StarPU-MPI: Task Programming over Clusters of Machines Enhanced with Accelerators

Augonnet, Cédric; Aumage, Olivier; Furmento, Nathalie; Namyst, Raymond; Thibault, Samuel

doi:10.1007/978-3-642-33518-1_40

Cited by 47 publications

(61 citation statements)

References 3 publications

(2 reference statements)

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“…However, there has also been some efforts to apply task-based approaches to clusters of computers. Cilk-NOW is a variant of Cilk for networks of workstations [8], StarPU-MPI is an extension of StarPU for clusters of acceleratorenhanced machines [9], and OmpSs has been implemented for clusters of GPUs as well [5]. The DAGuE framework [10] is an example of a task-based approach that achieves high performance in dense linear algebra operations.…”

Section: Parallel Programming Modelsmentioning

confidence: 99%

Chunks and Tasks: A programming model for parallelization of dynamic algorithms

Rubensson

Rudberg

2014

Parallel Computing

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We propose Chunks and Tasks, a parallel programming model built on abstractions for both data and work. The application programmer specifies how data and work can be split into smaller pieces, chunks and tasks, respectively. The Chunks and Tasks library maps the chunks and tasks to physical resources. In this way we seek to combine user friendliness with high performance. An application programmer can express a parallel algorithm using a few simple building blocks, defining data and work objects and their relationships. No explicit communication calls are needed; the distribution of both work and data is handled by the Chunks and Tasks library. This makes efficient implementation of complex applications that require dynamic distribution of work and data easier. At the same time, Chunks and Tasks imposes restrictions on data access and task dependencies that facilitates the development of high performance parallel back ends. We discuss the fundamental abstractions underlying the programming model, as well as performance and fault resilience considerations. We also present a pilot C++ library implementation for clusters of multicore machines and demonstrate its performance for sparse blocked matrix-matrix multiplication.Keywords: distributed memory parallelization, dynamic distribution, dynamic load balancing, fault tolerance, parallel programming model, work stealing Submitted manuscriptThis manuscript was submitted to Parallel Computing (Elsevier) for the special issue devoted to the PMAA 2012 conference.

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Section: Parallel Programming Modelsmentioning

confidence: 99%

Chunks and Tasks: A programming model for parallelization of dynamic algorithms

Rubensson

Rudberg

2014

Parallel Computing

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“…OMPSs may schedule program on a distributed memory architecture in the same fashion as our old Kaapi implementation [15]. StarPU [2] allows to mix dependent tasks with MPI commu- nication primitives. X-KAAPI provides a binary compatible libGOMP library called libKOMP [5].…”

Section: Overall Gains For Epxmentioning

confidence: 99%

X-kaapi: A Multi Paradigm Runtime for Multicore Architectures

Gautier

Lementec

Faucher

et al. 2013

2013 42nd International Conference on Parallel Processing

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Abstract-The paper presents X-KAAPI, a compact runtime for multicore architectures that brings multi parallel paradigms (parallel independent loops, fork-join tasks and dataflow tasks) in a unified framework without performance penalty. Comparisons on independent loops with OpenMP and on dense linear algebra with QUARK/PLASMA confirm our design decisions. Applied to EUROPLEXUS, an industrial simulation code for fast transient dynamics, we show that X-KAAPI achieves high speedups on multicore architectures by efficiently parallelizing both independent loops and dataflow tasks.

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“…Prior work on heterogeneous CPU/GPU systems has focused on new programming models and API extensions for supporting multiple heterogeneous devices [23,3,12], automating the mapping processor [5,15,16], enabling CPU and GPU sharing [20]. Different mapping heuristics have been designed and applied in these work.…”

Section: Introductionmentioning

confidence: 99%

Task mapping in heterogeneous embedded systems for fast completion time

Zhou

Liu

2014

Proceedings of the 14th International Conference on Embedded Software

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Graphics processing units are being widely used in embedded systems as they can achieve high performance and energy efficiency. In such systems, the problem of computation and data mapping for multiple applications while minimizing the completion time is quite challenging due to a large size of the policy space, including heterogeneous application characteristics, complex application structure, data communication costs, and data partitioning. To achieve fast competition time, a fine-grain mapping framework that explores a set of critical factors is needed for heterogeneous embedded systems. In this paper, we consider this mapping problem by presenting a theoretical framework that yields an optimal integer programming solution. Moreover, based upon several interesting measurements-based case studies, we design three practical mapping algorithms with low time complexity, each of which explores a specific set of factors that may affect the completion time performance. We evaluated the proposed algorithms by implementing them on a real heterogeneous system and using a large set of popular benchmarks for evaluation. Experimental results demonstrate that our proposed algorithms can achieve up to 30% faster completion time compared to the state-of-the-art mapping techniques, and can perform consistently well across different workloads.

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StarPU-MPI: Task Programming over Clusters of Machines Enhanced with Accelerators

Cited by 47 publications

References 3 publications

Chunks and Tasks: A programming model for parallelization of dynamic algorithms

Chunks and Tasks: A programming model for parallelization of dynamic algorithms

X-kaapi: A Multi Paradigm Runtime for Multicore Architectures

Task mapping in heterogeneous embedded systems for fast completion time

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