SWOT Analysis of Parallel Processing APIs - CUDA, OpenCL, OpenMP and MPI and their Usage in Various Companies

Abstract

Purpose: Parallel Processing is the technique of achieving High Performance Computing (HPC) with parallel execution of programs that are synchronized during the execution time. This research paper studied the companies which use Parallel Processing techniques in their projects and products along with the identification of major Application Program Interfaces (APIs) that are used to achieve parallelism. The major aim of this research work is to perform the SWOT analysis of Parallel Processing APIs to identify the importance of each one from the company perspective. Design/Methodology/Approach: The research method adopted to accomplish the SWOT Analysis of Parallel Processing APIs - CUDA, OpenCL, OpenMP and MPI and their Usage in Various Companies is qualitative and exploratory in nature. Systematic literature review of different companies that use Parallel Processing techniques to build and develop parallel programs is done during this research work. Findings/Results: Parallel Processing constructs can be used to solve various problems in the six major application domains as: - Computational Finance & Business Economics, Artificial Intelligence, Machine Learning (ML), Data Science, Numerical Analysis and Design of Algorithms. Major Parallel Processing APIs used in companies are: - CUDA, OpenCL, OpenMP and MPI for implementing the problems with parallel execution. Foremost companies that use Parallel Processing APIs are studied and various applications, systems, models, and projects that are developed using Parallel Processing techniques are listed. SWOT Analysis is performed on all four Parallel Processing APIs and its SWOT(Strengths-Weaknesses-Opportunities-Threats) are identified. Originality/Value: Listing of SWOT Analysis (Strengths-Weaknesses-Opportunities-Threats) of Parallel Processing APIs - CUDA, OpenCL, OpenMP and MPI. Paper Type: Company Analysis research paper