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alias the array regular via inter-procedural analysis, the loop can be safely parallelized.Therefore, the utility of an automatic parallelizing compiler is largely determined by thequality of its memory dependence analysis.In some cases, static analysis may be imprecise. For example, within the function foo,assume that there is a read from or write to the array element regular[i+M], (and thesize of the array is greater than (M+N)), where M is an input from the user. In this case,Loop A may not be DOALL-able depending on the value of M. If M is greater than N, theloop is DOALL-able; otherwise, it is not. Some research compilers such as SUIF [2] andPolaris [7, 64] integrate low-cost runtime analysis capabilities to insert a small test code tocheck the value of M at runtime to select either a sequential or parallel version of the loopaccordingly. However, the coverage of these techniques is mostly restricted to the caseswhen a predicate can be extracted outside the analyzed loop and a low cost runtime test canbe generated [64]. They cannot be applied to Loop B in Figure 2.1, for example, wherean index array is used to access the array irregular and a simple predicate cannot beextracted outside the loop to test the if condition within the loop body.Another issue with automatic parallelization is the fragility of static analysis. Figure 2.2from [33] illustrates how fragile static analysis can be with a small change in the program.In this example, the automatic parallelizer can easily parallelize the unmodified PolyBenchbenchmarks [54] using static arrays. However, if the static arrays are replaced with dynamicallyallocated arrays, it not only suppresses some of the optimizations previouslyapplied but also blocks parallelization for several benchmarks since heap objects are generallymore difficult to analyze. This leads to runtime performance that is highly sensitiveto the implementation style.Therefore, analysis-based approaches are not sufficient for parallelization of even arraybasedapplications, let alone pointer-based ones, having irregular memory accesses andcomplex control flows. Moreover, recursive data structures, dynamic memory allocation,and frequent accesses to shared variables pose additional challenges. Imprecise, fragile12
1: Loop_A:2: for (int i=0; i
Page 1 and 2: AUTOMATICALLY EXPLOITINGCROSS-INVOC
Page 4 and 5: techniques. Among twenty programs f
Page 6 and 7: in particular. Their professionalis
Page 8 and 9: ContentsAbstract . . . . . . . . .
Page 10 and 11: 4.5.4 Load Balancing Techniques . .
Page 12 and 13: 2.4 Sequential Loop Example for DOA
Page 14 and 15: 4.5 Overview of SPECCROSS: At compi
Page 16 and 17: 1.1 Limitations of Existing Approac
Page 18 and 19: advanced forms of parallelism (MPI,
Page 20 and 21: the graph stands for an iteration i
Page 22 and 23: 1.2 ContributionsFigure 1.5 demonst
Page 24 and 25: 1.3 Dissertation OrganizationChapte
Page 28 and 29: 1 for (i = 0; i < M; i++){2 node =
Page 30 and 31: 1 cost = 0;2 node = list->head;3 Wh
Page 32 and 33: example which cannot benefit from e
Page 34 and 35: These techniques are referred to as
Page 36 and 37: unnecessary overhead at runtime.Tab
Page 38 and 39: Chapter 3Non-Speculatively Exploiti
Page 40 and 41: for a variety of reasons. For insta
Page 42 and 43: 12x11x10xDOMOREPthread Barrier9xLoo
Page 44 and 45: Algorithm 1: Pseudo-code for schedu
Page 46 and 47: Algorithm 2: Pseudo-code for worker
Page 48 and 49: 3.3 Compiler ImplementationThe DOMO
Page 50 and 51: to T i . DOMORE’s MTCG follows th
Page 52 and 53: Outer_Preheaderbr BB1ABB1A:ind1 = P
Page 54 and 55: Algorithm 3: Pseudo-code for genera
Page 56 and 57: Scheduler Function SchedulerSync Fu
Page 58 and 59: SchedulerWorker1 Worker2Worker3Work
Page 60 and 61: 3.5 Related Work3.5.1 Cross-invocat
Page 62 and 63: ations during the inspecting proces
Page 64 and 65: for (t = 0; t < STEP; t++) {L1: for
Page 66 and 67: sequential_func() {for (t = 0; t <
Page 68 and 69: Workerthread 1Workerthread 2Workert
Page 70 and 71: library provides efficient misspecu
Page 72 and 73: Workerthread 1TimeFigure 4.6: Timin
Page 74 and 75: 4.2 SPECCROSS Runtime System4.2.1 M
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takes up to 200MB memory space.To d
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checkpoint, the child spawns new wo
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Operation DescriptionFunctions for
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Main thread:main() {init();create_t
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implemented in the Liberty parallel
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Algorithm 5: Pseudo-code for SPECCR
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CROSS, since SPECCROSS can be appli
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techniques.Synchronization via sche
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Source Benchmark Function % of exec
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applied to the outermost loop, gene
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5.2 SPECCROSS Performance Evaluatio
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and the number of checking requests
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8x7xno misspec.with misspec.Geomean
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This thesisPrevious workSpeedup (x)
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Program Speedup6x5x4x3x2xLOCALWRITE
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for DOMORE and SPECCROSS. Others (e
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programs and it achieves a geomean
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Bibliography[1] R. Allen and K. Ken
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[15] R. Cytron. DOACROSS: Beyond ve
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[31] T. B. Jablin, Y. Zhang, J. A.
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[47] A. Nicolau, G. Li, A. V. Veide
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[62] L. Rauchwerger and D. Padua. T
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national conference on Parallel Arc
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