Restructuring programs for high-speed computers with Polaris

Blume, Holger; Eigenmann,; Faigin,; Grout, Ian; Lee, Jaejin; Lawrence,; Hoeflinger,; Padua,; Paek, Yunheung; Petersen, J. C.; Pottenger,; Rauchwerger,; Tu, Peng

doi:10.1109/icppw.1996.538601

Cited by 52 publications

(40 citation statements)

References 15 publications

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“…The Polaris [5,16] compiler, which we have co-developed in prior work, was an important influence on the design of our new infrastructure. Polaris is written in C++ and operates on Fortran 77 programs.…”

Section: The Polaris Compilermentioning

confidence: 99%

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Cetus – An Extensible Compiler Infrastructure for Source-to-Source Transformation

Lee

Johnson

Eigenmann

2004

Languages and Compilers for Parallel Computing

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Abstract. Cetus is a compiler infrastructure for the source-to-source transformation of programs. We created Cetus out of the need for a compiler research environment that facilitates the development of interprocedural analysis and parallelization techniques for C, C++, and Java programs. We will describe our rationale for creating a new compiler infrastructure and give an overview of the Cetus architecture. The design is intended to be extensible for multiple languages and will become more flexible as we incorporate feedback from any difficulties we encounter introducing other languages. We will characterize Cetus' runtime behavior of parsing and IR generation in terms of execution time, memory usage, and parallel speedup of parsing, as well as motivate its usefulness through examples of projects that use Cetus. We will then compare these results with those of the Polaris Fortran translator.

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Section: The Polaris Compilermentioning

confidence: 99%

“…Parallelizing compiler technology is most mature for the Fortran 77 language [4,5,17,19]. The simplicity of the language without pointers or user-defined types makes it easy to analyze and to develop many advanced compiler passes.…”

Section: Introductionmentioning

confidence: 99%

Cetus – An Extensible Compiler Infrastructure for Source-to-Source Transformation

Lee

Johnson

Eigenmann

2004

Languages and Compilers for Parallel Computing

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“…We have implemented the previously presented method of reducing marking points in a program through the grouping algorithm in the POLARIS compiler infrastructure [2].…”

Section: Run-time Overhead Reductionmentioning

confidence: 99%

Techniques for Reducing the Overhead of Run-Time Parallelization

Rauchwerger

2000

Lecture Notes in Computer Science

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Abstract. Current parallelizing compilers cannot identify a significant fraction of parallelizable loops because they have complex or statically insufficiently defined access patterns. As parallelizable loops arise frequently in practice, we have introduced a novel framework for their identification: speculative parallelization. While we have previously shown that this method is inherently scalable its practical success depends on the fraction of ideal speedup that can be obtained on modest to moderately large parallel machines. Maximum parallelism can be obtained only through a minimization of the run-time overhead of the method, which in turn depends on its level of integration within a classic restructuring compiler and on its adaptation to characteristics of the parallelized application. We present several compiler and run-time techniques designed specifically for optimizing the run-time parallelization of sparse applications. We show how we minimize the run-time overhead associated with the speculative parallelization of sparse applications by using static control flow information to reduce the number of memory references that have to be collected at run-time. We then present heuristics to speculate on the type and data structures used by the program and thus reduce the memory requirements needed for tracing the sparse access patterns. We present an implementation in the Polaris infrastructure and experimental results.

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“…It has also been used in distributed memory machines [14]. The present paper was motivated by an effort to include a tiling technique into our Polaris parallelizing compiler [5,13] for shared memory machines. (Polaris translates sequential Fortran 77 programs into parallel OpenMP form.…”

Section: Introductionmentioning

confidence: 99%

On the Interaction of Tiling and Automatic Parallelization

Pan

Armstrong

Bae

et al. 2008

OpenMP Shared Memory Parallel Programming

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Abstract. Iteration space tiling is a well-explored programming and compiler technique to enhance program locality. Its performance benefit appears obvious, as the ratio of processor versus memory speed increases continuously. In an effort to include a tiling pass into an advanced parallelizing compiler, we have found that the interaction of tiling and parallelization raises unexplored issues. Applying existing, sequential tiling techniques, followed by parallelization, leads to performance degradation in many programs. Applying tiling after parallelization without considering parallel execution semantics may lead to incorrect programs. Doing so conservatively, also introduces overhead in some of the measured programs. In this paper, we present an algorithm that applies tiling in concert with parallelization. The algorithm avoids the above negative effects. Our paper also presents the first comprehensive evaluation of tiling techniques on compiler-parallelized programs. Our tiling algorithm improves the SPEC CPU95 floating-point programs by up to 21% over non-tiled versions (4.9% on average) and the SPEC CPU2000 Fortran 77 programs up to 49% (11% on average). Notably, in about half of the benchmarks, tiling does not have a significant effect.

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Restructuring programs for high-speed computers with Polaris

Cited by 52 publications

References 15 publications

Cetus – An Extensible Compiler Infrastructure for Source-to-Source Transformation

Cetus – An Extensible Compiler Infrastructure for Source-to-Source Transformation

Techniques for Reducing the Overhead of Run-Time Parallelization

On the Interaction of Tiling and Automatic Parallelization

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