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A domain specific approach to high performance heterogeneous computing

A domain specific approach to high performance heterogeneous computing
A domain specific approach to high performance heterogeneous computing

Users of heterogeneous computing systems face two problems: first, in understanding the trade-off relationships between the observable characteristics of their applications, such as latency and quality of the result, and second, how to exploit knowledge of these characteristics to allocate work to distributed computing platforms efficiently. A domain specific approach addresses both of these problems. By considering a subset of operations or functions, models of the observable characteristics or domain metrics may be formulated in advance, and populated at run-time for task instances. These metric models can then be used to express the allocation of work as a constrained integer program. These claims are illustrated using the domain of derivatives pricing in computational finance, with the domain metrics of workload latency and pricing accuracy. For a large, varied workload of 128 Black-Scholes and Heston model-based option pricing tasks, running upon a diverse array of 16 Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both the makespan and accuracy are generally within 10 percent of the run-time performance. When these models are used as inputs to machine learning and MILP-based workload allocation approaches, a latency improvement of up to 24 and 270 times over the heuristic approach is seen.

Distributed computing, programming environments, accelerator architectures, high performance computing, application software
1045-9219
2-15
Inggs, Gordon
30d77ff5-5dce-4356-8e04-d132e0cb9161
Thomas, David B.
5701997d-7de3-4e57-a802-ea2bd3e6ab6c
Luk, Wayne
ea937a29-564d-4b87-8570-a2c284f956c6
Inggs, Gordon
30d77ff5-5dce-4356-8e04-d132e0cb9161
Thomas, David B.
5701997d-7de3-4e57-a802-ea2bd3e6ab6c
Luk, Wayne
ea937a29-564d-4b87-8570-a2c284f956c6

Inggs, Gordon, Thomas, David B. and Luk, Wayne (2017) A domain specific approach to high performance heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems, 28 (1), 2-15, [7465804]. (doi:10.1109/TPDS.2016.2563427).

Record type: Article

Abstract

Users of heterogeneous computing systems face two problems: first, in understanding the trade-off relationships between the observable characteristics of their applications, such as latency and quality of the result, and second, how to exploit knowledge of these characteristics to allocate work to distributed computing platforms efficiently. A domain specific approach addresses both of these problems. By considering a subset of operations or functions, models of the observable characteristics or domain metrics may be formulated in advance, and populated at run-time for task instances. These metric models can then be used to express the allocation of work as a constrained integer program. These claims are illustrated using the domain of derivatives pricing in computational finance, with the domain metrics of workload latency and pricing accuracy. For a large, varied workload of 128 Black-Scholes and Heston model-based option pricing tasks, running upon a diverse array of 16 Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both the makespan and accuracy are generally within 10 percent of the run-time performance. When these models are used as inputs to machine learning and MILP-based workload allocation approaches, a latency improvement of up to 24 and 270 times over the heuristic approach is seen.

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More information

Published date: 1 January 2017
Additional Information: Publisher Copyright: © 1990-2012 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
Keywords: Distributed computing, programming environments, accelerator architectures, high performance computing, application software

Identifiers

Local EPrints ID: 453685
URI: http://eprints.soton.ac.uk/id/eprint/453685
ISSN: 1045-9219
PURE UUID: 8642985e-2fb3-432f-9c83-98dfd07187d6
ORCID for David B. Thomas: ORCID iD orcid.org/0000-0002-9671-0917

Catalogue record

Date deposited: 20 Jan 2022 17:45
Last modified: 10 Jun 2022 01:57

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Contributors

Author: Gordon Inggs
Author: David B. Thomas ORCID iD
Author: Wayne Luk

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