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will generate a list of all the tasks and the details of the cores on which they will execute along with the time that they will execute for. The code
Generator will insert special constructs in the code that will be read during execution by the scheduler. These constructs will instruct the scheduler
610:
Recent research focuses on using the power of GPU's and multicore systems to compute such independent code blocks( or simply independent iterations of a loop) at runtime. The memory accessed (whether direct or indirect) can be simply marked for different iterations of a loop and can be compared for
288:
The second pass attempts to justify the parallelization effort by comparing the theoretical execution time of the code after parallelization to the code's sequential execution time. Somewhat counterintuitively, code does not always benefit from parallel execution. The extra overhead that can be
164:
of a program takes place inside some form of loop. There are two main approaches to parallelization of loops: pipelined multi-threading and cyclic multi-threading. For example, consider a loop that on each iteration applies a hundred operations, and runs for a thousand iterations. This can be
195:
is used to identify sections of code that can be executed concurrently. The analyzer uses the static data information provided by the scanner-parser. The analyzer will first find all the totally independent functions and mark them as individual tasks. The analyzer then finds which tasks have
644:
Due to the inherent difficulties in full automatic parallelization, several easier approaches exist to get a parallel program in higher quality. One of these is to allow programmers to add "hints" to their programs to guide compiler parallelization, such as
183:. These tokens will be stored in a file which will be used later by the grammar engine. The grammar engine will check patterns of tokens that match with pre-defined rules to identify variables, loops, control statements, functions etc. in the code.
208:
will list all the tasks and their dependencies on each other in terms of execution and start times. The scheduler will produce the optimal schedule in terms of number of processors to be used or the total execution time for the application.
165:
thought of as a grid of 100 columns by 1000 rows, a total of 100,000 operations. Cyclic multi-threading assigns each row to a different thread. Pipelined multi-threading assigns each column to a different thread.
543:
However, current parallelizing compilers are not usually capable of bringing out these parallelisms automatically, and it is questionable whether this code would benefit from parallelization in the first place.
276:
of the loop to determine whether each iteration of the loop can be executed independently of the others. Data dependence can sometimes be dealt with, but it may incur additional overhead in the form of
178:
This is the first stage where the scanner will read the input source files to identify all static and extern usages. Each line in the file will be checked against pre-defined patterns to segregate into
611:
dependency detection. Using this information, the iterations are grouped into levels such that iterations belonging to the same level are independent of each other, and can be executed in parallel.
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A pipelined multi-threading parallelizing compiler tries to break up the sequence of operations inside a loop into a series of code blocks, such that each code block can be executed on separate
623:
dependence analysis is hard for code that uses indirect addressing, pointers, recursion, or indirect function calls because it is difficult to detect such dependencies at compile time;
673:
Explorer (The
Stanford University Intermediate Format compiler), the Polaris compiler, and ParaWise (formally CAPTools). Finally, another approach is hardware-supported
268:
Is it worthwhile to parallelize it? This answer requires a reliable estimation (modeling) of the program workload and the capacity of the parallel system.
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code below is DOALL, and can be auto-parallelized by a compiler because each iteration is independent of the others, and the final result of array
938:
1231:
999:"Configuring parallel programs, Part 1: The Occam Transpiler, now under development, will make writing software for parallel processing easier"
603:
A pipelined multi-threading parallelizing compiler could assign each of these six operations to a different processor, perhaps arranged in a
386:
a ray-traced movie, each frame of the movie can be independently rendered, and each pixel of a single frame may be independently rendered.
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systems. Another approach is to build an interactive system between programmers and parallelizing tools/compilers. Notable examples are
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There are many pleasingly parallel problems that have such relatively independent code blocks, in particular systems using
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program as input and derives a new occam source code as output with link-to-channel assignments etc. added to it thereby
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accesses to global resources are difficult to coordinate in terms of memory allocation, I/O, and shared variables;
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For example, when producing live broadcast television, the following tasks must be performed many times a second:
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149:) machine. Fully automatic parallelization of sequential programs is a challenge because it requires complex
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usually conducts two passes of analysis before actual parallelization in order to determine the following:
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A loop is called DOALL if all of its iterations, in any given invocation, can be executed concurrently.
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This does not mean that the code cannot be parallelized. Indeed, it is equivalent to the DOALL loop
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associated with using multiple processors can eat into the potential speedup of parallelized code.
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Automatic parallelization by compilers or tools is very difficult due to the following reasons:
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797:"Experiments in Separating Computational Algorithm from Program Distribution and Communication"
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607:, inserting the appropriate code to forward the output of one processor to the next processor.
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and the best approach may depend upon parameter values that are not known at compilation time.
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that use input-dependent indirection interfere with compile-time analysis and optimization.
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The programming control structures on which autoparallelization places the most focus are
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Campanoni, Simone; Jones, Timothy; Holloway, Glenn; Wei, Gu-Yeon; Brooks, David (2012).
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On the other hand, the following code cannot be auto-parallelized, because the value of
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Add the appropriate error correction and do a FFT to convert the data packets into
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on which core a particular task will execute along with the start and end times.
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891:"Runtime dependence computation and execution of loops on heterogeneous systems"
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Rünger, Gudula (2006). "Parallel
Programming Models for Irregular Algorithms".
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will be correct regardless of the execution order of the other iterations.
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Is it safe to parallelize the loop? Answering this question needs accurate
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code in order to use multiple processors simultaneously in a shared-memory
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Applied
Parallel Computing. New Paradigms for HPC in Industry and Academia
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Zhuang, X.; Eichenberger, A. E.; Luo, Y.; O'Brien, Kathryn Kevin,
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960:. Lecture Notes in Computational Science and Engineering.
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programs, because
Fortran makes stronger guarantees about
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A cyclic multi-threading parallelizing compiler tries to
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Exploiting
Parallelism with Dependence-Aware Scheduling
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problems that have such DOALL loops. For example, when
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Read a frame of raw pixel data from the image sensor,
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Fox, Geoffrey; Williams, Roy; Messina, Paul (1994).
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Entropy compress the motion vectors and other data,
285:, or some other method of processor communication.
46:. Unsourced material may be challenged and removed.
842:
393:depends on the result of the previous iteration,
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1055:to run as efficient as possible on a network of
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1232:Induction variable recognition and elimination
1078:
788:
547:
939:"Automatic parallelism and data dependency"
626:loops have an unknown number of iterations;
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1085:
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949:
875:The HELIX Project: Overview and Directions
599:Send the COFDM signals out the TV antenna.
589:Break up the compressed data into packets,
272:The first pass of the compiler performs a
958:Parallel Algorithms and Cluster Computing
794:
225:
106:Learn how and when to remove this message
996:
931:
1266:Sparse conditional constant propagation
695:for automatic parallelization consider
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955:
1066:
44:adding citations to reliable sources
15:
169:Automatic parallelization technique
160:, because, in general, most of the
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990:
212:
14:
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808:Lecture Notes in Computer Science
681:Parallelizing compilers and tools
246:Compiler parallelization analysis
1216:Common subexpression elimination
997:Pountain, Dick (December 1989).
129:refers to converting sequential
20:
1357:Compile-time function execution
889:Anantpur, J.; Govindarajan, R.
614:
31:needs additional citations for
757:also known as Polyhedral model
687:Automatic parallelization tool
238:can be executed on a separate
1:
1007:. Vol. 14, no. 13.
781:
639:
1336:Interprocedural optimization
7:
1387:Profile-guided optimization
1352:Bounds-checking elimination
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199:
55:"Automatic parallelization"
10:
1485:
1151:Loop-invariant code motion
795:Yehezkael, Rafael (2000).
684:
675:speculative multithreading
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292:
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1131:Automatic parallelization
1121:
1100:
1035:source-to-source compiler
845:Parallel Computing Works!
548:Pipelined multi-threading
119:Automatic parallelization
820:10.1007/3-540-70734-4_32
750:Parallelization contract
707:. Typical examples are:
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399:
310:
274:data dependence analysis
173:
1136:Automatic vectorization
970:10.1007/3-540-33541-2_1
733:Vienna Fortran compiler
722:Rice Fortran D compiler
703:than languages such as
1464:Compiler optimizations
1284:Instruction scheduling
1261:Global value numbering
1237:Live-variable analysis
1166:Loop nest optimization
1094:Compiler optimizations
1019:. ark:/13960/t34188734
745:Loop nest optimization
226:Cyclic multi-threading
1413:Control-flow analysis
1408:Array-access analysis
1362:Dead-code elimination
1320:Tail-call elimination
1289:Instruction selection
1113:Local value numbering
1108:Peephole optimization
851:. pp. 575, 593.
281:, synchronization of
1443:Value range analysis
1367:Expression templates
1211:Available expression
1041:that takes a normal
814:. pp. 268–278.
761:Scalable parallelism
633:irregular algorithms
123:auto parallelization
40:improve this article
1423:Dependence analysis
1294:Register allocation
1186:Software pipelining
1053:parallel processing
1033:as a synonym for a
1029:(NB. Uses the term
581:motion compensation
554:software pipelining
380:pleasingly parallel
260:dependence analysis
127:autoparallelization
1469:Parallel computing
1418:Data-flow analysis
1382:Partial evaluation
1191:Strength reduction
1141:Induction variable
1011:pp. 349–352.
810:. Vol. 1947.
651:distributed memory
1451:
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1299:Rematerialization
1009:McGraw-Hill, Inc.
979:978-3-540-33539-9
903:on 6 October 2015
858:978-1-55860-253-3
829:978-3-540-41729-3
712:Paradigm compiler
567:pipes and filters
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108:
90:
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1433:Pointer analysis
1372:Inline expansion
1242:Use-define chain
1221:Constant folding
1181:Loop unswitching
1161:Loop interchange
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1080:
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1064:
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1031:Occam transpiler
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1024:
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945:on 14 July 2014.
941:. Archived from
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896:. Archived from
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717:Polaris compiler
583:on the raw data,
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771:Vectorization
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96:February 2008
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57: –
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51:Find sources:
45:
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29:This article
27:
23:
18:
17:
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1046:
1030:
1021:. Retrieved
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943:the original
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905:. Retrieved
898:the original
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653:systems and
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615:Difficulties
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596:signals, and
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38:Please help
33:verification
30:
1228:elimination
1146:Loop fusion
1101:Basic block
1057:transputers
1048:configuring
1458:Categories
1308:Functional
1226:Dead store
782:References
685:See also:
669:' Pareon,
640:Workaround
560:processors
139:vectorized
66:newspapers
1201:Data-flow
1023:6 January
1017:0360-5280
907:5 October
776:SequenceL
693:compilers
384:rendering
240:processor
236:iteration
219:scheduler
206:scheduler
1203:analysis
964:: 3–23.
739:See also
729:compiler
701:aliasing
659:OpenHMPP
579:Do MPEG
395:z(i - 1)
252:compiler
200:Schedule
193:analyzer
1051:it for
697:Fortran
302:Fortran
293:Example
187:Analyze
137:and/or
121:, also
80:scholar
1329:Global
1254:-based
1015:
976:
855:
826:
655:OpenMP
181:tokens
82:
75:
68:
61:
53:
1345:Other
1043:occam
901:(PDF)
894:(PDF)
800:(PDF)
766:BMDFM
594:COFDM
538:enddo
459:enddo
373:enddo
174:Parse
158:loops
133:into
125:, or
87:JSTOR
73:books
1123:Loop
1025:2022
1013:ISSN
1004:BYTE
974:ISBN
909:2015
853:ISBN
824:ISBN
727:SUIF
671:SUIF
661:for
649:for
391:z(i)
300:The
262:and
250:The
217:The
204:The
191:The
131:code
59:news
1252:SSA
966:doi
816:doi
657:or
647:HPF
469:do
402:do
313:do
147:SMP
42:by
1460::
1059:.)
1001:.
972:.
962:52
847:.
822:.
806:.
802:.
677:.
569:.
520:**
397:.
1086:e
1079:t
1072:v
1027:.
982:.
968::
911:.
878:.
861:.
832:.
818::
705:C
535:)
532:1
529:-
526:i
523:(
517:2
514:*
511:)
508:1
505:(
502:z
499:=
496:)
493:i
490:(
487:z
484:n
481:,
478:2
475:=
472:i
456:2
453:*
450:)
447:1
444:-
441:i
438:(
435:z
432:=
429:)
426:i
423:(
420:z
417:n
414:,
411:2
408:=
405:i
370:)
367:i
364:(
361:y
358:+
355:)
352:i
349:(
346:x
343:=
340:)
337:i
334:(
331:z
328:n
325:,
322:1
319:=
316:i
306:z
145:(
109:)
103:(
98:)
94:(
84:·
77:·
70:·
63:·
36:.
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