Gillespie’s Stochastic Simulation Algorithm on MIC coprocessors

To investigate the behavior of biochemical systems, many runs of Gillespie’s Stochastic Simulation Algorithm (SSA) are generally needed, causing excessive computational costs on Central Processing Units (CPUs). Since all SSA runs are independent, the Intel Xeon Phi coprocessors based on the Many Int...

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Published in:	The Journal of supercomputing Vol. 73; no. 2; pp. 676 - 686
Main Authors:	Tangherloni, Andrea, Nobile, Marco S., Cazzaniga, Paolo, Besozzi, Daniela, Mauri, Giancarlo
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2017 Springer Nature B.V
Subjects:	Central processing units Compilers Computer Science Computer simulation Coprocessors CPUs Graphics processing units Interpreters Processor Architectures Programming Languages Redesign Stochastic Simulation Algorithm High-Performance Computing Parallel computing Stochastic modeling GPU MIC
ISSN:	0920-8542, 1573-0484
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Abstract	To investigate the behavior of biochemical systems, many runs of Gillespie’s Stochastic Simulation Algorithm (SSA) are generally needed, causing excessive computational costs on Central Processing Units (CPUs). Since all SSA runs are independent, the Intel Xeon Phi coprocessors based on the Many Integrated Core (MIC) architecture can be exploited to distribute the workload. We considered two execution modalities on MIC: one consisted in running exactly the same CPU code of SSA, while the other exploited MIC’s vector instructions to reuse the CPU code with only few modifications. MIC performance was compared with Graphics Processing Units (GPUs), specifically implemented in CUDA to optimize the use of memory hierarchy. Our results show that GPU largely outperforms MIC and CPU, but required a complete redesign of SSA. MIC allows a relevant speedup, especially when vector instructions are used, with the additional advantage of requiring minimal modifications to CPU code.
AbstractList	To investigate the behavior of biochemical systems, many runs of Gillespie’s Stochastic Simulation Algorithm (SSA) are generally needed, causing excessive computational costs on Central Processing Units (CPUs). Since all SSA runs are independent, the Intel Xeon Phi coprocessors based on the Many Integrated Core (MIC) architecture can be exploited to distribute the workload. We considered two execution modalities on MIC: one consisted in running exactly the same CPU code of SSA, while the other exploited MIC’s vector instructions to reuse the CPU code with only few modifications. MIC performance was compared with Graphics Processing Units (GPUs), specifically implemented in CUDA to optimize the use of memory hierarchy. Our results show that GPU largely outperforms MIC and CPU, but required a complete redesign of SSA. MIC allows a relevant speedup, especially when vector instructions are used, with the additional advantage of requiring minimal modifications to CPU code.
Author	Cazzaniga, Paolo Besozzi, Daniela Nobile, Marco S. Mauri, Giancarlo Tangherloni, Andrea
Author_xml	– sequence: 1 givenname: Andrea surname: Tangherloni fullname: Tangherloni, Andrea email: tangherloni@disco.unimib.it organization: Department of Informatics, Systems and Communication, University of Milano-Bicocca – sequence: 2 givenname: Marco S. surname: Nobile fullname: Nobile, Marco S. organization: Department of Informatics, Systems and Communication, University of Milano-Bicocca, SYSBIO.IT Centre of Systems Biology – sequence: 3 givenname: Paolo surname: Cazzaniga fullname: Cazzaniga, Paolo organization: Department of Human and Social Sciences, University of Bergamo, SYSBIO.IT Centre of Systems Biology – sequence: 4 givenname: Daniela surname: Besozzi fullname: Besozzi, Daniela organization: Department of Informatics, Systems and Communication, University of Milano-Bicocca, SYSBIO.IT Centre of Systems Biology – sequence: 5 givenname: Giancarlo surname: Mauri fullname: Mauri, Giancarlo organization: Department of Informatics, Systems and Communication, University of Milano-Bicocca, SYSBIO.IT Centre of Systems Biology
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SubjectTerms	Central processing units Compilers Computer Science Computer simulation Coprocessors CPUs Graphics processing units Interpreters Processor Architectures Programming Languages Redesign
Title	Gillespie’s Stochastic Simulation Algorithm on MIC coprocessors
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