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Title: A Code Transformation to Improve the Efficiency of OpenCL Code on FPGA through Pipes
Over the past few years, there has been an increased interest in using FPGAs alongside CPUs and GPUs in high-performance computing systems and data centers. This trend has led to a push toward the use of high-level programming models and libraries, such as OpenCL, both to lower the barriers to the adoption of FPGAs by programmers unfamiliar with hardware description languages, and to allow to deploy a single code on different devices seamlessly. Today, both Intel and Xilinx offer toolchains to compile OpenCL code onto FPGA. However, using OpenCL on FPGAs is complicated by performance portability issues, since different devices have fundamental differences in architecture and nature of hardware parallelism they offer. Hence, platform-specific optimizations are crucial to achieving good performance across devices. In this paper, we propose a code transformation to improve the performance of OpenCL codes running on FPGA. The proposed method uses pipes to separate the memory accesses and core computation within OpenCL kernels. We analyze the benefits of the approach as well as the restrictions to its applicability. Using OpenCL applications from popular benchmark suites, we show that this code transformation can result in higher utilization of the global memory bandwidth available and increased instruction concurrency, thus improving the overall throughput of OpenCL kernels at the cost of a modest resource utilization overhead. Further concurrency can be achieved by using multiple memory and compute kernels.  more » « less
Award ID(s):
1812727
PAR ID:
10438459
Author(s) / Creator(s):
;
Date Published:
Journal Name:
CF '23: Proceedings of the 20th ACM International Conference on Computing Frontiers
Page Range / eLocation ID:
101 to 111
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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