P_ARP_A: A Parallel Framework Simultaneously Using Heterogeneous Architecture for High Performance Computing

Hyojae Cho, Taehyun Han, Hyeonmyeong Lee, Heeseung Jo

http://doi.org/10.5626/JOK.2019.46.9.876

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.