Parallel programming with Polaris

Blume, William; Doallo, Ramón; Eigenmann, Rudolf; Grout, John; Hoeflinger, Jay; Lawrence, Thomas R.

doi:10.1109/2.546612

Cited by 263 publications

(124 citation statements)

References 4 publications

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“…Symbolic dependence analysis has been the subject of several studies [14,[18][19][20], mainly for the purpose of achieving powerful dependence testing, array privatization and generalized induction variable substitution, especially in the context of parallelizing compilers such as Polaris [5] and SUIF [17]. This body of work differs from the work presented in this paper in that our compiler does not focus on dependence testing, or try to statically find independent statements, in order to parallelize them.…”

Section: Related Workcontrasting

confidence: 47%

From Serial Loops to Parallel Execution on Distributed Systems

Bosilca

Bouteiller

Danalis

et al. 2012

Euro-Par 2012 Parallel Processing

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Abstract. Programmability and performance portability are two major challenges in today's dynamic environment. Algorithm designers targeting efficient algorithms should focus on designing high-level algorithms exhibiting maximum parallelism, while relying on compilers and runtime systems to discover and exploit this parallelism, delivering sustainable performance on a variety of hardware. The compiler tool presented in this paper can analyze the data flow of serial codes with imperfectly nested, affine loop-nests and if statements, commonly found in scientific applications. This tool operates as the front-end compiler for the DAGuE run-time system by automatically converting serial codes into the symbolic representation of their data flow. We show how the compiler analyzes the data flow, and demonstrate that scientifically important, dense linear algebra operations can benefit from this analysis, and deliver high performance on large scale platforms.

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Section: Related Workcontrasting

confidence: 47%

From Serial Loops to Parallel Execution on Distributed Systems

Bosilca

Bouteiller

Danalis

et al. 2012

Euro-Par 2012 Parallel Processing

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“…We implemented the tiling algorithm presented in Section 4 in the Polaris [6,13] parallelizing compiler. The experiments were done on an Ultra SPARC II machine with four 250 MHZ processors.…”

Section: Experimental Environmentmentioning

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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“…OpenMP [4], OpenACC [5]), on parallel programming languages (e.g. PGAS languages), and on parallelizing compilers that automatically rewrite sequential programs into a parallel counterpart [6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

A novel compiler support for automatic parallelization on multicore systems

et al. 2013

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The widespread use of multicore processors is not a consequence of significant advances in parallel programming.In contrast, multicore processors arise due to the complexity of building power-efficient, high-clock-rate, single-core chips. Automatic parallelization of sequential applications is the ideal solution for making parallel programming as easy as writing programs for sequential computers. However, automatic parallelization remains a grand challenge due to its need for complex program analysis and the existence of unknowns during compilation. This paper proposes a new method for converting a sequential application into a parallel counterpart that can be executed on current multicore processors. It hinges on an intermediate representation based on the concept of domain-independent kernel (e.g., assignment, reduction, recurrence). Such kernel-centric view hides the complexity of the implementation details, enabling the construction of the parallel version even when the source code of the sequential application contains different syntactic variations of the computations (e.g., pointers, arrays, complex control flows). Experiments that evaluate the effectiveness and performance of our approach with respect to state-of-the-art compilers are also presented. The benchmark suite consists of synthetic codes that represent common domain-independent kernels, dense/sparse linear algebra and image processing routines, and full-scale applications from SPEC CPU2000.

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Parallel programming with Polaris

Cited by 263 publications

References 4 publications

From Serial Loops to Parallel Execution on Distributed Systems

From Serial Loops to Parallel Execution on Distributed Systems

On the Interaction of Tiling and Automatic Parallelization

A novel compiler support for automatic parallelization on multicore systems

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