Auto-parallelisation of Sieve C++ Programs

Donaldson, Alastair F.; Riley, Colin; Lokhmotov, Anton; Cook, A.

doi:10.1007/978-3-540-78474-6_5

Cited by 6 publications

(3 citation statements)

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“…Currently, there is a number of parallel programming models for Cell such as OpenMP, Sieve C++ and Offload. These models offer semi‐automatic parallelisation environments because the user is required to identify possible parallelism . Recent releases of the GNU tool chain and IBM XL offer compilers for C/C++ and FORTRAN on both architectures and support OpenMP for Linux platforms.…”

Section: Hardware Usedmentioning

confidence: 99%

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Array languages and the N‐body problem

Cockshott

Gdura

Keir

2013

Concurrency and Computation

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SUMMARYThis paper is a description of the contributions to the SICSA multicore challenge on many body planetary simulation made by a compiler group at the University of Glasgow. Our group is part of the Computer Vision and Graphics research group and we have for some years been developing array compilers because we think these are a good tool both for expressing graphics algorithms and for exploiting the parallelism that computer vision applications require. We shall describe experiments using two languages on two dierent platforms and we shall compare the performance of these with reference C implementations running on the same platforms. Finally we shall draw conclusions both about the viability of the array language approach as compared to other approaches used in the challenge and also about the strengths and weaknesses of the two, very dierent, processor architectures we used. Copyright © 0000 John Wiley & Sons, Ltd.Received . . . ARRAY LANGUAGESBy the term array language, we mean a programming language that allows array values to be operated on as a whole: passed to functions as a whole, returned from functions as a whole, subjected to arithmetic operations as a whole, and, in the case of an imperative language, assigned as a whole.

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Section: Hardware Usedmentioning

confidence: 99%

“…These models oer semi-automatic parallelisation environments because the user is required to identify possible parallelism [23,25,26]. Recent releases of the GNU tool chain and IBM XL oer compilers for C/C++ and Fortran on both architectures and support OpenMP for Linux platforms.…”

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confidence: 99%

Array languages and the N‐body problem

Cockshott

Gdura

Keir

2013

Concurrency and Computation

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“…One of the most widely available types of heterogeneous processing resource is the Graphics Processing Unit (GPU) present in most commodity computer systems. As GPUs have become more capable, a number of systems, such as Cuda [18], Sieve C++ [7] and OpenCL [14], have been developed to enable general purpose applications to exploit a GPUs potential processing performance. While these systems abstract many of the difficulties involved in writing general purpose code for GPUs, they are relatively inflexible; a program's threads of execution cannot easily be Figure 14.…”

Section: Related Workmentioning

confidence: 99%

Hera-JVM

McIlroy

Sventek

2010

Proceedings of the ACM International Conference on Object Oriented Programming Systems Languages and Applications

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Heterogeneous multi-core processors, such as the IBM Cell processor, can deliver high performance. However, these processors are notoriously difficult to program: different cores support different instruction set architectures, and the processor as a whole does not provide coherence between the different cores' local memories.We present Hera-JVM, an implementation of the Java Virtual Machine which operates over the Cell processor, thereby making this platforms more readily accessible to mainstream developers. Hera-JVM supports the full Java language; threads from an unmodified Java application can be simultaneously executed on both the main PowerPCbased core and on the additional SPE accelerator cores. Migration of threads between these cores is transparent from the point of view of the application, requiring no modification to Java source code or bytecode. Hera-JVM supports the existing Java Memory Model, even though the underlying hardware does not provide cache coherence between the different core types.We examine Hera-JVM's performance under a series of real-world Java benchmarks from the SpecJVM, Java Grande and Dacapo benchmark suites. These benchmarks show a wide variation in relative performance on the different core types of the Cell processor, depending upon the nature of their workload. Execution of these benchmarks on Hera-JVM can achieve speedups of up to 2.25x by using one of the Cell processor's SPE accelerator cores, compared to execution on the main PowerPC-based core. When all six SPE cores are exploited, parallel workloads can achieve speedups of up to 13x compared to execution on the single PowerPC core.

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