SIMD as well) being that the repeat length does not have to be part of the instruction encoding.

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Having to perform 4-wide simultaneous 64-bit LOADs and 64-bit STOREs is very costly in hardware (256 bit data paths to memory). Having 4x 64-bit ALUs, especially MULTIPLY, likewise. To avoid these high costs, a SIMD processor would have to have 1-wide 64-bit LOAD, 1-wide 64-bit STORE, and only 2-wide

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As of 2016 most commodity CPUs implement architectures that feature fixed-length SIMD instructions. On first inspection these can be considered a form of vector processing because they operate on multiple (vectorized, explicit length) data sets, and borrow features from vector processors. However, by

3384:– elements may typically contain two, three or four sub-elements (vec2, vec3, vec4) where any given bit of a predicate mask applies to the whole vec2/3/4, not the elements in the sub-vector. Sub-vectors are also introduced in RISC-V RVV (termed "LMUL"). Subvectors are a critical integral part of the 446:

the key distinguishing factor of SIMT-based GPUs is that it has a single instruction decoder-broadcaster but that the cores receiving and executing that same instruction are otherwise reasonably normal: their own ALUs, their own register files, their own Load/Store units and their own independent L1

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Moreira, José E.; Barton, Kit; Battle, Steven; Bergner, Peter; Bertran, Ramon; Bhat, Puneeth; Caldeira, Pedro; Edelsohn, David; Fossum, Gordon; Frey, Brad; Ivanovic, Nemanja; Kerchner, Chip; Lim, Vincent; Kapoor, Shakti; Tulio Machado Filho; Silvia Melitta Mueller; Olsson, Brett; Sadasivam, Satish;

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for example, things go rapidly downhill just as they did with the general case of using SIMD for general-purpose IAXPY loops. To sum the four partial results, two-wide SIMD can be used, followed by a single scalar add, to finally produce the answer, but, frequently, the data must be transferred out

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in which the instructions pass through several sub-units in turn. The first sub-unit reads the address and decodes it, the next "fetches" the values at those addresses, and the next does the math itself. With pipelining the "trick" is to start decoding the next instruction even before the first has

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In general terms, CPUs are able to manipulate one or two pieces of data at a time. For instance, most CPUs have an instruction that essentially says "add A to B and put the result in C". The data for A, B and C could be—in theory at least—encoded directly into the instruction. However, in efficient

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Consider both a SIMD processor and a vector processor working on 4 64-bit elements, doing a LOAD, ADD, MULTIPLY and STORE sequence. If the SIMD width is 4, then the SIMD processor must LOAD four elements entirely before it can move on to the ADDs, must complete all the ADDs before it can move on to

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Whereas pure (fixed-width, no predication) SIMD is often mistakenly claimed to be "vector" (because SIMD processes data which happens to be vectors), through close analysis and comparison of historic and modern ISAs, actual vector ISAs may be observed to have the following features that no SIMD ISA

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be the vectorization ratio. If the time taken for the vector unit to add an array of 64 numbers is 10 times faster than its equivalent scalar counterpart, r = 10. Also, if the total number of operations in a program is 100, out of which only 10 are scalar (after vectorization), then f = 0.9, i.e.,

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This begins to hint at the reason why ffirst is so innovative, and is best illustrated by memcpy or strcpy when implemented with standard 128-bit non-predicated non-ffirst SIMD. For IBM POWER9 the number of hand-optimised instructions to implement strncpy is in excess of 240. By contrast, the same

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From the IAXPY example, it can be seen that unlike SIMD processors, which can simplify their internal hardware by avoiding dealing with misaligned memory access, a vector processor cannot get away with such simplification: algorithms are written which inherently rely on Vector Load and Store being

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Implementations in hardware may, if they are certain that the right answer will be produced, perform the reduction in parallel. Some vector ISAs offer a parallel reduction mode as an explicit option, for when the programmer knows that any potential rounding errors do not matter, and low latency is

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Also note, that just like the predicated SIMD variant, the pointers to x and y are advanced by t0 times four because they both point to 32 bit data, but that n is decremented by straight t0. Compared to the fixed-size SIMD assembler there is very little apparent difference: x and y are advanced by

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Realistically, for general-purpose loops such as in portable libraries, where n cannot be limited in this way, the overhead of setup and cleanup for SIMD in order to cope with non-multiples of the SIMD width, can far exceed the instruction count inside the loop itself. Assuming worst-case that the

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here (only start on a multiple of 16) and that n is a multiple of 4, as otherwise some setup code would be needed to calculate a mask or to run a scalar version. It can also be assumed, for simplicity, that the SIMD instructions have an option to automatically repeat scalar operands, like ARM NEON

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This example starts with an algorithm ("IAXPY"), first show it in scalar instructions, then SIMD, then predicated SIMD, and finally vector instructions. This incrementally helps illustrate the difference between a traditional vector processor and a modern SIMD one. The example starts with a 32-bit

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The vector pseudocode example above comes with a big assumption that the vector computer can process more than ten numbers in one batch. For a greater quantity of numbers in the vector register, it becomes unfeasible for the computer to have a register that large. As a result, the vector processor

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to implement vector instructions rather than multiple ALUs. In addition, the design had completely separate pipelines for different instructions, for example, addition/subtraction was implemented in different hardware than multiplication. This allowed a batch of vector instructions to be pipelined

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workloads. Of interest, however, is that speed is far more important than accuracy in 3D for GPUs, where computation of pixel coordinates simply do not require high precision. The Vulkan specification recognises this and sets surprisingly low accuracy requirements, so that GPU Hardware can reduce

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first have to have a preparatory section which works on the beginning unaligned data, up to the first point where SIMD memory-aligned operations can take over. this will either involve (slower) scalar-only operations or smaller-sized packed SIMD operations. Each copy implements the full algorithm

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To illustrate what a difference this can make, consider the simple task of adding two groups of 10 numbers together. In a normal programming language one would write a "loop" that picked up each of the pairs of numbers in turn, and then added them. To the CPU, this would look something like this:

302:, but at data-related tasks they could keep up while being much smaller and less expensive. However the machine also took considerable time decoding the vector instructions and getting ready to run the process, so it required very specific data sets to work on before it actually sped anything up. 2296:

Here it can be seen that the code is much cleaner but a little complex: at least, however, there is no setup or cleanup: on the last iteration of the loop, the predicate mask wil be set to either 0b0000, 0b0001, 0b0011, 0b0111 or 0b1111, resulting in between 0 and 4 SIMD element operations being

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Vector processors take this concept one step further. Instead of pipelining just the instructions, they also pipeline the data itself. The processor is fed instructions that say not just to add A to B, but to add all of the numbers "from here to here" to all of the numbers "from there to there".

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The basic ASC (i.e., "one pipe") ALU used a pipeline architecture that supported both scalar and vector computations, with peak performance reaching approximately 20 MFLOPS, readily achieved when processing long vectors. Expanded ALU configurations supported "two pipes" or "four pipes" with a

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adding those numbers in parallel. The checking of dependencies between those numbers is not required as a vector instruction specifies multiple independent operations. This simplifies the control logic required, and can further improve performance by avoiding stalls. The math operations thus

3280:– a less restrictive more generic variation of the compress/expand theme which instead takes one vector to specify the indices to use to "reorder" another vector. Gather/scatter is more complex to implement than compress/expand, and, being inherently non-sequential, can interfere with 2499:

This is essentially not very different from the SIMD version (processes 4 data elements per loop), or from the initial Scalar version (processes just the one). n still contains the number of data elements remaining to be processed, but t0 contains the copy of VL – the number that is

3270:– usually using a bit-mask, data is linearly compressed or expanded (redistributed) based on whether bits in the mask are set or clear, whilst always preserving the sequential order and never duplicating values (unlike Gather-Scatter aka permute). These instructions feature in 1669:

The STAR-like code remains concise, but because the STAR-100's vectorisation was by design based around memory accesses, an extra slot of memory is now required to process the information. Two times the latency is also needed due to the extra requirement of memory access.

1332:(SIMT). SIMT units run from a shared single broadcast synchronised Instruction Unit. The "vector registers" are very wide and the pipelines tend to be long. The "threading" part of SIMT involves the way data is handled independently on each of the compute units. 686:, almost qualifies as a vector processor. Predicated SIMD uses fixed-width SIMD ALUs but allows locally controlled (predicated) activation of units to provide the appearance of variable length vectors. Examples below help explain these categorical distinctions. 1972:

Eight-wide SIMD requires repeating the inner loop algorithm first with four-wide SIMD elements, then two-wide SIMD, then one (scalar), with a test and branch in between each one, in order to cover the first and last remaining SIMD elements (0 <= n <= 7).

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Even with a general loop (n not fixed), the only way to use 4-wide SIMD is to assume four separate "streams", each offset by four elements. Finally, the four partial results have to be summed. Other techniques involve shuffle: examples online can be found for

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Aside from the size of the program and the complexity, an additional potential problem arises if floating-point computation is involved: the fact that the values are not being summed in strict order (four partial results) could result in rounding errors.

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iterations of the loop the batches of vectorised memory reads are optimally aligned with the underlying caches and virtual memory arrangements. Additionally, the hardware may choose to use the opportunity to end any given loop iteration's memory reads

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on a page boundary (avoiding a costly second TLB lookup), with speculative execution preparing the next virtual memory page whilst data is still being processed in the current loop. All of this is determined by the hardware, not the program itself.

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It is clear how predicated SIMD at least merits the term "vector capable", because it can cope with variable-length vectors by using predicate masks. The final evolving step to a "true" vector ISA, however, is to not have any evidence in the ISA

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prefix. However, only very simple calculations can be done effectively in hardware this way without a very large cost increase. Since all operands have to be in memory for the STAR-100 architecture, the latency caused by access became huge too.

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number that can be processed by the hardware in subsequent vector instructions, and sets the internal special register, "VL", to that same amount. ARM refers to this technique as "vector length agnostic" programming in its tutorials on SVE2.

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machine with 256 ALUs, but, when it was finally delivered in 1972, it had only 64 ALUs and could reach only 100 to 150 MFLOPS. Nevertheless, it showed that the basic concept was sound, and, when used on data-intensive applications, such as

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Not only is it a much more compact program (saving on L1 Cache size), but as previously mentioned, the vector version can issue far more data processing to the ALUs, again saving power because Instruction Decode and Issue can sit idle.

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Compared to any SIMD processor claiming to be a vector processor, the order of magnitude reduction in program size is almost shocking. However, this level of elegance at the ISA level has quite a high price tag at the hardware level:

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SIMD instruction sets lack crucial features when compared to vector instruction sets. The most important of these is that vector processors, inherently by definition and design, have always been variable-length since their inception.

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Where with predicated SIMD the mask bitlength is limited to that which may be held in a scalar (or special mask) register, vector ISA's mask registers have no such limitation. Cray-I vectors could be just over 1,000 elements (in

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Vector processors on the other hand are designed to issue computations of variable length for an arbitrary count, n, and thus require very little setup, and no cleanup. Even compared to those SIMD ISAs which have masks (but no

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2 module within a card that physically resembles a graphics coprocessor, but instead of serving as a co-processor, it is the main computer with the PC-compatible computer into which it is plugged serving support functions.

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The simplicity of the algorithm is stark in comparison to SIMD. Again, just as with the IAXPY example, the algorithm is length-agnostic (even on Embedded implementations where maximum vector length could be only one).

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Instead of constantly having to decode instructions and then fetch the data needed to complete them, the processor reads a single instruction from memory, and it is simply implied in the definition of the instruction

730:, the ability to run MULTIPLY simultaneously with ADD), may complete the four operations faster than a SIMD processor with 1-wide LOAD, 1-wide STORE, and 2-wide SIMD. This more efficient resource utilization, due to 191:(DAP) design, categorising the ILLIAC and DAP as cellular array processors that potentially offered substantial performance benefits over conventional vector processor designs such as the CDC STAR-100 and Cray 1. 1348:

IV is also capable of this hybrid approach: nominally stating that its SIMD QPU Engine supports 16-long FP array operations in its instructions, it actually does them 4 at a time, as (another) form of "threads".

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This example again highlights a key critical fundamental difference between true vector processors and those SIMD processors, including most commercial GPUs, which are inspired by features of vector processors.

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Introduced in ARM SVE2 and RISC-V RVV is the concept of speculative sequential Vector Loads. ARM SVE2 has a special register named "First Fault Register", where RVV modifies (truncates) the Vector Length (VL).

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Modern SIMD computers claim to improve on early Cray by directly using multiple ALUs, for a higher degree of parallelism compared to only using the normal scalar pipeline. Modern vector processors (such as the

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data caches. Thus although all cores simultaneously execute the exact same instruction in lock-step with each other they do so with completely different data from completely different memory locations. This is

1313:) processing, and it is these which somewhat deserve the nomenclature "vector processor" or at least deserve the claim of being capable of "vector processing". SIMD processors without per-element predication ( 2846:

This is where the problems start. SIMD by design is incapable of doing arithmetic operations "inter-element". Element 0 of one SIMD register may be added to Element 0 of another register, but Element 0 may

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may use fewer vector units than the width implies: instead of having 64 units for a 64-number-wide register, the hardware might instead do a pipelined loop over 16 units for a hybrid approach. The Broadcom

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find out in advance (in each inner loop, every time) what might optimally succeed, those instructions only serve to hinder performance because they would, by necessity, be part of the critical inner loop.

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This example starts with an algorithm which involves reduction. Just as with the previous example, it will be first shown in scalar instructions, then SIMD, and finally vector instructions, starting in

173:, the ILLIAC was the fastest machine in the world. The ILLIAC approach of using separate ALUs for each data element is not common to later designs, and is often referred to under a separate category, 2006:

Without predication, the wider the SIMD width the worse the problems get, leading to massive opcode proliferation, degraded performance, extra power consumption and unnecessary software complexity.

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adding up many numbers in a row. The more complex instructions also add to the complexity of the decoders, which might slow down the decoding of the more common instructions such as normal adding. (

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Saleil, Baptiste; Schmidt, Bill; Srinivasaraghavan, Rajalakshmi; Srivatsan, Shricharan; Thompto, Brian; Wagner, Andreas; Wu, Nelson (2021). "A matrix math facility for Power ISA(TM) processors".

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field, but unlike the STAR-100 which uses memory for its repeats, the Videocore IV repeats are on all operations including arithmetic vector operations. The repeat length can be a small range of

327:. The Cray-1 normally had a performance of about 80 MFLOPS, but with up to three chains running it could peak at 240 MFLOPS and averaged around 150 – far faster than any machine of the era. 3377:

operations as well as short vectors for common operations (RGB, ARGB, XYZ, XYZW) support for the following is typically present in modern GPUs, in addition to those found in vector processors:

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On calling setvl with the number of outstanding data elements to be processed, "setvl" is permitted (essentially required) to limit that to the Maximum Vector Length (MVL) and thus returns the

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not make the mistake of assuming a fixed vector width: consequently MVL is not a quantity that the programmer needs to know. This can be a little disconcerting after years of SIMD mindset).

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This is very straightforward. "y" starts at zero, 32 bit integers are loaded one at a time into r1, added to y, and the address of the array "x" moved on to the next element in the array.

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Additionally, the number of elements going in to the function can start at zero. This sets the vector length to zero, which effectively disables all vector instructions, turning them into

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Below is the Cray-style vector assembler for the same SIMD style loop, above. Note that t0 (which, containing a convenient copy of VL, can vary) is used instead of hard-coded constants:

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amount loaded to either the amount that would succeed without raising a memory fault or simply to an amount (greater than zero) that is most convenient. The important factor is that

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Over time as the ISA evolves to keep increasing performance, it results in ISA Architects adding 2-wide SIMD, then 4-wide SIMD, then 8-wide and upwards. It can therefore be seen why

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Assuming a hypothetical predicated (mask capable) SIMD ISA, and again assuming that the SIMD instructions can cope with misaligned data, the instruction loop would look like this:

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Additionally, vector processors can be more resource-efficient by using slower hardware and saving power, but still achieving throughput and having less latency than SIMD, through

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Vector processors were traditionally designed to work best only when there are large amounts of data to be worked on. For this reason, these sorts of CPUs were found primarily in

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instruction), Vector processors produce much more compact code because they do not need to perform explicit mask calculation to cover the last few elements (illustrated below).

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Not all problems can be attacked with this sort of solution. Including these types of instructions necessarily adds complexity to the core CPU. That complexity typically makes

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amount (the number of hardware "lanes") is termed "MVL" (Maximum Vector Length). Note that, as seen in SX-Aurora and Videocore IV, MVL may be an actual hardware lane quantity

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performed, respectively. One additional potential complication: some RISC ISAs do not have a "min" instruction, needing instead to use a branch or scalar predicated compare.

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instructions are notified or may determine exactly how many Loads actually succeeded, using that quantity to only carry out work on the data that has actually been loaded.

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Unfortunately for SIMD, the clue was in the assumption above, "that n is a multiple of 4" as well as "aligned access", which, clearly, is a limited specialist use-case.

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that the instruction will operate again on another item of data, at an address one increment larger than the last. This allows for significant savings in decoding time.

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The self-repeating instructions are found in early vector computers like the STAR-100, where the above action would be described in a single instruction (somewhat like

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With the length (equivalent to SIMD width) not being hard-coded into the instruction, not only is the encoding more compact, it's also "future-proof" and allows even

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Although vector supercomputers resembling the Cray-1 are less popular these days, NEC has continued to make this type of computer up to the present day with their

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in the SIMD width (load32x4 etc.) the vector ISA equivalents have no such limit. This makes vector programs both portable, Vendor Independent, and future-proof.

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either gains the ability to perform loops itself, or exposes some sort of vector control (status) register to the programmer, usually known as a vector Length.

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Note that both x and y pointers are incremented by 16, because that is how long (in bytes) four 32-bit integers are. The decision was made that the algorithm

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API although the internal details are not available. The most resource-efficient technique is in-place reordering of access to otherwise linear vector data.

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machine, but it sold poorly and they took that as an opportunity to leave the supercomputing field entirely. In the early and mid-1980s Japanese companies (

30:) that were specifically designed from the ground up to handle large Vectors (Arrays). For SIMD instructions present in some general-purpose computers, see 3254:

containing multiple members. The members are extracted from data structure (element), and each extracted member is placed into a different vector register.

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In each iteration, every element of y has an element of x multiplied by a and added to it. The program is expressed in scalar linear form for readability.

3304:– a very simple and strategically useful instruction which drops sequentially-incrementing immediates into successive elements. Usually starts from zero. 504:. The difference is illustrated below with examples, showing and comparing the three categories: Pure SIMD, Predicated SIMD, and Pure Vector Processing. 1189:

Additionally, in more modern vector processor ISAs, "Fail on First" or "Fault First" has been introduced (see below) which brings even more advantages.

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many elements to load, the first part of a strncpy, if beginning initially on a sub-optimal memory boundary, may return just enough loads such that on

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The code itself is also smaller, which can lead to more efficient memory use, reduction in L1 instruction cache size, reduction in power consumption.

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The code when n is larger than the maximum vector length is not that much more complex, and is a similar pattern to the first example ("IAXPY").

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than another Element 0. This places some severe limitations on potential implementations. For simplicity it can be assumed that n is exactly 8:

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of the sub-vector, heavily features in 3D Shader binaries, and is sufficiently important as to be part of the Vulkan SPIR-V spec. The Broadcom

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or decimal fixed-point, and support for much larger (arbitrary precision) arithmetic operations by supporting parallel carry-in and carry-out

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perform the aligned SIMD loop at the maximum SIMD width up until the last few elements (those remaining that do not fit the fixed SIMD width)

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processing rather than better implementations of vector processors. However, recognising the benefits of vector processing, IBM developed

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to be processed in each iteration. t0 is subtracted from n after each iteration, and if n is zero then all elements have been processed.

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Throughout, Cray continued to be the performance leader, continually beating the competition with a series of machines that led to the

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allow parallel if/then/else constructs without resorting to branches. This allows code with conditional statements to be vectorized.

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the MULTIPLYs, and likewise must complete all of the MULTIPLYs before it can start the STOREs. This is by definition and by design.

6138: 4310: 682:) which is comprehensive individual element-level predicate masks on every vector instruction as is now available in ARM SVE2. And 145:(ALUs), one per cycle, but with a different data point for each one to work on. This allowed the Solomon machine to apply a single 3453:

The basic principle of ffirst is to attempt a large sequential Vector Load, but to allow the hardware to arbitrarily truncate the

2560:, at runtime. Thus, unlike non-predicated SIMD, even when there are no elements to process there is still no wasted cleanup code. 1746:), can do most of the operation in batches. The code is mostly similar to the scalar version. It is assumed that both x and y are 622:

Other CPU designs include some multiple instructions for vector processing on multiple (vectorized) data sets, typically known as

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is crucial to the performance. This ratio depends on the efficiency of the compilation like adjacency of the elements in memory.

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only three address translations are needed. Depending on the architecture, this can represent a significant savings by itself.

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on a vector (for example, find the one maximum value of an entire vector, or sum all elements). Iteration is of the form

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to the ISA. If it is assumed that n is less or equal to the maximum vector length, only three instructions are required:

1329: 1310: 1240: 361:(NEC) introduced register-based vector machines similar to the Cray-1, typically being slightly faster and much smaller. 2549:. Even compared to the predicate-capable SIMD, it is still more compact, clearer, more elegant and uses less resources. 5185: 4684: 3652: 3231:

Where many SIMD ISAs borrow or are inspired by the list below, typical features that a vector processor will have are:

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follows similar principles as the early vector processors, and is being implemented in commercial products such as the

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The scalar version of this would load one of each of x and y, process one calculation, store one result, and loop:

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variants of the standard vector load and stores. Segment loads read a vector from memory, where each element is a

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Another saving is fetching and decoding the instruction itself, which has to be done only one time instead of ten.

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Cray-style vector ISAs take this a step further and provide a global "count" register, called vector length (VL):

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in Flynn's Taxonomy. Common examples using SIMD with features inspired by vector processors include: Intel x86's

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is constantly in use. Any particular instruction takes the same amount of time to complete, a time known as the

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designs to consider using vectors purely to gain all the other advantages, rather than go for high performance.

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Memory Load/Store modes, Gather/scatter vector operations act on the vector registers, and are often termed a

442:, and can be considered vector processors (using a similar strategy for hiding memory latencies). As shown in 6215: 6063: 6036: 5897: 5511: 5472: 5329: 5093: 4927: 4778: 4465: 3406: 1751:

can. If it does not, a "splat" (broadcast) must be used, to copy the scalar argument across a SIMD register:

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corresponding 2X or 4X performance gain. Memory bandwidth was sufficient to support these expanded modes.

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completed far faster overall, the limiting factor being the time required to fetch the data from memory.

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In order to reduce the amount of time consumed by these steps, most modern CPUs use a technique known as

397: 358: 188: 81:(SWAR) Arithmetic Units. Vector processors can greatly improve performance on certain workloads, notably 4253: 3831:

An algorithm of hardware unit generation for processor core synthesis with packed SIMD type instructions

133:

project. Solomon's goal was to dramatically increase math performance by using a large number of simple

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increase in instruction count! This can easily be demonstrated by compiling the iaxpy example for

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Modern GPUs, which have many small compute units each with their own independent SIMD ALUs, use

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power usage. The concept of reducing accuracy where it is simply not needed is explored in the

1968:

have a cleanup phase which, like the preparatory section, is just as large and just as complex.

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which has performed 10 sequential operations: effectively the loop count is on an explicit

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and uses no SIMD ALUs, only having 1-wide 64-bit LOAD, 1-wide 64-bit STORE (and, as in the

400:

for use in supercomputers coupling several scalar processors to act as a vector processor.

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These stark differences are what distinguishes a vector processor from one that has SIMD.

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computing. Around this time Flynn categorized this type of processing as an early form of

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A number of things to note, when comparing against the Predicated SIMD assembly variant:

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principles: RVV only adds around 190 vector instructions even with the advanced features.

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to cope with iteration and reduction. This is illustrated further with examples, below.

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of vector ISAs brings other benefits which are compelling even for Embedded use-cases.

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Thus it can be seen, very clearly, how vector ISAs reduce the number of instructions.

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definition, the addition of SIMD cannot, by itself, qualify a processor as an actual

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90% of the work is done by the vector unit. It follows the achievable speed up of:

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IV uses the terminology "Lane rotate" where the rest of the industry uses the term

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Where the SIMD variant hard-coded both the width (4) into the creation of the mask

1950:

only cope with 4-wide SIMD, therefore the constant is hard-coded into the program.

468: 310: 70: 20: 4277: 3833:. Asia-Pacific Conference on Circuits and Systems. Vol. 1. pp. 171–176. 6939: 6924: 6872: 6776: 6751: 6588: 6581: 6432: 6427: 6422: 6361: 6269: 6259: 5981: 5816: 5768: 5531: 5415: 5383: 5284: 5279: 5200: 4947: 4887: 4822: 4669: 4659: 4592: 4582: 4424: 4414: 3647: 2972:

of dedicated SIMD registers before the last scalar computation can be performed.

1957:

hardware cannot do misaligned SIMD memory accesses, a real-world algorithm will:

1193: 764: 603: 156:

In 1962, Westinghouse cancelled the project, but the effort was restarted by the

54: 4229: 4218:"A Modular Massively Parallel Processor for Volumetric Visualisation Processing" 3838: 7050: 6884: 6867: 6860: 6756: 6613: 6350: 6264: 6195: 5778: 5740: 5689: 5684: 5679: 5393: 5217: 5078: 4894: 4551: 4444: 3913: 3663: 3309: 3285: 3251: 1265:

Interestingly, though, Broadcom included space in all vector operations of the

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Flynn's taxonomy § Single instruction stream, multiple data streams (SIMD)

2959:- Fourth SIMD ADD: element 3 of first group added to element 2 of second group 2947:- Second SIMD ADD: element 1 of first group added to element 1 of second group 463:

Several modern CPU architectures are being designed as vector processors. The

187:

sought to avoid many of the difficulties with the ILLIAC concept with its own

7070: 6845: 6761: 5801: 5783: 5576: 5269: 4967: 4844: 4093:"Assembly - Fastest way to do horizontal SSE vector sum (Or other reduction)" 3416: 3374: 2953:- Third SIMD ADD: element 2 of first group added to element 2 of second group 2941:- First SIMD ADD: element 0 of first group added to element 0 of second group 1220: 760: 97: 5704: 3971:

Computer Organization and Design: the Hardware/Software Interface page 751-2

7055: 6993: 6809: 6786: 6598: 6319: 5257: 4567: 3469:

strncpy routine in hand-optimised RVV assembler is a mere 22 instructions.

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Here, an accumulator (y) is used to sum up all the values in the array, x.

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But more than that, a high performance vector processor may have multiple

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The time taken would be basically the same as a vector implementation of

1063:

Note the complete lack of looping in the instructions, because it is the

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The STAR-100 was otherwise slower than CDC's own supercomputers like the

134: 2708:

The scalar version of this would load each of x, add it to y, and loop:

1178:

With the program size being reduced branch prediction has an easier job.

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Miyaoka, Y.; Choi, J.; Togawa, N.; Yanagisawa, M.; Ohtsuki, T. (2002).

3431:

obviously feature much more predominantly in 3D than in many demanding

3242:) addressing mode. Advanced architectures may also include support for 793:; assume a, b, and c are memory locations in their respective registers 4171:"IBM's POWER10 Processor - William Starke & Brian W. Thompto, IBM" 3847: 330: 6817: 6814: 6556: 5626: 5604: 4962: 4937: 4354: 3808:"Andes Announces RISC-V Multicore 1024-bit Vector Processor: AX45MPV" 3428: 3402: 3314: 1345: 1336: 1266: 480: 389: 385: 305:

The vector technique was first fully exploited in 1976 by the famous

161: 146: 38: 216: 112:

designs led to a decline in vector supercomputers during the 1990s.

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But to a vector processor, this task looks considerably different:

536: 411:

places the processor and either 24 or 48 gigabytes of memory on an

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Computer processor which works on arrays of several numbers at once

4222:

High Performance Computing for Computer Graphics and Visualisation

3204:

Whilst from the reduction example it can be seen that, aside from

1742:

A modern packed SIMD architecture, known by many names (listed in

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the size of the code, in fact in extreme cases it results in an

641: 392:. Since then, the supercomputer market has focused much more on 85:

and similar tasks. Vector processing techniques also operate in

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successful, regardless of alignment of the start of the vector.

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has historically become a large impediment to performance; see

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The history of computer technology in their faces (in Russian)

125:

Vector processing development began in the early 1960s at the

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So, even if the performance of the vector unit is very high (

2992:

Vector instruction sets have arithmetic reduction operations

2557: 291:(ASC), which were introduced in 1974 and 1972, respectively. 141:(CPU). The CPU fed a single common instruction to all of the 100:

design through the 1970s into the 1990s, notably the various

61:

are designed to operate efficiently and effectively on large

3219:

simplified software and complex hardware (vector processors)

1210:

This can be somewhat mitigated by keeping the entire ISA to

796:; add 10 numbers in a to 10 numbers in b, store results in c 6978: 6126: 6046: 5636: 5022: 4952: 4942: 3828: 3420: 3331: 2305:

of a SIMD width, leaving that entirely up to the hardware.

1236: 1211: 663:

instruction in NEC SX, without restricting the length to a

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vector processor architectures being developed, including

96:

Vector machines appeared in the early 1970s and dominated

19:"Array processor" redirects here. Not to be confused with 5566: 5556: 4932: 4909: 4107:"Riscv-v-spec/V-spec.adoc at master · riscv/Riscv-v-spec" 4001:"Riscv-v-spec/V-spec.adoc at master · riscv/Riscv-v-spec" 3863:"Riscv-v-spec/V-spec.adoc at master · riscv/Riscv-v-spec" 3691:, an open ISA standard with an associated variable width 1290: 1254: 556: 194: 27: 626:(Multiple Instruction, Multiple Data) and realized with 164:. Their version of the design originally called for a 1 3784: 3726: 689:

SIMD, because it uses fixed-width batch processing, is

486: 321:

into each of the ALU subunits, a technique they called

1309:) are capable of this kind of selective, per-element ( 715:

64-bit ALUs. As shown in the diagram, which assumes a

4079:"Sse - 1-to-4 broadcast and 4-to-1 reduce in AVX-512" 3598: 3572: 3512: 1737: 1165:

There are several savings inherent in this approach.

345:

tried to re-enter the high-end market again with its

337:

processor module with four scalar/vector processors

3968: 3883:"Vector Engine Assembly Language Reference Manual" 3624: 3584: 3556: 638:VLIW/vector processor combines both technologies. 3959: 3364: 1339:IV and other external vector processors like the 634:(Explicitly Parallel Instruction Computing). The 120: 7068: 4043:"Coding for Neon - Part 3 Matrix Multiplication" 203:was presented and developed by Kartsev in 1967. 3810:(Press release). GlobeNewswire. 7 December 2022 3759: 3216:complex software and simplified hardware (SIMD) 722:A vector processor, by contrast, even if it is 4322:PATCH to libc6 to add optimised POWER9 strncpy 3212:Overall then there is a choice to either have 5139: 4370: 2934:At this point four adds have been performed: 2547:and there would still be no SIMD cleanup code 1352: 1277:or sourced from one of the scalar registers. 1204:instructions run slower—i.e., whenever it is 1079:; again assume we have vector registers v1-v3 642:Difference between SIMD and vector processors 3226: 2533:that is automatically applied to the vectors 1358:integer variant of the "DAXPY" function, in 655:a way to set the vector length, such as the 26:This article is about Processors (including 6144:Computer performance by orders of magnitude 3486: 2563: 245:. Unsourced material may be challenged and 5153: 5146: 5132: 4377: 4363: 158:University of Illinois at Urbana–Champaign 4200: 3975:(2nd ed.). Morgan Kaufmann. p. 3846: 3753: 3732: 3190: 2308: 265:Learn how and when to remove this message 4215: 4159:RVV register gather-scatter instructions 3927:Vector and SIMD processors, slides 12-13 3444: 2096:# now do the operation, masked by m bits 2030:# prepare mask. few ISAs have min though 695: 369:(FPS) built add-on array processors for 329: 277:The first vector supercomputers are the 2987: 1335:In addition, GPUs such as the Broadcom 965:; assume we have vector registers v1-v3 749:Random-access memory § Memory wall 667:or to a multiple of a fixed data width. 7069: 4384: 4278:Abandoned US patent US20110227920-0096 4031:Videocore IV QPU analysis by Jeff Bush 3938:Array vs Vector Processing, slides 5-7 3684:Computer for operations with functions 3278:Register Gather, Scatter (aka permute) 1230: 1082:; with size larger than or equal to 10 670:Iteration and reduction over elements 201:computer for operations with functions 195:Computer for operations with functions 5127: 4358: 2963:but with 4-wide SIMD being incapable 2703: 2515:instruction has embedded within it a 1293:, which face exactly the same issue. 458: 137:under the control of a single master 6115:Floating-point operations per second 487:Comparison with modern architectures 426:Single instruction, multiple threads 243:adding citations to reliable sources 210: 179:single instruction, multiple threads 1492: 1330:Single Instruction Multiple Threads 1241:Single Instruction Multiple Threads 968:; with size equal or larger than 10 759:left the CPU, in the fashion of an 13: 3733:Parkinson, Dennis (17 June 1976). 3579: 2017: 1738:Pure (non-predicated, packed) SIMD 430:Modern graphics processing units ( 14: 7103: 2841: 2207:# update x, y and n for next loop 952:; loop back if count is not yet 0 659:instruction in RISCV RVV, or the 630:(Very Long Instruction Word) and 407:of computers. Most recently, the 206: 127:Westinghouse Electric Corporation 104:platforms. The rapid fall in the 75:single instruction, multiple data 7041:Semiconductor device fabrication 5107: 5106: 4183:from the original on 2021-12-11. 4020:Videocore IV Programmer's Manual 3949:SIMD vs Vector GPU, slides 22-24 3632:, which suggests that the ratio 471:AX45MPV. There are also several 215: 7016:History of general-purpose CPUs 5243:Nondeterministic Turing machine 4578:Analysis of parallel algorithms 4337: 4326: 4315: 4304: 4293: 4282: 4271: 4260: 4246: 4209: 4187: 4163: 4152: 4141: 4130: 4119: 4099: 4085: 4071: 4060: 4049: 4035: 4024: 4013: 3993: 3953: 3942: 3931: 3920: 3906: 3900:"Documentation – Arm Developer" 3592:) there is a speedup less than 3321:where Reduction is of the form 2968: 2956: 2950: 2944: 2938: 2531:creates a hidden predicate mask 2516: 2512: 2011: 1989: 1774: 1270: 1258: 1250: 660: 656: 511:- also known as "Packed SIMD", 451:more complex and involved than 185:International Computers Limited 153:, fed in the form of an array. 5196:Deterministic finite automaton 3892: 3875: 3855: 3822: 3800: 3789: 3778: 3655:on pipelined vector processors 3619: 3607: 3551: 3536: 3524: 3521: 3497:be the vector speed ratio and 3365:GPU vector processing features 2980:of how to do "Horizontal Sum" 1253:). They are also found in the 737: 121:Early research and development 1: 5987:Simultaneous and heterogenous 4525:Simultaneous and heterogenous 3785:MIAOW Vertical Research Group 3719: 1675:; Assume tmp is pre-allocated 1284:introduced the idea of using 614:. Although memory-based, the 598:- these include the original 6671:Integrated memory controller 6653:Translation lookaside buffer 5852:Memory dependence prediction 5295:Random-access stored program 5248:Probabilistic Turing machine 5113:Category: Parallel computing 4344:ARM SVE2 paper by N. Stevens 4254:"CUDA C++ Programming Guide" 4216:Krikelis, Anargyros (1996). 3679:Chaining (vector processing) 3344:arithmetic, but can include 618:was also a vector processor. 289:Advanced Scientific Computer 171:computational fluid dynamics 7: 6127:Synaptic updates per second 4230:10.1007/978-1-4471-1011-8_8 3839:10.1109/APCCAS.2002.1114930 3735:"Computers by the thousand" 3641: 790:; Hypothetical RISC machine 717:multi-issue execution model 567:collaborated to create the 398:Virtual Vector Architecture 373:, later building their own 359:Nippon Electric Corporation 189:Distributed Array Processor 10: 7108: 6531:Heterogeneous architecture 5453:Orthogonal instruction set 5223:Alternating Turing machine 5211:Quantum cellular automaton 4420:High-performance computing 3714:Supercomputer architecture 3313:– operations that perform 3284:. Not to be confused with 1990:"-O3 -march=knl" 1353:Vector instruction example 1234: 423: 115: 106:price-to-performance ratio 25: 18: 7021:Microprocessor chronology 7008: 6984:Dynamic frequency scaling 6957: 6893: 6831: 6785: 6737: 6692: 6612: 6539: 6508: 6413: 6334: 6298: 6252: 6152: 6139:Cache performance metrics 6078: 6012: 5962: 5873: 5864: 5837: 5792: 5759: 5731: 5722: 5542: 5445: 5434: 5305: 5161: 5102: 5054:Automatic parallelization 5046: 4908: 4748: 4698: 4690:Application checkpointing 4652: 4616: 4560: 4504: 4453: 4392: 3709:History of supercomputing 3585:{\displaystyle r=\infty } 3227:Vector processor features 1091:# Set vector length VL=10 678:Predicated SIMD (part of 341:Other examples followed. 69:. This is in contrast to 53:(CPU) that implements an 7036:Hardware security module 6379:Digital signal processor 6356:Graphics processing unit 6168:Graphics processing unit 4300:Introduction to ARM SVE2 3760:B.N. Malinovsky (1995). 3487:Performance and speed up 3050: 2998: 2857: 2710: 2575: 2564:Vector reduction example 2338: 2024: 1779: 1753: 1672: 1499: 1364: 1325:) categorically do not. 1076: 962: 787: 343:Control Data Corporation 279:Control Data Corporation 7077:Central processing unit 6989:Dynamic voltage scaling 6772:Memory address register 6666:Branch target predictor 6630:Address generation unit 6373:Physics processing unit 6162:Central processing unit 6121:Transactions per second 6109:Instructions per second 6032:Array processing (SIMT) 5176:Stored-program computer 5069:Embarrassingly parallel 5064:Deterministic algorithm 4056:SIMD considered harmful 3674:Automatic vectorization 3625:{\displaystyle 1/(1-f)} 3328:Matrix Multiply support 3167:# repeat if n != 0 2821:; loop back if n > 0 2725:; y initialised to zero 2529:Setting VL effectively 2494:# repeat if n != 0 1658:; loop back if n > 0 465:RISC-V vector extension 438:which may be driven by 139:Central processing unit 51:central processing unit 6795:Hardwired control unit 6677:Memory management unit 6642:Memory management unit 6391:Secure cryptoprocessor 6385:Tensor Processing Unit 6367:Vision processing unit 6101:Cycles per instruction 6095:Instructions per cycle 6042:Associative processing 5733:Instruction pipelining 5155:Processor technologies 4784:Associative processing 4740:Non-blocking algorithm 4546:Clustered multi-thread 3704:Tensor Processing Unit 3626: 3586: 3558: 3191:Insights from examples 2309:Pure (true) vector ISA 756:instruction pipelining 700: 579:associative processing 513:SIMD within a register 434:) include an array of 419: 367:Floating Point Systems 338: 143:arithmetic logic units 79:SIMD within a register 63:one-dimensional arrays 7092:Vector supercomputers 6878:Sum-addressed decoder 6624:Arithmetic logic unit 5751:Classic RISC pipeline 5705:Epiphany architecture 5552:Motorola 68000 series 4900:Hardware acceleration 4813:Superscalar processor 4803:Dataflow architecture 4400:Distributed computing 4311:RVV fault-first loads 3914:"Vector Architecture" 3627: 3587: 3559: 3445:Fault (or Fail) First 3373:applications needing 3338:Advanced Math formats 3246:load and stores, and 3236:Vector Load and Store 2851:be added to anything 2743:; load one 32bit data 2285:; go back if n > 0 2093:; m = (1<<t0)-1 1935:; go back if n > 0 1517:; load one 32bit data 1341:NEC SX-Aurora TSUBASA 699: 571:, which is also SIMD. 333: 316:The Cray design used 91:graphics accelerators 6999:Performance per watt 6577:replacement policies 6243:Package on a package 6133:Performance per watt 6037:Pipelined processing 5807:Tomasulo's algorithm 5612:Clipper architecture 5468:Application-specific 5181:Finite-state machine 4779:Pipelined processing 4728:Explicit parallelism 4723:Implicit parallelism 4713:Dataflow programming 4224:. pp. 101–124. 4045:. 11 September 2013. 3596: 3570: 3510: 3346:binary-coded decimal 3206:permute instructions 2988:Vector ISA reduction 2228:; x := x + t0*4 2186:; v3 := v1 + v2 1988:, using the options 1848:; v3 := v1 + v2 1571:; r3 := r1 + r2 1257:architecture as the 859:; r3 := r1 + r2 594:- as categorised in 531:instructions, AMD's 318:pipeline parallelism 239:improve this section 83:numerical simulation 7031:Digital electronics 6684:Instruction decoder 6636:Floating-point unit 6290:Soft microprocessor 6237:System in a package 5812:Reservation station 5342:Transport-triggered 5003:Parallel Extensions 4808:Pipelined processor 4333:RVV strncpy example 4115:. 19 November 2022. 4009:. 19 November 2022. 3961:Patterson, David A. 3419:operations such as 3290:permute instruction 3268:Compress and Expand 3137:# advance x by VL*4 3080:# VL=t0=min(MVL, n) 3043:# reduce-add into y 3013:# VL=t0=min(MVL, n) 2833:; returns result, y 2464:# advance x by VL*4 2443:# advance y by VL*4 2356:# VL=t0=min(MVL, n) 2159:; v1 := v1 * a 1827:; v1 := v1 * a 1550:; r1 := r1 * a 1286:processor registers 1231:Vector instructions 517:Pipelined Processor 7087:Parallel computing 6903:Integrated circuit 6747:Processor register 6401:Baseband processor 5746:Operand forwarding 5206:Cellular automaton 4877:Massively parallel 4855:distributed shared 4675:Cache invalidation 4639:Instruction window 4430:Manycore processor 4410:Massively parallel 4405:Parallel computing 4386:Parallel computing 4267:LMUL > 1 in RVV 3741:. pp. 626–627 3622: 3582: 3557:{\displaystyle r/} 3554: 3395:Sub-vector Swizzle 3116:# add all x into y 2764:; y := y + r1 2267:; n := n - t0 1982:order of magnitude 1881:; x := x + 16 1251:vadd c, a, b, $ 10 1183:embedded processor 1157:# 10 stores into c 811:; count := 10 701: 500:, and vectors are 496:, because SIMD is 459:Recent development 444:Flynn's 1972 paper 394:massively parallel 375:minisupercomputers 339: 175:massively parallel 87:video-game console 7064: 7063: 6953: 6952: 6572:Instruction cache 6562:Scratchpad memory 6409: 6408: 6396:Network processor 6325:Network on a chip 6280:Ultra-low-voltage 6231:Multi-chip module 6074: 6073: 5860: 5859: 5847:Branch prediction 5824:Register renaming 5718: 5717: 5700:VISC architecture 5522:Quantum computing 5517:VISC architecture 5399:Secondary storage 5315:Microarchitecture 5275:Register machines 5121: 5120: 5074:Parallel slowdown 4708:Stream processing 4598:Karp–Flatt metric 4239:978-3-540-76016-0 4148:SX-Arora Overview 4067:ARM SVE2 tutorial 3965:Hennessy, John L. 3669:Stream processing 3653:Duncan's taxonomy 3340:– often includes 3296:Splat and Extract 3258:Masked Operations 2806:; n := n - 1 2785:; x := x + 4 2072:; m = 1<<t0 1920:; n := n - 4 1777:described above. 1643:; n := n - 1 1604:; x := x + 4 1299:SX-Aurora TSUBASA 1121:# 10 loads from b 1106:# 10 loads from a 596:Duncan's taxonomy 509:Pure (fixed) SIMD 409:SX-Aurora TSUBASA 286:Texas Instruments 275: 274: 267: 71:scalar processors 7099: 7026:Processor design 6918:Power management 6800:Instruction unit 6661:Branch predictor 6610: 6609: 6308:System on a chip 6250: 6249: 6090:Transistor count 6014:Flynn's taxonomy 5871: 5870: 5729: 5728: 5532:Addressing modes 5443: 5442: 5389:Memory hierarchy 5253:Hypercomputation 5171:Abstract machine 5148: 5141: 5134: 5125: 5124: 5110: 5109: 5084:Software lockout 4883:Computer cluster 4818:Vector processor 4773:Array processing 4758:Flynn's taxonomy 4665:Memory coherence 4440:Computer network 4379: 4372: 4365: 4356: 4355: 4346: 4341: 4335: 4330: 4324: 4319: 4313: 4308: 4302: 4297: 4291: 4289:Videocore IV QPU 4286: 4280: 4275: 4269: 4264: 4258: 4257: 4250: 4244: 4243: 4213: 4207: 4206: 4204: 4191: 4185: 4184: 4167: 4161: 4156: 4150: 4145: 4139: 4134: 4128: 4123: 4117: 4116: 4103: 4097: 4096: 4089: 4083: 4082: 4075: 4069: 4064: 4058: 4053: 4047: 4046: 4039: 4033: 4028: 4022: 4017: 4011: 4010: 3997: 3991: 3990: 3974: 3957: 3951: 3946: 3940: 3935: 3929: 3924: 3918: 3917: 3916:. 27 April 2020. 3910: 3904: 3903: 3896: 3890: 3889: 3887: 3879: 3873: 3872: 3859: 3853: 3852: 3850: 3826: 3820: 3819: 3817: 3815: 3804: 3798: 3793: 3787: 3782: 3776: 3775: 3757: 3751: 3750: 3748: 3746: 3730: 3699:Barrel processor 3693:vector extension 3631: 3629: 3628: 3623: 3606: 3591: 3589: 3588: 3583: 3563: 3561: 3560: 3555: 3520: 3353:Bit manipulation 3324: 3320: 3174: 3171: 3168: 3165: 3162: 3159: 3156: 3153: 3150: 3147: 3144: 3141: 3138: 3135: 3132: 3129: 3126: 3123: 3120: 3117: 3114: 3111: 3108: 3105: 3102: 3099: 3096: 3093: 3090: 3087: 3084: 3081: 3078: 3075: 3072: 3069: 3066: 3063: 3060: 3057: 3054: 3044: 3041: 3038: 3035: 3032: 3029: 3026: 3023: 3020: 3017: 3014: 3011: 3008: 3005: 3002: 2970: 2958: 2952: 2946: 2940: 2930: 2927: 2924: 2921: 2918: 2915: 2912: 2909: 2906: 2903: 2900: 2897: 2894: 2891: 2888: 2885: 2882: 2879: 2878:; for 2nd 4 of x 2876: 2873: 2870: 2867: 2864: 2861: 2834: 2831: 2828: 2825: 2822: 2819: 2816: 2813: 2810: 2807: 2804: 2801: 2798: 2795: 2792: 2789: 2786: 2783: 2780: 2777: 2774: 2771: 2768: 2765: 2762: 2759: 2756: 2753: 2750: 2747: 2744: 2741: 2738: 2735: 2732: 2729: 2726: 2723: 2720: 2717: 2714: 2704:Scalar assembler 2696: 2693: 2690: 2687: 2684: 2681: 2678: 2675: 2672: 2669: 2666: 2663: 2660: 2657: 2654: 2651: 2648: 2645: 2642: 2639: 2636: 2633: 2630: 2627: 2624: 2621: 2618: 2615: 2612: 2609: 2606: 2603: 2600: 2597: 2594: 2591: 2588: 2585: 2582: 2579: 2518: 2514: 2495: 2492: 2489: 2486: 2483: 2480: 2477: 2474: 2471: 2468: 2465: 2462: 2459: 2456: 2453: 2450: 2447: 2444: 2441: 2438: 2435: 2432: 2429: 2426: 2423: 2420: 2417: 2414: 2411: 2408: 2405: 2402: 2399: 2396: 2393: 2390: 2387: 2384: 2381: 2378: 2375: 2372: 2369: 2366: 2363: 2360: 2357: 2354: 2351: 2348: 2345: 2342: 2316:or a virtual one 2292: 2289: 2286: 2283: 2280: 2277: 2274: 2271: 2268: 2265: 2262: 2259: 2256: 2253: 2250: 2247: 2244: 2241: 2238: 2235: 2232: 2229: 2226: 2223: 2220: 2217: 2214: 2211: 2208: 2205: 2202: 2199: 2196: 2193: 2190: 2187: 2184: 2181: 2178: 2175: 2172: 2169: 2166: 2163: 2160: 2157: 2154: 2151: 2148: 2145: 2142: 2139: 2136: 2133: 2130: 2127: 2124: 2121: 2118: 2115: 2112: 2109: 2106: 2103: 2100: 2097: 2094: 2091: 2088: 2085: 2082: 2079: 2076: 2073: 2070: 2067: 2064: 2061: 2058: 2055: 2052: 2051:; t0 = min(n, 4) 2049: 2046: 2043: 2040: 2037: 2034: 2031: 2028: 2013: 1991: 1942: 1939: 1936: 1933: 1930: 1927: 1924: 1921: 1918: 1915: 1912: 1909: 1906: 1903: 1900: 1897: 1894: 1891: 1888: 1885: 1882: 1879: 1876: 1873: 1870: 1867: 1864: 1861: 1858: 1855: 1852: 1849: 1846: 1843: 1840: 1837: 1834: 1831: 1828: 1825: 1822: 1819: 1816: 1813: 1810: 1807: 1804: 1801: 1798: 1795: 1792: 1789: 1786: 1783: 1776: 1769: 1766: 1763: 1760: 1757: 1748:properly aligned 1744:Flynn's taxonomy 1733: 1730: 1727: 1724: 1721: 1718: 1715: 1712: 1709: 1706: 1703: 1700: 1697: 1694: 1691: 1688: 1685: 1682: 1679: 1676: 1665: 1662: 1659: 1656: 1653: 1650: 1647: 1644: 1641: 1638: 1635: 1632: 1629: 1626: 1623: 1620: 1617: 1614: 1611: 1608: 1605: 1602: 1599: 1596: 1593: 1590: 1587: 1584: 1581: 1578: 1575: 1572: 1569: 1566: 1563: 1560: 1557: 1554: 1551: 1548: 1545: 1542: 1539: 1536: 1533: 1530: 1527: 1524: 1521: 1518: 1515: 1512: 1509: 1506: 1503: 1493:Scalar assembler 1485: 1482: 1479: 1476: 1473: 1470: 1467: 1464: 1461: 1458: 1455: 1452: 1449: 1446: 1443: 1440: 1437: 1434: 1431: 1428: 1425: 1422: 1419: 1416: 1413: 1410: 1407: 1404: 1401: 1398: 1395: 1392: 1389: 1386: 1383: 1380: 1377: 1374: 1371: 1368: 1272: 1260: 1252: 1194:functional units 1161: 1158: 1155: 1152: 1149: 1146: 1143: 1140: 1137: 1134: 1131: 1128: 1125: 1122: 1119: 1116: 1113: 1110: 1107: 1104: 1101: 1098: 1095: 1092: 1089: 1086: 1083: 1080: 1059: 1056: 1053: 1050: 1047: 1044: 1041: 1038: 1035: 1032: 1029: 1026: 1023: 1020: 1017: 1014: 1011: 1008: 1005: 1002: 999: 996: 993: 990: 987: 984: 981: 978: 975: 972: 969: 966: 956: 953: 950: 947: 944: 941: 938: 935: 932: 929: 926: 923: 920: 917: 914: 911: 908: 905: 902: 899: 896: 893: 890: 887: 884: 881: 878: 875: 872: 869: 866: 863: 860: 857: 854: 851: 848: 845: 842: 839: 836: 833: 830: 827: 824: 821: 818: 815: 812: 809: 806: 803: 800: 797: 794: 791: 691:unable by design 680:Flynn's taxonomy 662: 658: 577:- also known as 494:vector processor 469:Andes Technology 436:shader pipelines 311:vector registers 270: 263: 259: 256: 250: 219: 211: 108:of conventional 89:hardware and in 43:vector processor 21:array processing 7107: 7106: 7102: 7101: 7100: 7098: 7097: 7096: 7067: 7066: 7065: 7060: 7046:Tick–tock model 7004: 6960: 6949: 6889: 6873:Address decoder 6827: 6781: 6777:Program counter 6752:Status register 6733: 6688: 6648:Load–store unit 6615: 6608: 6535: 6504: 6405: 6362:Image processor 6337: 6330: 6300: 6294: 6270:Microcontroller 6260:Embedded system 6248: 6148: 6081: 6070: 6008: 5958: 5856: 5833: 5817:Re-order buffer 5788: 5769:Data dependency 5755: 5714: 5544: 5538: 5437: 5436:Instruction set 5430: 5416:Multiprocessing 5384:Cache hierarchy 5377:Register/memory 5301: 5201:Queue automaton 5157: 5152: 5122: 5117: 5098: 5042: 4948:Coarray Fortran 4904: 4888:Beowulf cluster 4744: 4694: 4685:Synchronization 4670:Cache coherence 4660:Multiprocessing 4648: 4612: 4593:Cost efficiency 4588:Gustafson's law 4556: 4500: 4449: 4425:Multiprocessing 4415:Cloud computing 4388: 4383: 4352: 4350: 4349: 4342: 4338: 4331: 4327: 4320: 4316: 4309: 4305: 4298: 4294: 4287: 4283: 4276: 4272: 4265: 4261: 4252: 4251: 4247: 4240: 4214: 4210: 4192: 4188: 4169: 4168: 4164: 4157: 4153: 4146: 4142: 4135: 4131: 4124: 4120: 4105: 4104: 4100: 4091: 4090: 4086: 4077: 4076: 4072: 4065: 4061: 4054: 4050: 4041: 4040: 4036: 4029: 4025: 4018: 4014: 3999: 3998: 3994: 3987: 3958: 3954: 3947: 3943: 3936: 3932: 3925: 3921: 3912: 3911: 3907: 3898: 3897: 3893: 3888:. 16 June 2023. 3885: 3881: 3880: 3876: 3871:. 16 June 2023. 3861: 3860: 3856: 3827: 3823: 3813: 3811: 3806: 3805: 3801: 3794: 3790: 3783: 3779: 3772: 3758: 3754: 3744: 3742: 3731: 3727: 3722: 3648:SX architecture 3644: 3602: 3597: 3594: 3593: 3571: 3568: 3567: 3516: 3511: 3508: 3507: 3489: 3447: 3413:Transcendentals 3367: 3322: 3318: 3282:vector chaining 3262:predicate masks 3229: 3193: 3176: 3175: 3172: 3169: 3166: 3163: 3160: 3157: 3154: 3151: 3148: 3145: 3142: 3139: 3136: 3133: 3130: 3127: 3124: 3121: 3118: 3115: 3112: 3109: 3106: 3103: 3100: 3097: 3095:# load vector x 3094: 3091: 3088: 3085: 3082: 3079: 3076: 3073: 3070: 3067: 3064: 3061: 3058: 3055: 3052: 3046: 3045: 3042: 3039: 3036: 3033: 3030: 3028:# load vector x 3027: 3024: 3021: 3018: 3015: 3012: 3009: 3006: 3003: 3000: 2990: 2932: 2931: 2928: 2925: 2922: 2919: 2916: 2913: 2910: 2907: 2904: 2901: 2898: 2895: 2892: 2889: 2886: 2883: 2880: 2877: 2874: 2871: 2868: 2865: 2862: 2859: 2844: 2836: 2835: 2832: 2829: 2826: 2823: 2820: 2817: 2814: 2811: 2808: 2805: 2802: 2799: 2796: 2793: 2790: 2787: 2784: 2781: 2778: 2775: 2772: 2769: 2766: 2763: 2760: 2757: 2754: 2751: 2748: 2745: 2742: 2739: 2736: 2733: 2730: 2727: 2724: 2721: 2718: 2715: 2712: 2706: 2698: 2697: 2694: 2691: 2688: 2685: 2682: 2679: 2676: 2673: 2670: 2667: 2664: 2661: 2658: 2655: 2652: 2649: 2646: 2643: 2640: 2637: 2634: 2631: 2628: 2625: 2622: 2619: 2616: 2613: 2610: 2607: 2604: 2601: 2598: 2595: 2592: 2589: 2586: 2583: 2580: 2577: 2566: 2497: 2496: 2493: 2490: 2487: 2484: 2481: 2478: 2475: 2472: 2469: 2466: 2463: 2460: 2457: 2454: 2451: 2448: 2445: 2442: 2439: 2436: 2433: 2430: 2427: 2424: 2421: 2418: 2415: 2412: 2409: 2406: 2403: 2400: 2397: 2394: 2391: 2388: 2386:# load vector y 2385: 2382: 2379: 2376: 2373: 2371:# load vector x 2370: 2367: 2364: 2361: 2358: 2355: 2352: 2349: 2346: 2343: 2340: 2311: 2294: 2293: 2290: 2287: 2284: 2281: 2278: 2275: 2272: 2269: 2266: 2263: 2260: 2257: 2254: 2251: 2248: 2245: 2242: 2239: 2236: 2233: 2230: 2227: 2224: 2221: 2218: 2215: 2212: 2209: 2206: 2203: 2200: 2197: 2194: 2191: 2188: 2185: 2182: 2179: 2176: 2173: 2170: 2167: 2164: 2161: 2158: 2155: 2152: 2149: 2146: 2143: 2140: 2137: 2134: 2131: 2128: 2125: 2122: 2119: 2116: 2113: 2110: 2107: 2104: 2101: 2098: 2095: 2092: 2089: 2086: 2083: 2080: 2077: 2074: 2071: 2068: 2065: 2062: 2059: 2056: 2053: 2050: 2047: 2044: 2041: 2038: 2035: 2032: 2029: 2026: 2020: 2018:Predicated SIMD 2003:exists in x86. 1976:This more than 1944: 1943: 1940: 1937: 1934: 1931: 1928: 1925: 1922: 1919: 1916: 1913: 1910: 1907: 1904: 1901: 1898: 1895: 1892: 1889: 1886: 1883: 1880: 1877: 1874: 1871: 1868: 1865: 1862: 1859: 1856: 1853: 1850: 1847: 1844: 1841: 1838: 1835: 1832: 1829: 1826: 1823: 1820: 1817: 1814: 1811: 1808: 1805: 1802: 1799: 1796: 1793: 1790: 1787: 1784: 1781: 1771: 1770: 1767: 1764: 1761: 1758: 1755: 1740: 1735: 1734: 1731: 1728: 1725: 1722: 1719: 1716: 1713: 1710: 1707: 1704: 1701: 1698: 1695: 1692: 1689: 1686: 1683: 1680: 1677: 1674: 1667: 1666: 1663: 1660: 1657: 1654: 1651: 1648: 1645: 1642: 1639: 1636: 1633: 1630: 1627: 1624: 1621: 1618: 1615: 1612: 1609: 1606: 1603: 1600: 1597: 1594: 1591: 1588: 1585: 1582: 1579: 1576: 1573: 1570: 1567: 1564: 1561: 1558: 1555: 1552: 1549: 1546: 1543: 1540: 1537: 1534: 1531: 1528: 1525: 1522: 1519: 1516: 1513: 1510: 1507: 1504: 1501: 1495: 1487: 1486: 1483: 1480: 1477: 1474: 1471: 1468: 1465: 1462: 1459: 1456: 1453: 1450: 1447: 1444: 1441: 1438: 1435: 1432: 1429: 1426: 1423: 1420: 1417: 1414: 1411: 1408: 1405: 1402: 1399: 1396: 1393: 1390: 1387: 1384: 1381: 1378: 1375: 1372: 1369: 1366: 1355: 1243: 1233: 1163: 1162: 1159: 1156: 1153: 1150: 1147: 1144: 1141: 1138: 1135: 1132: 1129: 1126: 1123: 1120: 1117: 1114: 1111: 1108: 1105: 1102: 1099: 1096: 1093: 1090: 1087: 1084: 1081: 1078: 1069:per-instruction 1061: 1060: 1057: 1054: 1051: 1048: 1045: 1042: 1039: 1036: 1033: 1030: 1027: 1024: 1021: 1018: 1015: 1012: 1009: 1006: 1003: 1000: 997: 994: 991: 988: 985: 982: 979: 976: 973: 970: 967: 964: 958: 957: 954: 951: 948: 945: 942: 939: 936: 933: 930: 927: 924: 921: 918: 915: 912: 909: 906: 903: 900: 897: 894: 891: 888: 885: 882: 879: 876: 873: 870: 867: 864: 861: 858: 855: 852: 849: 846: 843: 840: 837: 834: 831: 828: 825: 822: 819: 816: 813: 810: 807: 804: 801: 798: 795: 792: 789: 765:address decoder 740: 732:vector chaining 705:vector chaining 644: 604:Convex C-Series 575:Predicated SIMD 502:variable-length 489: 461: 440:compute kernels 428: 422: 324:vector chaining 271: 260: 254: 251: 236: 220: 209: 197: 123: 118: 65:of data called 55:instruction set 47:array processor 35: 24: 17: 12: 11: 5: 7105: 7095: 7094: 7089: 7084: 7079: 7062: 7061: 7059: 7058: 7053: 7051:Pin grid array 7048: 7043: 7038: 7033: 7028: 7023: 7018: 7012: 7010: 7006: 7005: 7003: 7002: 6996: 6991: 6986: 6981: 6976: 6971: 6965: 6963: 6955: 6954: 6951: 6950: 6948: 6947: 6942: 6937: 6932: 6927: 6922: 6921: 6920: 6915: 6910: 6899: 6897: 6891: 6890: 6888: 6887: 6885:Barrel shifter 6882: 6881: 6880: 6875: 6868:Binary decoder 6865: 6864: 6863: 6853: 6848: 6843: 6837: 6835: 6829: 6828: 6826: 6825: 6820: 6812: 6807: 6802: 6797: 6791: 6789: 6783: 6782: 6780: 6779: 6774: 6769: 6764: 6759: 6757:Stack register 6754: 6749: 6743: 6741: 6735: 6734: 6732: 6731: 6730: 6729: 6724: 6714: 6709: 6704: 6698: 6696: 6690: 6689: 6687: 6686: 6681: 6680: 6679: 6668: 6663: 6658: 6657: 6656: 6650: 6639: 6633: 6627: 6620: 6618: 6607: 6606: 6601: 6596: 6591: 6586: 6585: 6584: 6579: 6574: 6569: 6564: 6559: 6549: 6543: 6541: 6537: 6536: 6534: 6533: 6528: 6523: 6518: 6512: 6510: 6506: 6505: 6503: 6502: 6501: 6500: 6490: 6485: 6480: 6475: 6470: 6465: 6460: 6455: 6450: 6445: 6440: 6435: 6430: 6425: 6419: 6417: 6411: 6410: 6407: 6406: 6404: 6403: 6398: 6393: 6388: 6382: 6376: 6370: 6364: 6359: 6353: 6351:AI accelerator 6348: 6342: 6340: 6332: 6331: 6329: 6328: 6322: 6317: 6314:Multiprocessor 6311: 6304: 6302: 6296: 6295: 6293: 6292: 6287: 6282: 6277: 6272: 6267: 6265:Microprocessor 6262: 6256: 6254: 6253:By application 6247: 6246: 6240: 6234: 6228: 6223: 6218: 6213: 6208: 6203: 6198: 6196:Tile processor 6193: 6188: 6183: 6178: 6177: 6176: 6165: 6158: 6156: 6150: 6149: 6147: 6146: 6141: 6136: 6130: 6124: 6118: 6112: 6106: 6105: 6104: 6092: 6086: 6084: 6076: 6075: 6072: 6071: 6069: 6068: 6067: 6066: 6056: 6051: 6050: 6049: 6044: 6039: 6034: 6024: 6018: 6016: 6010: 6009: 6007: 6006: 6001: 5996: 5991: 5990: 5989: 5984: 5982:Hyperthreading 5974: 5968: 5966: 5964:Multithreading 5960: 5959: 5957: 5956: 5951: 5946: 5945: 5944: 5934: 5933: 5932: 5927: 5917: 5916: 5915: 5910: 5900: 5895: 5894: 5893: 5888: 5877: 5875: 5868: 5862: 5861: 5858: 5857: 5855: 5854: 5849: 5843: 5841: 5835: 5834: 5832: 5831: 5826: 5821: 5820: 5819: 5814: 5804: 5798: 5796: 5790: 5789: 5787: 5786: 5781: 5776: 5771: 5765: 5763: 5757: 5756: 5754: 5753: 5748: 5743: 5741:Pipeline stall 5737: 5735: 5726: 5720: 5719: 5716: 5715: 5713: 5712: 5707: 5702: 5697: 5694: 5693: 5692: 5690:z/Architecture 5687: 5682: 5677: 5669: 5664: 5659: 5654: 5649: 5644: 5639: 5634: 5629: 5624: 5619: 5614: 5609: 5608: 5607: 5602: 5597: 5589: 5584: 5579: 5574: 5569: 5564: 5559: 5554: 5548: 5546: 5540: 5539: 5537: 5536: 5535: 5534: 5524: 5519: 5514: 5509: 5504: 5499: 5494: 5493: 5492: 5482: 5481: 5480: 5470: 5465: 5460: 5455: 5449: 5447: 5440: 5432: 5431: 5429: 5428: 5423: 5418: 5413: 5408: 5403: 5402: 5401: 5396: 5394:Virtual memory 5386: 5381: 5380: 5379: 5374: 5369: 5364: 5354: 5349: 5344: 5339: 5334: 5333: 5332: 5322: 5317: 5311: 5309: 5303: 5302: 5300: 5299: 5298: 5297: 5292: 5287: 5282: 5272: 5267: 5262: 5261: 5260: 5255: 5250: 5245: 5240: 5235: 5230: 5225: 5218:Turing machine 5215: 5214: 5213: 5208: 5203: 5198: 5193: 5188: 5178: 5173: 5167: 5165: 5159: 5158: 5151: 5150: 5143: 5136: 5128: 5119: 5118: 5116: 5115: 5103: 5100: 5099: 5097: 5096: 5091: 5086: 5081: 5079:Race condition 5076: 5071: 5066: 5061: 5056: 5050: 5048: 5044: 5043: 5041: 5040: 5035: 5030: 5025: 5020: 5015: 5010: 5005: 5000: 4995: 4990: 4985: 4980: 4975: 4970: 4965: 4960: 4955: 4950: 4945: 4940: 4935: 4930: 4925: 4920: 4914: 4912: 4906: 4905: 4903: 4902: 4897: 4892: 4891: 4890: 4880: 4874: 4873: 4872: 4867: 4862: 4857: 4852: 4847: 4837: 4836: 4835: 4830: 4823:Multiprocessor 4820: 4815: 4810: 4805: 4800: 4799: 4798: 4793: 4788: 4787: 4786: 4781: 4776: 4765: 4754: 4752: 4746: 4745: 4743: 4742: 4737: 4736: 4735: 4730: 4725: 4715: 4710: 4704: 4702: 4696: 4695: 4693: 4692: 4687: 4682: 4677: 4672: 4667: 4662: 4656: 4654: 4650: 4649: 4647: 4646: 4641: 4636: 4631: 4626: 4620: 4618: 4614: 4613: 4611: 4610: 4605: 4600: 4595: 4590: 4585: 4580: 4575: 4570: 4564: 4562: 4558: 4557: 4555: 4554: 4552:Hardware scout 4549: 4543: 4538: 4533: 4527: 4522: 4516: 4510: 4508: 4506:Multithreading 4502: 4501: 4499: 4498: 4493: 4488: 4483: 4478: 4473: 4468: 4463: 4457: 4455: 4451: 4450: 4448: 4447: 4445:Systolic array 4442: 4437: 4432: 4427: 4422: 4417: 4412: 4407: 4402: 4396: 4394: 4390: 4389: 4382: 4381: 4374: 4367: 4359: 4348: 4347: 4336: 4325: 4314: 4303: 4292: 4281: 4270: 4259: 4245: 4238: 4208: 4186: 4162: 4151: 4140: 4137:RISC-V RVV ISA 4129: 4118: 4098: 4084: 4070: 4059: 4048: 4034: 4023: 4012: 3992: 3985: 3952: 3941: 3930: 3919: 3905: 3891: 3874: 3854: 3821: 3799: 3788: 3777: 3770: 3752: 3724: 3723: 3721: 3718: 3717: 3716: 3711: 3706: 3701: 3696: 3686: 3681: 3676: 3671: 3666: 3664:Compute kernel 3661: 3656: 3650: 3643: 3640: 3621: 3618: 3615: 3612: 3609: 3605: 3601: 3581: 3578: 3575: 3553: 3550: 3547: 3544: 3541: 3538: 3535: 3532: 3529: 3526: 3523: 3519: 3515: 3488: 3485: 3446: 3443: 3442: 3441: 3410: 3392: 3366: 3363: 3362: 3361: 3349: 3335: 3325: 3323:x = y + y… + y 3308:Reduction and 3305: 3299: 3293: 3286:Gather-scatter 3275: 3265: 3255: 3252:data structure 3228: 3225: 3221: 3220: 3217: 3210: 3209: 3202: 3192: 3189: 3152:# n -= VL (t0) 3051: 2999: 2989: 2986: 2961: 2960: 2954: 2948: 2942: 2929:; add 2 groups 2893:; first 4 of x 2858: 2843: 2842:SIMD reduction 2840: 2711: 2705: 2702: 2576: 2565: 2562: 2539: 2538: 2534: 2527: 2520: 2479:# n -= VL (t0) 2407:# v1 += v0 * a 2339: 2310: 2307: 2025: 2019: 2016: 1970: 1969: 1966: 1963: 1780: 1768:; v4 = a,a,a,a 1754: 1739: 1736: 1673: 1500: 1494: 1491: 1365: 1354: 1351: 1232: 1229: 1221:supercomputers 1187: 1186: 1179: 1176: 1173: 1170: 1077: 963: 788: 745:memory latency 739: 736: 692: 676: 675: 673: 668: 643: 640: 620: 619: 589: 572: 569:Cell processor 503: 499: 488: 485: 460: 457: 424:Main article: 421: 418: 273: 272: 223: 221: 214: 208: 207:Supercomputers 205: 196: 193: 122: 119: 117: 114: 110:microprocessor 15: 9: 6: 4: 3: 2: 7104: 7093: 7090: 7088: 7085: 7083: 7080: 7078: 7075: 7074: 7072: 7057: 7054: 7052: 7049: 7047: 7044: 7042: 7039: 7037: 7034: 7032: 7029: 7027: 7024: 7022: 7019: 7017: 7014: 7013: 7011: 7007: 7000: 6997: 6995: 6992: 6990: 6987: 6985: 6982: 6980: 6977: 6975: 6972: 6970: 6967: 6966: 6964: 6962: 6956: 6946: 6943: 6941: 6938: 6936: 6933: 6931: 6928: 6926: 6923: 6919: 6916: 6914: 6911: 6909: 6906: 6905: 6904: 6901: 6900: 6898: 6896: 6892: 6886: 6883: 6879: 6876: 6874: 6871: 6870: 6869: 6866: 6862: 6859: 6858: 6857: 6854: 6852: 6849: 6847: 6846:Demultiplexer 6844: 6842: 6839: 6838: 6836: 6834: 6830: 6824: 6821: 6819: 6816: 6813: 6811: 6808: 6806: 6803: 6801: 6798: 6796: 6793: 6792: 6790: 6788: 6784: 6778: 6775: 6773: 6770: 6768: 6767:Memory buffer 6765: 6763: 6762:Register file 6760: 6758: 6755: 6753: 6750: 6748: 6745: 6744: 6742: 6740: 6736: 6728: 6725: 6723: 6720: 6719: 6718: 6715: 6713: 6710: 6708: 6705: 6703: 6702:Combinational 6700: 6699: 6697: 6695: 6691: 6685: 6682: 6678: 6675: 6674: 6672: 6669: 6667: 6664: 6662: 6659: 6654: 6651: 6649: 6646: 6645: 6643: 6640: 6637: 6634: 6631: 6628: 6625: 6622: 6621: 6619: 6617: 6611: 6605: 6602: 6600: 6597: 6595: 6592: 6590: 6587: 6583: 6580: 6578: 6575: 6573: 6570: 6568: 6565: 6563: 6560: 6558: 6555: 6554: 6553: 6550: 6548: 6545: 6544: 6542: 6538: 6532: 6529: 6527: 6524: 6522: 6519: 6517: 6514: 6513: 6511: 6507: 6499: 6496: 6495: 6494: 6491: 6489: 6486: 6484: 6481: 6479: 6476: 6474: 6471: 6469: 6466: 6464: 6461: 6459: 6456: 6454: 6451: 6449: 6446: 6444: 6441: 6439: 6436: 6434: 6431: 6429: 6426: 6424: 6421: 6420: 6418: 6416: 6412: 6402: 6399: 6397: 6394: 6392: 6389: 6386: 6383: 6380: 6377: 6374: 6371: 6368: 6365: 6363: 6360: 6357: 6354: 6352: 6349: 6347: 6344: 6343: 6341: 6339: 6333: 6326: 6323: 6321: 6318: 6315: 6312: 6309: 6306: 6305: 6303: 6297: 6291: 6288: 6286: 6283: 6281: 6278: 6276: 6273: 6271: 6268: 6266: 6263: 6261: 6258: 6257: 6255: 6251: 6244: 6241: 6238: 6235: 6232: 6229: 6227: 6224: 6222: 6219: 6217: 6214: 6212: 6209: 6207: 6204: 6202: 6199: 6197: 6194: 6192: 6189: 6187: 6184: 6182: 6179: 6175: 6172: 6171: 6169: 6166: 6163: 6160: 6159: 6157: 6155: 6151: 6145: 6142: 6140: 6137: 6134: 6131: 6128: 6125: 6122: 6119: 6116: 6113: 6110: 6107: 6102: 6099: 6098: 6096: 6093: 6091: 6088: 6087: 6085: 6083: 6077: 6065: 6062: 6061: 6060: 6057: 6055: 6052: 6048: 6045: 6043: 6040: 6038: 6035: 6033: 6030: 6029: 6028: 6025: 6023: 6020: 6019: 6017: 6015: 6011: 6005: 6002: 6000: 5997: 5995: 5992: 5988: 5985: 5983: 5980: 5979: 5978: 5975: 5973: 5970: 5969: 5967: 5965: 5961: 5955: 5952: 5950: 5947: 5943: 5940: 5939: 5938: 5935: 5931: 5928: 5926: 5923: 5922: 5921: 5918: 5914: 5911: 5909: 5906: 5905: 5904: 5901: 5899: 5896: 5892: 5889: 5887: 5884: 5883: 5882: 5879: 5878: 5876: 5872: 5869: 5867: 5863: 5853: 5850: 5848: 5845: 5844: 5842: 5840: 5836: 5830: 5827: 5825: 5822: 5818: 5815: 5813: 5810: 5809: 5808: 5805: 5803: 5802:Scoreboarding 5800: 5799: 5797: 5795: 5791: 5785: 5784:False sharing 5782: 5780: 5777: 5775: 5772: 5770: 5767: 5766: 5764: 5762: 5758: 5752: 5749: 5747: 5744: 5742: 5739: 5738: 5736: 5734: 5730: 5727: 5725: 5721: 5711: 5708: 5706: 5703: 5701: 5698: 5695: 5691: 5688: 5686: 5683: 5681: 5678: 5676: 5673: 5672: 5670: 5668: 5665: 5663: 5660: 5658: 5655: 5653: 5650: 5648: 5645: 5643: 5640: 5638: 5635: 5633: 5630: 5628: 5625: 5623: 5620: 5618: 5615: 5613: 5610: 5606: 5603: 5601: 5598: 5596: 5593: 5592: 5590: 5588: 5585: 5583: 5580: 5578: 5577:Stanford MIPS 5575: 5573: 5570: 5568: 5565: 5563: 5560: 5558: 5555: 5553: 5550: 5549: 5547: 5541: 5533: 5530: 5529: 5528: 5525: 5523: 5520: 5518: 5515: 5513: 5510: 5508: 5505: 5503: 5500: 5498: 5495: 5491: 5488: 5487: 5486: 5483: 5479: 5476: 5475: 5474: 5471: 5469: 5466: 5464: 5461: 5459: 5456: 5454: 5451: 5450: 5448: 5444: 5441: 5439: 5438:architectures 5433: 5427: 5424: 5422: 5419: 5417: 5414: 5412: 5409: 5407: 5406:Heterogeneous 5404: 5400: 5397: 5395: 5392: 5391: 5390: 5387: 5385: 5382: 5378: 5375: 5373: 5370: 5368: 5365: 5363: 5360: 5359: 5358: 5357:Memory access 5355: 5353: 5350: 5348: 5345: 5343: 5340: 5338: 5335: 5331: 5328: 5327: 5326: 5323: 5321: 5318: 5316: 5313: 5312: 5310: 5308: 5304: 5296: 5293: 5291: 5290:Random-access 5288: 5286: 5283: 5281: 5278: 5277: 5276: 5273: 5271: 5270:Stack machine 5268: 5266: 5263: 5259: 5256: 5254: 5251: 5249: 5246: 5244: 5241: 5239: 5236: 5234: 5231: 5229: 5226: 5224: 5221: 5220: 5219: 5216: 5212: 5209: 5207: 5204: 5202: 5199: 5197: 5194: 5192: 5189: 5187: 5186:with datapath 5184: 5183: 5182: 5179: 5177: 5174: 5172: 5169: 5168: 5166: 5164: 5160: 5156: 5149: 5144: 5142: 5137: 5135: 5130: 5129: 5126: 5114: 5105: 5104: 5101: 5095: 5092: 5090: 5087: 5085: 5082: 5080: 5077: 5075: 5072: 5070: 5067: 5065: 5062: 5060: 5057: 5055: 5052: 5051: 5049: 5045: 5039: 5036: 5034: 5031: 5029: 5026: 5024: 5021: 5019: 5016: 5014: 5011: 5009: 5006: 5004: 5001: 4999: 4996: 4994: 4991: 4989: 4986: 4984: 4981: 4979: 4976: 4974: 4971: 4969: 4968:Global Arrays 4966: 4964: 4961: 4959: 4956: 4954: 4951: 4949: 4946: 4944: 4941: 4939: 4936: 4934: 4931: 4929: 4926: 4924: 4921: 4919: 4916: 4915: 4913: 4911: 4907: 4901: 4898: 4896: 4895:Grid computer 4893: 4889: 4886: 4885: 4884: 4881: 4878: 4875: 4871: 4868: 4866: 4863: 4861: 4858: 4856: 4853: 4851: 4848: 4846: 4843: 4842: 4841: 4838: 4834: 4831: 4829: 4826: 4825: 4824: 4821: 4819: 4816: 4814: 4811: 4809: 4806: 4804: 4801: 4797: 4794: 4792: 4789: 4785: 4782: 4780: 4777: 4774: 4771: 4770: 4769: 4766: 4764: 4761: 4760: 4759: 4756: 4755: 4753: 4751: 4747: 4741: 4738: 4734: 4731: 4729: 4726: 4724: 4721: 4720: 4719: 4716: 4714: 4711: 4709: 4706: 4705: 4703: 4701: 4697: 4691: 4688: 4686: 4683: 4681: 4678: 4676: 4673: 4671: 4668: 4666: 4663: 4661: 4658: 4657: 4655: 4651: 4645: 4642: 4640: 4637: 4635: 4632: 4630: 4627: 4625: 4622: 4621: 4619: 4615: 4609: 4606: 4604: 4601: 4599: 4596: 4594: 4591: 4589: 4586: 4584: 4581: 4579: 4576: 4574: 4571: 4569: 4566: 4565: 4563: 4559: 4553: 4550: 4547: 4544: 4542: 4539: 4537: 4534: 4531: 4528: 4526: 4523: 4520: 4517: 4515: 4512: 4511: 4509: 4507: 4503: 4497: 4494: 4492: 4489: 4487: 4484: 4482: 4479: 4477: 4474: 4472: 4469: 4467: 4464: 4462: 4459: 4458: 4456: 4452: 4446: 4443: 4441: 4438: 4436: 4433: 4431: 4428: 4426: 4423: 4421: 4418: 4416: 4413: 4411: 4408: 4406: 4403: 4401: 4398: 4397: 4395: 4391: 4387: 4380: 4375: 4373: 4368: 4366: 4361: 4360: 4357: 4353: 4345: 4340: 4334: 4329: 4323: 4318: 4312: 4307: 4301: 4296: 4290: 4285: 4279: 4274: 4268: 4263: 4255: 4249: 4241: 4235: 4231: 4227: 4223: 4219: 4212: 4203: 4198: 4190: 4182: 4178: 4177: 4172: 4166: 4160: 4155: 4149: 4144: 4138: 4133: 4127: 4126:Cray Overview 4122: 4114: 4113: 4108: 4102: 4094: 4088: 4080: 4074: 4068: 4063: 4057: 4052: 4044: 4038: 4032: 4027: 4021: 4016: 4008: 4007: 4002: 3996: 3988: 3982: 3978: 3973: 3972: 3966: 3962: 3956: 3950: 3945: 3939: 3934: 3928: 3923: 3915: 3909: 3901: 3895: 3884: 3878: 3870: 3869: 3864: 3858: 3849: 3844: 3840: 3836: 3832: 3825: 3809: 3803: 3797: 3792: 3786: 3781: 3773: 3767: 3763: 3756: 3740: 3739:New Scientist 3736: 3729: 3725: 3715: 3712: 3710: 3707: 3705: 3702: 3700: 3697: 3694: 3690: 3687: 3685: 3682: 3680: 3677: 3675: 3672: 3670: 3667: 3665: 3662: 3660: 3657: 3654: 3651: 3649: 3646: 3645: 3639: 3637: 3636: 3616: 3613: 3610: 3603: 3599: 3576: 3573: 3564: 3548: 3545: 3542: 3539: 3533: 3530: 3527: 3517: 3513: 3505: 3502: 3501: 3496: 3495: 3484: 3481: 3476: 3470: 3466: 3462: 3460: 3456: 3451: 3439: 3434: 3430: 3426: 3422: 3418: 3417:trigonometric 3414: 3411: 3408: 3404: 3400: 3396: 3393: 3390: 3387: 3383: 3380: 3379: 3378: 3376: 3375:trigonometric 3372: 3369:With many 3D 3359: 3355: 3354: 3350: 3347: 3343: 3339: 3336: 3333: 3329: 3326: 3316: 3312: 3311: 3306: 3303: 3300: 3297: 3294: 3291: 3287: 3283: 3279: 3276: 3273: 3269: 3266: 3263: 3259: 3256: 3253: 3249: 3245: 3241: 3237: 3234: 3233: 3232: 3224: 3218: 3215: 3214: 3213: 3207: 3203: 3199: 3198: 3197: 3188: 3184: 3180: 3049: 2997: 2995: 2985: 2981: 2979: 2973: 2966: 2955: 2949: 2943: 2937: 2936: 2935: 2856: 2854: 2850: 2839: 2709: 2701: 2574: 2572: 2561: 2559: 2554: 2550: 2548: 2542: 2535: 2532: 2528: 2525: 2521: 2510: 2509: 2508: 2505: 2503: 2337: 2334: 2331: 2326: 2325: 2323: 2317: 2306: 2304: 2298: 2023: 2015: 2007: 2004: 2002: 1997: 1995: 1987: 1983: 1979: 1974: 1967: 1964: 1960: 1959: 1958: 1954: 1951: 1949: 1778: 1752: 1749: 1745: 1729:; y = y + tmp 1702:; tmp = a * x 1671: 1498: 1490: 1363: 1361: 1350: 1347: 1342: 1338: 1333: 1331: 1326: 1324: 1320: 1316: 1312: 1308: 1304: 1300: 1294: 1292: 1287: 1283: 1278: 1276: 1269:IV ISA for a 1268: 1263: 1256: 1247: 1242: 1238: 1228: 1226: 1222: 1217: 1215: 1213: 1207: 1203: 1198: 1195: 1190: 1184: 1180: 1177: 1174: 1171: 1168: 1167: 1166: 1075: 1072: 1070: 1066: 961: 786: 782: 780: 774: 772: 771: 766: 762: 761:assembly line 757: 752: 750: 746: 735: 733: 729: 725: 720: 718: 712: 708: 706: 698: 694: 690: 687: 685: 681: 671: 669: 666: 654: 653: 652: 648: 639: 637: 633: 629: 625: 617: 613: 609: 605: 601: 597: 593: 590: 588: 584: 580: 576: 573: 570: 566: 562: 558: 554: 550: 546: 542: 538: 534: 530: 526: 522: 518: 514: 510: 507: 506: 505: 501: 497: 495: 484: 482: 478: 474: 470: 466: 456: 454: 453:"Packed SIMD" 450: 449:significantly 445: 441: 437: 433: 427: 417: 414: 410: 406: 401: 399: 395: 391: 387: 383: 378: 376: 372: 371:minicomputers 368: 364: 360: 356: 352: 348: 344: 336: 332: 328: 326: 325: 319: 314: 312: 308: 303: 301: 296: 292: 290: 287: 283: 280: 269: 266: 258: 248: 244: 240: 234: 233: 229: 224:This section 222: 218: 213: 212: 204: 202: 192: 190: 186: 182: 180: 176: 172: 167: 163: 159: 154: 152: 148: 144: 140: 136: 132: 128: 113: 111: 107: 103: 99: 98:supercomputer 94: 92: 88: 84: 80: 76: 72: 68: 64: 60: 56: 52: 48: 44: 40: 33: 29: 22: 7082:Coprocessors 7056:Chip carrier 6994:Clock gating 6913:Mixed-signal 6810:Write buffer 6787:Control unit 6599:Clock signal 6338:accelerators 6320:Cypress PSoC 6180: 5977:Simultaneous 5941: 5794:Out-of-order 5426:Neuromorphic 5307:Architecture 5265:Belt machine 5258:Zeno machine 5191:Hierarchical 4817: 4653:Coordination 4583:Amdahl's law 4519:Simultaneous 4351: 4339: 4328: 4317: 4306: 4295: 4284: 4273: 4262: 4248: 4221: 4211: 4189: 4174: 4165: 4154: 4143: 4132: 4121: 4110: 4101: 4087: 4073: 4062: 4051: 4037: 4026: 4015: 4004: 3995: 3970: 3955: 3944: 3933: 3922: 3908: 3894: 3877: 3866: 3857: 3830: 3824: 3812:. Retrieved 3802: 3791: 3780: 3761: 3755: 3743:. Retrieved 3738: 3728: 3634: 3633: 3565: 3506: 3499: 3498: 3493: 3492: 3490: 3479: 3474: 3471: 3467: 3463: 3458: 3454: 3452: 3448: 3412: 3399:mini-permute 3394: 3381: 3368: 3351: 3342:Galois field 3337: 3327: 3307: 3301: 3295: 3277: 3267: 3257: 3247: 3243: 3239: 3235: 3230: 3222: 3211: 3194: 3185: 3181: 3177: 3047: 2993: 2991: 2982: 2974: 2964: 2962: 2933: 2908:; 2nd 4 of x 2852: 2848: 2845: 2837: 2707: 2699: 2567: 2555: 2551: 2546: 2543: 2540: 2530: 2523: 2506: 2501: 2498: 2335: 2329: 2327: 2321: 2319: 2315: 2312: 2302: 2299: 2295: 2021: 2008: 2005: 1998: 1981: 1977: 1975: 1971: 1955: 1952: 1947: 1945: 1772: 1741: 1668: 1496: 1488: 1356: 1334: 1327: 1311:"predicated" 1295: 1279: 1275:power of two 1264: 1248: 1244: 1224: 1218: 1209: 1205: 1201: 1199: 1191: 1188: 1164: 1073: 1068: 1064: 1062: 983:; count = 10 959: 783: 778: 775: 768: 753: 741: 724:single-issue 723: 721: 713: 709: 702: 688: 677: 665:power of two 649: 645: 636:Fujitsu FR-V 621: 616:CDC STAR-100 592:Pure Vectors 591: 574: 535:extensions, 515:(SWAR), and 508: 498:fixed-length 493: 490: 462: 448: 429: 402: 379: 340: 323: 315: 304: 297: 293: 276: 261: 252: 237:Please help 225: 198: 183: 155: 135:coprocessors 130: 124: 95: 66: 59:instructions 46: 42: 36: 6841:Multiplexer 6805:Data buffer 6516:Single-core 6488:bit slicing 6346:Coprocessor 6201:Coprocessor 6082:performance 6004:Cooperative 5994:Speculative 5954:Distributed 5913:Superscalar 5898:Instruction 5866:Parallelism 5839:Speculative 5671:System/3x0 5543:Instruction 5320:Von Neumann 5233:Post–Turing 5089:Scalability 4850:distributed 4733:Concurrency 4700:Programming 4541:Cooperative 4530:Speculative 4466:Instruction 3814:23 December 3382:Sub-vectors 3358:many others 2519:instruction 1962:inner loop. 937:; decrement 738:Description 555:. In 2000, 543:extension, 473:open source 149:to a large 7071:Categories 6961:management 6856:Multiplier 6717:Logic gate 6707:Sequential 6614:Functional 6594:Clock rate 6567:Data cache 6540:Components 6521:Multi-core 6509:Core count 5999:Preemptive 5903:Pipelining 5886:Bit-serial 5829:Wide-issue 5774:Structural 5696:Tilera ISA 5662:MicroBlaze 5632:ETRAX CRIS 5527:Comparison 5372:Load–store 5352:Endianness 5094:Starvation 4833:asymmetric 4568:PRAM model 4536:Preemptive 4202:2104.03142 3986:155860491X 3848:2065/10689 3771:5770761318 3720:References 3475:subsequent 3459:subsequent 3440:extension. 3248:fail-first 3183:critical. 2967:of adding 1775:y = mx + c 1235:See also: 1225:efficiency 612:RISC-V RVV 551:and MIPS' 539:, Sparc's 477:ForwardCom 77:(SIMD) or 57:where its 6895:Circuitry 6815:Microcode 6739:Registers 6582:coherence 6557:CPU cache 6415:Word size 6080:Processor 5724:Execution 5627:DEC Alpha 5605:Power ISA 5421:Cognitive 5228:Universal 4828:symmetric 4573:PEM model 3796:MIAOW GPU 3614:− 3580:∞ 3540:∗ 3531:− 3429:logarithm 3407:"swizzle" 3403:Videocore 3319:x = y + x 3315:mapreduce 3310:Iteration 3098:vredadd32 3031:vredadd32 2965:by design 2422:# store Y 2189:store32x4 1851:store32x4 1346:Videocore 1337:Videocore 1267:Videocore 1142:# 10 adds 892:; move on 763:, so the 481:Libre-SOC 405:SX series 390:Cray Y-MP 386:Cray X-MP 255:July 2023 226:does not 162:ILLIAC IV 147:algorithm 129:in their 39:computing 6833:Datapath 6526:Manycore 6498:variable 6336:Hardware 5972:Temporal 5652:OpenRISC 5347:Cellular 5337:Dataflow 5330:modified 5059:Deadlock 5047:Problems 5013:pthreads 4993:OpenHMPP 4918:Ateji PX 4879:computer 4750:Hardware 4617:Elements 4603:Slowdown 4514:Temporal 4496:Pipeline 4181:Archived 3967:(1998). 3642:See also 3292:instead. 2994:built-in 2896:load32x4 2881:load32x4 2117:load32x4 2099:load32x4 1797:load32x4 1785:load32x4 1065:hardware 674:vectors. 583:ARM SVE2 537:ARM NEON 335:Cray J90 300:CDC 7600 282:STAR-100 181:(SIMT). 151:data set 7009:Related 6940:Quantum 6930:Digital 6925:Boolean 6823:Counter 6722:Quantum 6483:512-bit 6478:256-bit 6473:128-bit 6316:(MPSoC) 6301:on chip 6299:Systems 6117:(FLOPS) 5930:Process 5779:Control 5761:Hazards 5647:Itanium 5642:Unicore 5600:PowerPC 5325:Harvard 5285:Pointer 5280:Counter 5238:Quantum 5018:RaftLib 4998:OpenACC 4973:GPUOpen 4963:C++ AMP 4938:Charm++ 4680:Barrier 4624:Process 4608:Speedup 4393:General 4176:YouTube 3764:. KIT. 3480:exactly 3438:MIPS-3D 3272:AVX-512 3244:segment 3240:indexed 2978:AVX-512 2911:add32x4 2389:vmadd32 2270:# loop? 2162:add32x4 2135:mul32x4 2001:AVX-512 1986:AVX-512 1978:triples 1830:add32x4 1809:mul32x4 1756:splatx4 1574:store32 1323:AltiVec 1303:AVX-512 1071:basis. 770:latency 684:AVX-512 587:AVX-512 561:Toshiba 549:AltiVec 545:PowerPC 365:-based 355:Hitachi 351:Fujitsu 247:removed 232:sources 160:as the 131:Solomon 116:History 67:vectors 6945:Switch 6935:Analog 6673:(IMC) 6644:(MMU) 6493:others 6468:64-bit 6463:48-bit 6458:32-bit 6453:24-bit 6448:16-bit 6443:15-bit 6438:12-bit 6275:Mobile 6191:Stream 6186:Barrel 6181:Vector 6170:(GPU) 6129:(SUPS) 6097:(IPC) 5949:Memory 5942:Vector 5925:Thread 5908:Scalar 5710:Others 5657:RISC-V 5622:SuperH 5591:Power 5587:MIPS-X 5562:PDP-11 5411:Fabric 5163:Models 5111: 4988:OpenCL 4983:OpenMP 4928:Chapel 4845:shared 4840:Memory 4775:(SIMT) 4718:Models 4629:Thread 4561:Theory 4532:(SpMT) 4486:Memory 4471:Thread 4454:Levels 4236: 4112:GitHub 4006:GitHub 3983: 3868:GitHub 3768: 3745:7 July 3689:RISC-V 3455:actual 3425:cosine 3389:SPIR-V 3386:Vulkan 3371:shader 3065:vloop: 2731:load32 2686:return 2638:size_t 2584:size_t 2558:no-ops 2537:1977). 2341:vloop: 2330:actual 2303:at all 2027:vloop: 1782:vloop: 1520:load32 1505:load32 1424:size_t 1376:size_t 1305:, ARM 1282:Cray-1 1145:vstore 1085:setvli 1040:vstore 779:itself 728:Cray-1 672:within 657:vsetvl 610:, and 608:NEC SX 600:Cray-1 533:3DNow! 382:Cray-2 363:Oregon 347:ETA-10 307:Cray-1 166:GFLOPS 7001:(PPW) 6959:Power 6851:Adder 6727:Array 6694:Logic 6655:(TLB) 6638:(FPU) 6632:(AGU) 6626:(ALU) 6616:units 6552:Cache 6433:8-bit 6428:4-bit 6423:1-bit 6387:(TPU) 6381:(DSP) 6375:(PPU) 6369:(VPU) 6358:(GPU) 6327:(NoC) 6310:(SoC) 6245:(PoP) 6239:(SiP) 6233:(MCM) 6174:GPGPU 6164:(CPU) 6154:Types 6135:(PPW) 6123:(TPS) 6111:(IPS) 6103:(CPI) 5874:Level 5685:S/390 5680:S/370 5675:S/360 5617:SPARC 5595:POWER 5478:TRIPS 5446:Types 4958:Dryad 4923:Boost 4644:Array 4634:Fiber 4548:(CMT) 4521:(SMT) 4435:GPGPU 4197:arXiv 3977:751-2 3886:(PDF) 3659:GPGPU 3391:spec. 3164:vloop 3083:vld32 3068:setvl 3016:vld32 3001:setvl 2853:other 2746:add32 2728:loop: 2602:const 2513:setvl 2502:going 2491:vloop 2410:vst32 2374:vld32 2359:vld32 2344:setvl 2282:vloop 2054:shift 2012:setvl 1948:shall 1932:vloop 1553:add32 1532:mul32 1502:loop: 1394:const 1370:iaxpy 1202:other 1109:vload 1094:vload 1055:count 1019:count 1004:vload 1001:count 986:vload 980:count 943:count 934:count 862:store 814:loop: 808:count 651:has: 49:is a 6979:ACPI 6712:Glue 6604:FIFO 6547:Core 6285:ASIP 6226:CPLD 6221:FPOA 6216:FPGA 6211:ASIC 6064:SPMD 6059:MIMD 6054:MISD 6047:SWAR 6027:SIMD 6022:SISD 5937:Data 5920:Task 5891:Word 5637:M32R 5582:MIPS 5545:sets 5512:ZISC 5507:NISC 5502:OISC 5497:MISC 5490:EPIC 5485:VLIW 5473:EDGE 5463:RISC 5458:CISC 5367:HUMA 5362:NUMA 5023:ROCm 4953:CUDA 4943:Cilk 4910:APIs 4870:COMA 4865:NUMA 4796:MIMD 4791:MISD 4768:SIMD 4763:SISD 4491:Loop 4481:Data 4476:Task 4234:ISBN 3981:ISBN 3816:2022 3766:ISBN 3747:2024 3491:Let 3427:and 3421:sine 3332:CUDA 3302:Iota 3155:bnez 2875:$ 16 2860:addl 2824:out: 2818:loop 2788:subl 2767:addl 2656:< 2578:void 2511:The 2482:bnez 2322:must 2288:out: 2249:subl 2231:addl 2210:addl 1938:out: 1902:subl 1899:$ 16 1884:addl 1878:$ 16 1863:addl 1705:vadd 1678:vmul 1661:out: 1655:loop 1625:subl 1607:addl 1586:addl 1442:< 1367:void 1307:SVE2 1291:GPUs 1280:The 1239:and 1237:SIMD 1212:RISC 1124:vadd 1088:$ 10 1022:vadd 974:$ 10 971:move 949:loop 940:jnez 829:load 817:load 802:$ 10 799:move 632:EPIC 628:VLIW 624:MIMD 585:and 565:Sony 563:and 527:and 479:and 432:GPUs 388:and 357:and 284:and 230:any 228:cite 102:Cray 41:, a 28:GPUs 6974:APM 6969:PMU 6861:CPU 6818:ROM 6589:Bus 6206:PAL 5881:Bit 5667:LMC 5572:ARM 5567:x86 5557:VAX 5038:ZPL 5033:TBB 5028:UPC 5008:PVM 4978:MPI 4933:HPX 4860:UMA 4461:Bit 4226:doi 3843:hdl 3835:doi 3433:HPC 3170:ret 3140:sub 3119:add 3053:set 2969:x+x 2957:x+x 2951:x+x 2945:x+x 2939:x+x 2849:not 2827:ret 2809:jgz 2803:$ 1 2782:$ 4 2713:set 2632:for 2617:int 2605:int 2593:int 2524:and 2517:min 2467:sub 2446:add 2425:add 2291:ret 2273:jgz 2090:$ 1 2075:sub 2063:$ 1 2048:$ 4 2033:min 1994:gcc 1992:to 1941:ret 1923:jgz 1917:$ 4 1732:ret 1720:tmp 1681:tmp 1664:ret 1646:jgz 1640:$ 1 1622:$ 4 1601:$ 4 1418:for 1406:int 1397:int 1385:int 1319:SSE 1315:MMX 1271:REP 1259:REP 1255:x86 1206:not 1160:ret 1058:ret 955:ret 931:dec 928:$ 4 913:add 910:$ 4 895:add 889:$ 4 874:add 841:add 661:lvl 557:IBM 553:MSA 547:'s 541:VIS 529:AVX 525:SSE 521:MMX 420:GPU 413:HBM 241:by 45:or 37:In 7073:: 6908:3D 4232:. 4220:. 4179:. 4173:. 4109:. 4003:. 3979:. 3963:; 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