Performance Comparisons for Python Libraries  in Parallel Computing and Physical Simulation

Olubunmi Gregory Adekanmbi

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Conference: 2022 ASEE Gulf Southwest Annual Conference
Location: Prairie View, Texas
Publication Date: March 16, 2022
Start Date: March 16, 2022
End Date: March 18, 2022
Page Count: 10
DOI: 10.18260/1-2--39194
Permanent URL: https://peer.asee.org/39194
Download Count: 1093

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Olubunmi Gregory Adekanmbi Prairie View A&M University

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Abstract

Performance Comparisons for Python Libraries in Parallel Computing and Physical Simulation

Olubunmi Adekanmbi, Lei Huang Computer Science Department Prairie View A&M University Abstract

Physical simulation today requires fast and efficient parallel computing to achieve the accuracy and performance. At the core of physical simulation lies the tools and frameworks that are driving it, from programming language, compilation, algorithms and high-performance computing. Python is a high-level programming language of choice favored by many developers and researchers since it is very productive. However, Python is hard to achieve high performance due to its interpreted mode and its internal global interpreter lock (GIL).

There are different Python parallel libraries that are available today with the main aim of ensuring Python codes run faster in parallel to break the GIL, which is essential to promote Python as a high-performance programming language. With the new Python parallel libraries, physical simulations can be executed successfully on GPUs and multicores. Taichi, Numpy and Numba are Python libraries designed for high-performance numerical computing and machine learning. In this paper we introduce these Python libraries / frameworks and use them to implement several physical simulations. We evaluate the performance of these libraries and discuss the advantages and disadvantages in physical simulations.

To accomplish the research, we choose Taichi, Numpy and Numba to start with because they were specifically designed for high performance computing. For us to thoroughly compare these libraries we must first define sample physical simulations, understand the physics behind the simulations and implement them using these libraries i.e., have same simulation written with Taichi, Numpy and Numba. The simulations we are implementing is the N-body problem. The work will provide an informed decision on which of the libraries to adopt for both simulations and machine learning applications. We believe with these comparisons and having identified the advantages and disadvantages; we can proceed to creating functional programs/instructions.

Citation
Format

Adekanmbi, O. G. (2022, March), Performance Comparisons for Python Libraries in Parallel Computing and Physical Simulation Paper presented at 2022 ASEE Gulf Southwest Annual Conference, Prairie View, Texas. 10.18260/1-2--39194

TY - CPAPER
AB - Performance Comparisons for Python Libraries
in Parallel Computing and Physical Simulation

Olubunmi Adekanmbi, Lei Huang
Computer Science Department Prairie View A&amp;amp;M University

Abstract

Physical simulation today requires fast and efficient parallel computing to achieve the accuracy and performance. At the core of physical simulation lies the tools and frameworks that are driving it, from programming language, compilation, algorithms and high-performance computing. Python is a high-level programming language of choice favored by many developers and researchers since it is very productive. However, Python is hard to achieve high performance due to its interpreted mode and its internal global interpreter lock (GIL).

There are different Python parallel libraries that are available today with the main aim of ensuring Python codes run faster in parallel to break the GIL, which is essential to promote Python as a high-performance programming language. With the new Python parallel libraries, physical simulations can be executed successfully on GPUs and multicores. Taichi, Numpy and Numba are Python libraries designed for high-performance numerical computing and machine learning. In this paper we introduce these Python libraries / frameworks and use them to implement several physical simulations. We evaluate the performance of these libraries and discuss the advantages and disadvantages in physical simulations.

To accomplish the research, we choose Taichi, Numpy and Numba to start with because they were specifically designed for high performance computing. For us to thoroughly compare these libraries we must first define sample physical simulations, understand the physics behind the simulations and implement them using these libraries i.e., have same simulation written with Taichi, Numpy and Numba. The simulations we are implementing is the N-body problem. The work will provide an informed decision on which of the libraries to adopt for both simulations and machine learning applications. We believe with these comparisons and having identified the advantages and disadvantages; we can proceed to creating functional programs/instructions.

AU - Olubunmi Gregory Adekanmbi
CY - Prairie View, Texas
DA - 2022/03/16
PB - ASEE Conferences
TI - Performance Comparisons for Python Libraries in Parallel Computing and Physical Simulation
UR - https://peer.asee.org/39194
DO - 10.18260/1-2--39194
ER -