Abstract
The advent of heterogeneous computing has forced programmers to use platform specific programming paradigms in order to achieve maximum performance. This approach has a steep learning curve for programmers and also has detrimental influence on productivity and code re-usability. To help with this situation, OpenCL an open-source, parallel computing API for cross platform computations was conceived. OpenCL provides a homogeneous view of the computational resources (CPU and GPU) thereby enabling software portability across different platforms. Although OpenCL resolves software portability issues, the programming paradigm presents low programmability and additionally falls short in performance. In this paper we focus on integrating OpenCL framework with the OmpSs task based programming model using Nanos run time infrastructure to address these shortcomings. This would enable the programmer to skip cumbersome OpenCL constructs including OpenCL plaform creation, compilation, kernel building, kernel argument setting and memory transfers, instead write a sequential program with annotated pragmas. Our proposal mainly focuses on how to exploit the best of the underlying hardware platform with greater ease in programming and to gain significant performance using the data parallelism offered by the OpenCL run time for GPUs and multicore architectures. We have evaluated the platform with important benchmarks and have noticed substantial ease in programming with comparable performance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
OpenCL programming, http://www.khronos.org/registry/cl/specs/OpenCL-1.1.pdf
Duran, A., Ayguadé, E., Badia, R.M., et al.: OmpSs: a Proposal for Programming Heterogeneous Multi-Core Architectures. Parallel Processing Letters, 173–193 (2011)
Perez, J.M., Badia, R.M., Labarta, J.: Handling task dependencies under strided and aliased references. In: Proceeding ICS 2010 Proceedings of the 24th ACM International Conference on Supercomputing (2010)
CUDA Programming, http://developer.download.nvidia.com/compute/cuda/4_0/toolkit/docs/CUDA_C_programming_Guide.pdf
CELL Programming HandBook, https://www-01.ibm.com/chips/techlib/techlib.nsf/techdocs/1741C509C5F64B3300257460006FD68D/$file/CellBE_PXCell_Handbook_v1.11_12May08_pub.pdf
Parallel Program Visualization and Analysis Tool, http://www.bsc.es/media/1364.pdf
Ayguadé, E., Badia, R.M., Igual, F.D., Labarta, J., Mayo, R., Quintana-Ortí, E.S.: An Extension of the StarSs Programming Model for Platforms with Multiple GPUs. In: Sips, H., Epema, D., Lin, H.-X. (eds.) Euro-Par 2009. LNCS, vol. 5704, pp. 851–862. Springer, Heidelberg (2009)
Munshi, A., Gaster, B.R., Mattson, T.G., Fung, J., Ginsburg, D.: OpenCL Programming Guide, 1st edn. Addison-Wesley Professional (July 25, 2011) ISBN-10: 0321749642
Lee, J., et al.: An OpenCL framework for heterogeneous multicores with local memory. In: Proceedings of the 19th International Conference on Parallel Architectures and Compilation Techniques, PACT (2010)
Grewe, D., O’Boyle, M.F.P.: A Static Task Partitioning Approach for Heterogeneous Systems Using OpenCL. In: Knoop, J. (ed.) CC 2011. LNCS, vol. 6601, pp. 286–305. Springer, Heidelberg (2011)
Aoki, R., et al.: Hybrid OpenCL: Enhancing OpenCL for Distributed Processing. In: Parallel and Distributed Processing with Applications, ISPA (2011)
Gregg, C., et al.: Contention-Aware Scheduling of Parallel Code for Heterogeneous Systems. In: Poster at HotPar 2010 (2010)
http://software.intel.com/en-us/articles/opencl-device-fission-for-cpu-performance/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Elangovan, V.K., Badia, R.M., Parra, E.A. (2013). OmpSs-OpenCL Programming Model for Heterogeneous Systems. In: Kasahara, H., Kimura, K. (eds) Languages and Compilers for Parallel Computing. LCPC 2012. Lecture Notes in Computer Science, vol 7760. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37658-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-37658-0_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37657-3
Online ISBN: 978-3-642-37658-0
eBook Packages: Computer ScienceComputer Science (R0)