Title: Heterogeneous computing on mobile GPU-FPGA cooperation platform

Authors: Nan Hu; Xuehai Zhou; Xi Li

Addresses: School of Computer Science, University of Science and Technology of China, Hefei, Anhui, China ' School of Computer Science, University of Science and Technology of China, Hefei, Anhui, China ' School of Computer Science, University of Science and Technology of China, Hefei, Anhui, China

Abstract: In recent years, mobile GPUs have been widely adopted in systems-on-chip (SoCs) platforms, especially in the graphics area. Meanwhile, reconfigurable processors and emerging FPGA computing devices are also widely used. However, the research of mobile GPU for general computing cooperation with FPGA, is still scarce. Such heterogeneous systems pose a great challenge to the parallel programming. In this paper, we present a flow-lead-in architecture (FLIA) is proposed as a unified data flow driven development model based on coupled GPU-FPGA. The servant represents an intermediate language module that is compiled from the high-level programming language and is compiled to different types of processors at runtime. Execution-flow abstracts the communication task between the servants and controls the pipeline execution for spatial parallelism. By scheduling multiple servants to heterogeneous processors, the cooperation system uses fewer resources to achieve near performance and power with the pure FPGA system.

Keywords: heterogeneous computing; GPU-FPGA cooperation; mobile GPU; ARM GPU FPGA partitioning; reconfigurable computing.

DOI: 10.1504/IJHPSA.2022.127758

International Journal of High Performance Systems Architecture, 2022 Vol.11 No.2, pp.73 - 84

Received: 21 Sep 2018
Accepted: 09 Jan 2019
Published online: 16 Dec 2022 *

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