@article{MD3FA38B6,
title = "P<SUB>AR</SUB>P<SUB>A</SUB>: A Parallel Framework Simultaneously Using Heterogeneous Architecture for High Performance Computing",
journal = "Journal of KIISE, JOK",
year = "2019",
issn = "2383-630X",
doi = "10.5626/JOK.2019.46.9.876",
author = "Hyojae Cho,Taehyun Han,Hyeonmyeong Lee,Heeseung Jo",
keywords = "OpenCL,high performance computing,heterogeneous computing,parallel processing",
abstract = "With the substantial performance improvements achieved in GPU, they have come to be commonly used not only in computer graphics but also in high performance computing. Simply using a CPU and a GPU concurrently is not difficult. However, distributing works and adjusting the computing ratio among these heterogeneous processors are challenging issues. We propose a novel framework in this paper, named PARPA, which automatically distributes and processes tasks to a CPU and a GPU. PARPA can maximize computation performance by using a CPU and a GPU simultaneously. The load balancing between them can be performed dynamically based on their usage and features. The evaluation results indicate that PARPA shows 3.48 times better performance."
}