Abstract
| Language | English |
|---|---|
| Journal | Concurrency and Computation: Practice and Experience |
| Volume | 27 |
| Early online date | 9 Feb 2015 |
| DOIs | |
| Publication status | Published - 2015 |
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Stepwise Refinement for Performance: a Methodology for Many-core Programming. / Hijma, P.; van Nieuwpoort, R.V.; Jacobs, C.J.H.; Bal, H.E.
In: Concurrency and Computation: Practice and Experience, Vol. 27, 2015.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - Stepwise Refinement for Performance: a Methodology for Many-core Programming
AU - Hijma, P.
AU - van Nieuwpoort, R.V.
AU - Jacobs, C.J.H.
AU - Bal, H.E.
PY - 2015
Y1 - 2015
N2 - Many-core hardware is targeted specifically at obtaining high performance, but reaching high performance is often challenging because hardware-specific details have to be taken into account. Although there are many programming systems that try to alleviate many-core programming, some providing a high-level language, others providing a low-level language for control, none of these systems have a clear and systematic methodology as a foundation. In this article, we propose stepwise-refinement for performance: a novel, clear, and structured methodology for obtaining high performance on many-cores. We present a system that supports this methodology, offers multiple levels of abstraction to provide programmers a trade-off between high-level and low-level programming, and provides programmers detailed performance feedback. We evaluate our methodology with several widely varying compute kernels on two different many-core architectures: a Graphical Processing Unit (GPU) and the Xeon Phi. We show that our methodology gives insight in the performance, and that in almost all cases, we gain a substantial performance improvement using our methodology.
AB - Many-core hardware is targeted specifically at obtaining high performance, but reaching high performance is often challenging because hardware-specific details have to be taken into account. Although there are many programming systems that try to alleviate many-core programming, some providing a high-level language, others providing a low-level language for control, none of these systems have a clear and systematic methodology as a foundation. In this article, we propose stepwise-refinement for performance: a novel, clear, and structured methodology for obtaining high performance on many-cores. We present a system that supports this methodology, offers multiple levels of abstraction to provide programmers a trade-off between high-level and low-level programming, and provides programmers detailed performance feedback. We evaluate our methodology with several widely varying compute kernels on two different many-core architectures: a Graphical Processing Unit (GPU) and the Xeon Phi. We show that our methodology gives insight in the performance, and that in almost all cases, we gain a substantial performance improvement using our methodology.
U2 - 10.1002/cpe.3416
DO - 10.1002/cpe.3416
M3 - Article
VL - 27
JO - Concurrency and Computation: Practice and Experience
T2 - Concurrency and Computation: Practice and Experience
JF - Concurrency and Computation: Practice and Experience
SN - 1532-0626
ER -