TY  - JOUR
T1  - P<SUB>AR</SUB>P<SUB>A</SUB>: A Parallel Framework Simultaneously Using Heterogeneous Architecture for High Performance Computing
AU  - Cho, Hyojae 
AU  - Han, Taehyun 
AU  - Lee, Hyeonmyeong 
AU  - Jo, Heeseung 
JO  - Journal of KIISE, JOK
PY  - 2019
DA  - 2019/1/14
DO  - 10.5626/JOK.2019.46.9.876
KW  - OpenCL
KW  - high performance computing
KW  - heterogeneous computing
KW  - parallel processing
AB  - 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.