Processing irregular codes containing arrays with multi-dimensional distributions by the PREPARE HPF compiler

Brezány, Peter; Sanjari, K.; Chéron, Olivier; Konijnenburg, Erik van

doi:10.1007/bfb0046677

Cited by 9 publications

(4 citation statements)

References 2 publications

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“…In the benchmark schedule reuse is prevented as to measure the basic performance of parallel loops with indirections. The PRE-HPF compiler uses a variant [10] of the standard inspector/executor scheme [11] as a basis. In addition, it uses a scheme of partial mask absorption as described in [1].…”

Section: Semi-regular Assignmentsmentioning

confidence: 99%

Finding performance bugs with the TNO HPF benchmark suite

Denissen¹,

Sips

2002

Concurrency and Computation

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SUMMARYHPF has been designed to provide portable performance on distributed memory machines. An important aspect of portable performance is the behavior of the available HPF compilers. Ideally, a programmer may expect comparable performance between different HPF compilers, given the same program and the same machine. To test the performance portability between compilers, we have designed a special benchmark suite, called the TNO HPF benchmark suite. It consists of a set of HPF programs, that test various aspects of efficient parallel code generation. The benchmark suite consists of a number of template programs that are used to generate test programs with different array sizes, alignments, distributions, and iteration spaces. It ranges from very simple assignments to more complex assignments like triangular iteration spaces, convex iteration spaces, coupled subscripts, and indirection arrays. We have run the TNO HPF benchmark suite on three compilers: the PREPARE prototype compiler, the PGI-HPF compiler, and the GMD Adaptor HPF compiler. Results show performance differences that can be quite large (up to two orders of magnitude for the same test program). Closer inspection reveals that the origin of most differences in performance is due to differences in local enumeration and storage of distributed array elements.

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Section: Semi-regular Assignmentsmentioning

confidence: 99%

Finding performance bugs with the TNO HPF benchmark suite

Denissen¹,

Sips

2002

Concurrency and Computation

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“…Such loop nests may access arrays that are distributed in more than one dimension and may contain conditional statements, reduction statements, and procedure calls. Previous work [9] in parallelizing irregular loops was based on linearization of arrays with multidimensional distributions. Our approach for handling arrays with multi-dimensional distributions is characterized by a dimension-wise processing of array subscripts and hence does not incorporate such aggressive program restructuring as caused by linearization.…”

Section: Parallelization Of Irregular Loopsmentioning

confidence: 99%

“…The PREPARE [9] compiler transforms a subset HPF-1 program into an SPMD message-passing program. Besides implementing a number of optimization techniques for regular codes, the system can handle limited forms of irregular loops based on the inspector/executor strategy.…”

Section: Related Workmentioning

confidence: 99%

VFC: The Vienna Fortran Compiler

Benkner

1999

Scientific Programming

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High Performance Fortran (HPF) offers an attractive high‐level language interface for programming scalable parallel architectures providing the user with directives for the specification of data distribution and delegating to the compiler the task of generating an explicitly parallel program. Available HPF compilers can handle regular codes quite efficiently, but dramatic performance losses may be encountered for applications which are based on highly irregular, dynamically changing data structures and access patterns. In this paper we introduce the Vienna Fortran Compiler (VFC), a new source‐to‐source parallelization system for HPF+, an optimized version of HPF, which addresses the requirements of irregular applications. In addition to extended data distribution and work distribution mechanisms, HPF+ provides the user with language features for specifying certain information that decisively influence a program’s performance. This comprises data locality assertions, non‐local access specifications and the possibility of reusing runtime‐generated communication schedules of irregular loops. Performance measurements of kernels from advanced applications demonstrate that with a high‐level data parallel language such as HPF+ a performance close to hand‐written message‐passing programs can be achieved even for highly irregular codes.

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“…These techniques automatically manage programmer-defined data distributions, partition loop iterations, remap data and generate optimized communication schedules. Most of these solutions are based on the inspector-executor paradigm [18,7].…”

Section: Introductionmentioning

confidence: 99%

HPF-2 Support for Dynamic Sparse Computations

Asenjo

Plata

Touriño

et al. 1999

Languages and Compilers for Parallel Computing

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Abstract. There is a class of sparse matrix computations, such as direct solvers of systems of linear equations, that change the fill-in (nonzero entries) of the coefficient matrix, and involve row and column operations (pivoting). This paper addresses the problem of the parallelization of these sparse computations from the point of view of the parallel language and the compiler. Dynamic data structures for sparse matrix storage are analyzed, permitting to efficiently deal with fill-in and pivoting issues. Any of the data representations considered enforces the handling of indirections for data accesses, pointer referencing and dynamic data creation. All of these elements go beyond current data-parallel compilation technology. We propose a small set of new extensions to HPF-2 to parallelize these codes, supporting part of the new capabilities on a runtime library. This approach has been evaluated on a Cray T3E, implementing, in particular, the sparse LU factorization.

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Processing irregular codes containing arrays with multi-dimensional distributions by the PREPARE HPF compiler

Cited by 9 publications

References 2 publications

Finding performance bugs with the TNO HPF benchmark suite

Finding performance bugs with the TNO HPF benchmark suite

VFC: The Vienna Fortran Compiler

HPF-2 Support for Dynamic Sparse Computations

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