Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

Marcin Bogdański; Peter R. Lewis; Tobias Becker; Xin Yao

doi:10.1109/CISIS.2011.81

Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

Marcin Bogdański^*, Peter R. Lewis, Tobias Becker, Xin Yao

^*Corresponding author for this work

Computer Science Research Group

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.

Original language	English
Title of host publication	Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011
Publisher	IEEE
Pages	496-501
Number of pages	6
ISBN (Electronic)	978-0-7695-4373-4
ISBN (Print)	978-1-61284-709-2
DOIs	https://doi.org/10.1109/CISIS.2011.81
Publication status	Published - 2011
Event	5th International Conference on Complex, Intelligent and Software Intensive Systems - Seoul, Korea, Democratic People's Republic of Duration: 30 Jun 2011 → 2 Jul 2011

Conference

Conference	5th International Conference on Complex, Intelligent and Software Intensive Systems
Abbreviated title	CISIS 2011
Country/Territory	Korea, Democratic People's Republic of
City	Seoul
Period	30/06/11 → 2/07/11

Keywords

Artificial Neural Network
Dynamic Optimisation
FPGA
Genetic Algorithm
GPU
Heterogeneous System
On-Line Learning
Scheduling

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10.1109/CISIS.2011.81

Cite this

@inproceedings{e7421d5512254bccb782abb5ac87918e,

title = "Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations",

abstract = "Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.",

keywords = "Artificial Neural Network, Dynamic Optimisation, FPGA, Genetic Algorithm, GPU, Heterogeneous System, On-Line Learning, Scheduling",

author = "Marcin Bogda{\'n}ski and Lewis, {Peter R.} and Tobias Becker and Xin Yao",

year = "2011",

doi = "10.1109/CISIS.2011.81",

language = "English",

isbn = "978-1-61284-709-2",

pages = "496--501",

booktitle = "Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011",

publisher = "IEEE",

address = "United States",

note = "5th International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011 ; Conference date: 30-06-2011 Through 02-07-2011",

}

Bogdański, M, Lewis, PR, Becker, T & Yao, X 2011, Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations. in Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011. IEEE, pp. 496-501, 5th International Conference on Complex, Intelligent and Software Intensive Systems, Seoul, Korea, Democratic People's Republic of, 30/06/11. https://doi.org/10.1109/CISIS.2011.81

Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations. / Bogdański, Marcin; Lewis, Peter R.; Becker, Tobias et al.
Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011. IEEE, 2011. p. 496-501.

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

AU - Bogdański, Marcin

AU - Lewis, Peter R.

AU - Becker, Tobias

AU - Yao, Xin

PY - 2011

Y1 - 2011

N2 - Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.

AB - Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.

KW - Artificial Neural Network

KW - Dynamic Optimisation

KW - FPGA

KW - Genetic Algorithm

KW - GPU

KW - Heterogeneous System

KW - On-Line Learning

KW - Scheduling

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ER -

Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

Abstract

Conference

Keywords

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