On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

This paper presents software advances to easily exploit computer architectures consisting of a multi-core CPU and CPU+GPU to accelerate diverse types of high-performance computing (HPC) applications using a single code implementation. The paper describes and demonstrates the performance of the open-...

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Bibliographic Details
Published in:Information (Basel) 2024-11, Vol.15 (11), p.673
Main Authors: Morgan, Nathaniel, Yenusah, Caleb, Diaz, Adrian, Dunning, Daniel, Moore, Jacob, Heilman, Erin, Roth, Calvin, Lieberman, Evan, Walton, Steven, Brown, Sarah, Holladay, Daniel, Knezevic, Marko, Whetstone, Gavin, Baker, Zachary, Robey, Robert
Format: Article
Language:English
Subjects:
Application programming interface
C plus plus
C++ (programming language)
Central processing units
Codes
CPUs
Data structures
dense and sparse data
fine-grained parallelism
FORTRAN
Fourier transforms
GPUs
Graphics processing units
Libraries
Memory management
Modernization
Open source software
Physics
Portability
productivity
Programming languages
Project feasibility
Python
Software
Software development
Source code
Syntax
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Summary:This paper presents software advances to easily exploit computer architectures consisting of a multi-core CPU and CPU+GPU to accelerate diverse types of high-performance computing (HPC) applications using a single code implementation. The paper describes and demonstrates the performance of the open-source C++ matrix and array (MATAR) library that uniquely offers: (1) a straightforward syntax for programming productivity, (2) usable data structures for data-oriented programming (DOP) for performance, and (3) a simple interface to the open-source C++ Kokkos library for portability and memory management across CPUs and GPUs. The portability across architectures with a single code implementation is achieved by automatically switching between diverse fine-grained parallelism backends (e.g., CUDA, HIP, OpenMP, pthreads, etc.) at compile time. The MATAR library solves many longstanding challenges associated with easily writing software that can run in parallel on any computer architecture. This work benefits projects seeking to write new C++ codes while also addressing the challenges of quickly making existing Fortran codes performant and portable over modern computer architectures with minimal syntactical changes from Fortran to C++. We demonstrate the feasibility of readily writing new C++ codes and modernizing existing codes with MATAR to be performant, parallel, and portable across diverse computer architectures.
ISSN:2078-2489
2078-2489
DOI:10.3390/info15110673