MIMD Programs Execution Support on SIMD Machines: A Holistic Survey

The Single Instruction Multiple Data (SIMD) architecture, supported by various high-performance computing platforms, efficiently utilizes data-level parallelism. The SIMD model is used in traditional CPUs, dedicated vector systems, and accelerators such as GPUs, vector extensions, and Xeon Phi. It p...

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Published in:IEEE access Vol. 12; pp. 34354 - 34377
Main Authors: Mustafa, Dheya, Alkhasawneh, Ruba, Obeidat, Fadi, Shatnawi, Ahmed S.
Format: Journal Article
Language:English
Published: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accelerators
Application programming interface
asynchronous applications
Asynchronous communication
Central processing units
Computer architecture
CPUs
Data processing
GPUs
Graphics processing units
High performance computing
Instruction sets
irregular applications
MIMD (computers)
Optimization
Optimization techniques
Parallel processing
Platforms
Principles
Programming
Reduced instruction set computing
SIMD
SIMD (computers)
Single instruction multiple data
Surveys
Vectors
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Abstract The Single Instruction Multiple Data (SIMD) architecture, supported by various high-performance computing platforms, efficiently utilizes data-level parallelism. The SIMD model is used in traditional CPUs, dedicated vector systems, and accelerators such as GPUs, vector extensions, and Xeon Phi. It provides performance throughput in computation-intensive and data-parallel applications. Despite the similarity of data-processing principles between these architectures, porting various programming models between the reviewed platforms is challenging. Furthermore, enhancing the programmability of these architectures is an important feature for utilizing their emerging computing power and simplifying programming complexity. This paper reviews the basic principles of optimization techniques to run asynchronous Multiple Instruction Multiple Data (MIMD) on SIMD accelerators. It also surveys several GPU programming paradigms and application programming interfaces (APIs) and classifies these frameworks into different groups based on their criteria. In addition, a review of studies that performed a comparison of the collaborative execution of GPUs with CPUs and Xeon Phi is presented in this paper. This study will be beneficial for developers and researchers in the field of computer architecture and parallel computing of intensive scientific applications, specifically for early-stage high-performance computing researchers, to obtain a brief overview of performance optimization opportunities as well as the challenges of existing SIMD platforms.
AbstractList The Single Instruction Multiple Data (SIMD) architecture, supported by various high-performance computing platforms, efficiently utilizes data-level parallelism. The SIMD model is used in traditional CPUs, dedicated vector systems, and accelerators such as GPUs, vector extensions, and Xeon Phi. It provides performance throughput in computation-intensive and data-parallel applications. Despite the similarity of data-processing principles between these architectures, porting various programming models between the reviewed platforms is challenging. Furthermore, enhancing the programmability of these architectures is an important feature for utilizing their emerging computing power and simplifying programming complexity. This paper reviews the basic principles of optimization techniques to run asynchronous Multiple Instruction Multiple Data (MIMD) on SIMD accelerators. It also surveys several GPU programming paradigms and application programming interfaces (APIs) and classifies these frameworks into different groups based on their criteria. In addition, a review of studies that performed a comparison of the collaborative execution of GPUs with CPUs and Xeon Phi is presented in this paper. This study will be beneficial for developers and researchers in the field of computer architecture and parallel computing of intensive scientific applications, specifically for early-stage high-performance computing researchers, to obtain a brief overview of performance optimization opportunities as well as the challenges of existing SIMD platforms.
Author Alkhasawneh, Ruba
Shatnawi, Ahmed S.
Mustafa, Dheya
Obeidat, Fadi
Author_xml – sequence: 1
  givenname: Dheya
  orcidid: 0000-0003-1456-7377
  surname: Mustafa
  fullname: Mustafa, Dheya
  email: dheya@hu.edu.jo
  organization: Department of Computer Engineering, Faculty of Engineering, The Hashemite University, Zarqa, Jordan
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  givenname: Ruba
  orcidid: 0000-0001-9535-2612
  surname: Alkhasawneh
  fullname: Alkhasawneh, Ruba
  organization: Department of Communication and Computer Engineering, Faculty of Engineering, Al-Ahliyya Amman University, Amman, Jordan
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  givenname: Fadi
  orcidid: 0000-0002-8731-0989
  surname: Obeidat
  fullname: Obeidat, Fadi
  organization: Synopsys Inc., Austin, TX, USA
– sequence: 4
  givenname: Ahmed S.
  orcidid: 0000-0002-6239-3298
  surname: Shatnawi
  fullname: Shatnawi, Ahmed S.
  organization: Department of Software Engineering, Jordan University of Science and Technology, Irbid, Jordan
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Snippet The Single Instruction Multiple Data (SIMD) architecture, supported by various high-performance computing platforms, efficiently utilizes data-level...
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SubjectTerms Accelerators
Application programming interface
asynchronous applications
Asynchronous communication
Central processing units
Computer architecture
CPUs
Data processing
GPUs
Graphics processing units
High performance computing
Instruction sets
irregular applications
MIMD (computers)
Optimization
Optimization techniques
Parallel processing
Platforms
Principles
Programming
Reduced instruction set computing
SIMD
SIMD (computers)
Single instruction multiple data
Surveys
Vectors
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Title MIMD Programs Execution Support on SIMD Machines: A Holistic Survey
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