Maximizing loop parallelism and improving data locality via loop fusion and distribution

Kennedy, Ken; McKinley, Kathryn S.

doi:10.1007/3-540-57659-2_18

Cited by 145 publications

(151 citation statements)

References 16 publications

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“…Since loops are prevalent in multimedia processing, digital signal processing, etc., loop transformations such as loop fusion, loop distribution are needed to optimize the execution of the loops in embedded DSP applications [8,[1][2][3]5].…”

Section: Introductionmentioning

confidence: 99%

“…Loop distribution separates independent statements inside a single loop (or loop nest) into multiple loops (or loop nests) [8,1,3,2,5]. Loop distribution can be used to break up a large loop that doesn't fit into the cache [2,3,8,1].…”

Section: Introductionmentioning

confidence: 99%

“…Loop distribution can also enable other loop optimization techniques such as loop fusion, loop interchanging and loop permutation [8,1,3,5].…”

Section: Introductionmentioning

confidence: 99%

“…There are a lot of previous works using loop fusion to optimize the execution of loops [2,3,8,1,4,5,7]. But sometimes loop shifting or retiming is needed to enable loop fusion, which will cause the code size expansion because of the generation of the prologue and epilogue.…”

Section: Introductionmentioning

confidence: 99%

“…All the data dependences have to be preserved when breaking one single loop into multiple small loops. The authors of [2,3,5] stated that loop distribution preserves dependences if all statements involved in a data dependence cycle in the original loop are placed in the same loop. We show that dependence cycle is a restriction for loop distribution for one-level loops only.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Loop Distribution and Fusion with Timing and Code Size Optimization for Embedded DSPs

Liu¹,

Zhuge²,

Shao

et al. 2005

Embedded and Ubiquitous Computing – EUC 2005

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Abstract. Loop distribution and loop fusion are two effective loop transformation techniques to optimize the execution of the programs in DSP applications. In this paper, we propose a new technique combining loop distribution with direct loop fusion, which will improve the timing performance without jeopardizing the code size. We first develop the loop distribution theorems that state the legality conditions of loop distribution for multi-level nested loops. We show that if the summation of the edge weights of the dependence cycle satisfies a certain condition, then the statements involved in the dependence cycle can be distributed; otherwise, they should be put in the same loop after loop distribution. Then, we propose the technique of maximum loop distribution with direct loop fusion. The experimental results show that the execution time of the transformed loops by our technique is reduced 21.0% on average compared to the original loops and the code size of the transformed loops is reduced 7.0% on average compared to the original loops.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Loop distribution can also enable other loop optimization techniques such as loop fusion, loop interchanging and loop permutation [8,1,3,5].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Loop Distribution and Fusion with Timing and Code Size Optimization for Embedded DSPs

Liu¹,

Zhuge²,

Shao

et al. 2005

Embedded and Ubiquitous Computing – EUC 2005

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Analysis and transformation in an interactive parallel programming tool

Kennedy

McKinley

Tseng

1993

Concurrency: Pract. Exper.

Self Cite

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The ParaScope Editor is a new kind of interactive parallel programming tool for developing scienti c Fortran programs. It assists the knowledgeable user by displaying the results of sophisticated program analyses and by providing editing and a set of powerful interactive transformations. After an edit or parallelism-enhancing transformation, the ParaScope Editor incrementally updates both the analyses and source quickly. This paper describes the underlying implementation of the ParaScope Editor, paying particular attention to the analysis and representation of dependence information and its reconstruction after changes to the program.

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A study on popular auto‐parallelization frameworks

Prema

Nasre

Jehadeesan

et al. 2019

Concurrency and Computation

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We study five popular auto-parallelization frameworks (Cetus, Par4all, Rose, ICC, and Pluto) andcompare them qualitatively as well as quantitatively. All the frameworks primarily deal with loop parallelization but differ in the techniques used to identify parallelization opportunities. Due to this variance, various aspects, such as certain loop transformations, are supported only in a few frameworks. The frameworks exhibit varying abilities in handling loop-carried dependence and, therefore, achieve different amounts of speedup on widely used PolyBench and NAS parallel benchmarks. In particular, Intel C Compiler (ICC) fares as an overall good parallelizer. Our study also highlights the need for more sophisticated analyses, user-driven parallelization, and meta-auto-parallelizer that provides combined benefits of various frameworks. KEYWORDS loop-carried dependence, loop transformations, privatization, vectorization INTRODUCTIONQuest for performance has made multicore processors mainstream. It becomes vital for the programmers and algorithm developers to exploit these ubiquitous advanced architectures with parallel programming. Exploiting the potential of multicore processors through parallel programming is a significant challenge. Amid several approaches to tame this challenge, a promising and programmer-friendly approach is automatic parallelization. 1-3 Auto-parallelizers eliminate the need for a programmer to transform a sequential code into a parallel code, which is quite attractive.Source-to-source transformations with insertion of parallel directives are performed by auto-parallelizers, such as Cetus, 4-7 Par4all, 8,9 Pluto,10,11 Parallware, 12,13 Rose, 14,15 Intel C Compiler, 16 LLVM (Low Level Virtual Machine) Polly, 17 ParaWise, 18 ParaGraph, 19 SUIF (Stanford University Intermediate Format), 20-22 and Polaris. 23,24 Although these existing parallelizers offer considerable benefits, they still fall short of fully replacing the manual transformations. Several parallelizers do not utilize all the static information available, whereas several others fall short of modeling precision. It leads to either missed parallelism opportunities or unwanted parallelization of sequential codes. As a consequence, compared to the original sequential version, the auto-parallelized code may lead to parallelization overheads and may exhibit poorer performance. 25Earlier studies 26,27 of the parallelizing compilers using Perfect benchmarks performed a detailed analysis of the code restructuring techniques.The techniques include induction variable elimination, scalar expansion, forward substitution, strip mining, and loop interchange. The studies found that some of the programs showed improvements and that scalar expansion led to positive results. An effectiveness study on the Polaris compiler with OpenMP (Open Multi-Processing) 28 parallel code using Perfect benchmarks proclaimed a performance lag in small parallel loops. It also illustrated the importance of reduction operation, which resulted in a moderate (10%) performance...

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Maximizing loop parallelism and improving data locality via loop fusion and distribution

Cited by 145 publications

References 16 publications

Loop Distribution and Fusion with Timing and Code Size Optimization for Embedded DSPs

Loop Distribution and Fusion with Timing and Code Size Optimization for Embedded DSPs

Analysis and transformation in an interactive parallel programming tool

A study on popular auto‐parallelization frameworks

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