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Extending a Run-time Resource Management framework to support OpenCL and Heterogeneous Systems

Giuseppe Massari, Chiara Caffarri, Patrick Bellasi, William Fornaciari
DEIB – Politecnico di Milano, ITALY
Proceedings of Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and Design Tools and Architectures for Multicore Embedded Computing Platforms, 2014

@inproceedings{Massari:2014:ERR:2556863.2556868,

   author={Massari, Giuseppe and Caffarri, Chiara and Bellasi, Patrick and Fornaciari, William},

   title={Extending a Run-time Resource Management Framework to Support OpenCL and Heterogeneous Systems},

   booktitle={Proceedings of Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and Design Tools and Architectures for Multicore Embedded Computing Platforms},

   series={PARMA-DITAM ’14},

   year={2014},

   isbn={978-1-4503-2607-0},

   location={Vienna, Austria},

   pages={21:21–21:26},

   articleno={21},

   numpages={6},

   url={http://doi.acm.org/10.1145/2556863.2556868},

   doi={10.1145/2556863.2556868},

   acmid={2556868},

   publisher={ACM},

   address={New York, NY, USA},

   keywords={Graphical Processing Units (GPUs), Heterogeneous systems, Multicore, OpenCL, Parallel programming, Profiling, Runtime}

}

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From Mobile to High-Performance Computing (HPC) systems, performance and energy efficiency are becoming always more challenging requirements. In this regard, heterogeneous systems, made by a general-purpose processor and one or more hardware accelerators, are emerging as affordable solutions. However, the effective exploitation of such platforms requires specific programming languages, like for instance OpenCL, and suitable run-time software layers. This work illustrates the extension of a run-time resource management (RTRM) framework, to support the execution of OpenCL applications on systems featuring a multi-core CPU and multiple GPUs. Early results show how this solution leads to benefits both for the applications, in terms of performance, and for the system, in terms of resource utilization, i.e. load balancing and thermal leveling over the computing devices.
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