Heterogeneous Task Scheduling for Accelerated OpenMP

Heterogeneous systems with CPUs and computational accelerators such as GPUs, FPGAs or the upcoming Intel MIC are becoming mainstream. In these systems, peak performance includes the performance of not just the CPUs but also all available accelerators. In spite of this fact, the majority of programmi...

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Bibliographic Details
Published in:2012 IEEE 26th International Parallel and Distributed Processing Symposium pp. 144 - 155
Main Authors: Scogland, T. R. W., Rountree, B., Wu-chun Feng, de Supinski, B. R.
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2012
Subjects:
Acceleration
Dynamic scheduling
GPGPU
Graphics processing unit
Integrated circuits
OpenMP
Programming
Programming models
Runtime
Schedules
ISBN:1467309753, 9781467309752
ISSN:1530-2075
Online Access:Get full text
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Summary:Heterogeneous systems with CPUs and computational accelerators such as GPUs, FPGAs or the upcoming Intel MIC are becoming mainstream. In these systems, peak performance includes the performance of not just the CPUs but also all available accelerators. In spite of this fact, the majority of programming models for heterogeneous computing focus on only one of these. With the development of Accelerated Open MP for GPUs, both from PGI and Cray, we have a clear path to extend traditional Open MP applications incrementally to use GPUs. The extensions are geared toward switching from CPU parallelism to GPU parallelism. However they do not preserve the former while adding the latter. Thus computational potential is wasted since either the CPU cores or the GPU cores are left idle. Our goal is to create a runtime system that can intelligently divide an accelerated Open MP region across all available resources automatically. This paper presents our proof-of-concept runtime system for dynamic task scheduling across CPUs and GPUs. Further, we motivate the addition of this system into the proposed Open MP for Accelerators standard. Finally, we show that this option can produce as much as a two-fold performance improvement over using either the CPU or GPU alone.
ISBN:1467309753
9781467309752
ISSN:1530-2075
DOI:10.1109/IPDPS.2012.23