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  1. IEEE International Workshop on Multi-/Many-core Computing Systems (MuCoCoS).
  2. 2013 IEEE 6th International Workshop on Multi-/Many-core Computing Systems (MuCoCoS)
  3. One OpenCL to rule them all?
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2013 IEEE 6th International Workshop on Multi-/Many-core Computing Systems (MuCoCoS)
Contents
Message from the MuCoCoS-2013 workshop chairs
Dataflow language compilation for a single chip massively parallel processor
Multiscale Dataflow Computing
Automatic extraction of multi-objective aware parallelism for heterogeneous MPSoCs
Optimizing sparse matrix vector multiplication on emerging multicores
Quantifying the performance impacts of using local memory for many-core processors
Topology-aware equipartitioning with coscheduling on multicore systems
One OpenCL to rule them all?
Algorithmic species revisited: A program code classification based on array references
Towards a compiler/runtime synergy to predict the scalability of parallel loops
ELB-trees an efficient and lock-free B-tree derivative

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One OpenCL to rule them all?

Content Provider IEEE Xplore Digital Library
Author Dolbeau, R. Bodin, F. de Verdiere, G.C.
Copyright Year 2013
Description Author affiliation: CAPS entreprise, Rennes, France (Dolbeau, R.) || IRISA, Rennes, France (Bodin, F.) || CEA, DAM, Bruyères-le-Châtel, France (de Verdiere, G.C.)
Abstract OpenCL is now available on a very large set of processors. This makes this language an attractive layer to address multiple targets with a single code base. The question on how sensitive to the underlying hardware is the OpenCL code in practice remains to be better understood. 1. This paper studies how realistic it is to use a unique OpenCL code for a set of hardware co-processors with different underlying micro-architectures. In this work, we target $Intel^{®}$ Xeon Phi™, $NVIDIA^{®}$ K20C and $AMD^{®}$ 7970™ GPU. All these accelerators provide at least support for OpenCL 1.1 and fit in the same high-end version of accelerator technology. To assess performance, we use OpenACC CAPS compiler to generate OpenCL code and use a moderately complex miniapplication, Hydro. This code uses 22 OpenCL kernels and was tailored to limit data transfers between the host and the accelerator device. To study how stable are the performance, we performed many experiments to determine the best OpenCL code for each hardware platform. This paper shows that, if well chosen, a single version of the code can be executed on multiple platforms without significant performance losses (less than 12%). This study confirms the need for auto-tuning technology to look for performance tradeoffs but also shows that deploying selftuning/adaptive code is not always necessary if the ultimate performance is not the goal.
Starting Page 1
Ending Page 6
File Size 274047
Page Count 6
File Format PDF
ISBN 9781479910106
DOI 10.1109/MuCoCoS.2013.6633603
Language English
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date 2013-09-07
Publisher Place UK
Access Restriction Subscribed
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword Performance evaluation Graphics processing units Computer architecture Hardware Portability Code tuning Registers Performance Kernel Tuning GPGPU
Content Type Text
Resource Type Article
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