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.
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Exploring Portability and Performance of OpenCL FPGA Kernels on Intel HARPv2
FPGAs offer a heterogenous compute solution to the continuous de-
sire for increased performance by enabling the creation of application-
specific hardware that accelerates computation. While the barrier
to entry has historically been steep, advances in High Level Synthe-
sis (HLS) are making FPGAs more accessible. Specifically, the Intel
FPGA OpenCL SDK allows software designers to abstract away
low level details of architecting hardware on an FPGA and allows
them to author computational kernels in a higher level language.
Furthermore, Intel has developed a system that incorporates both a multicore Xeon CPU and Arria 10 FPGA into the same chip package as part of the Heterogeneous Accelerator Research Program (HARP) that can be targeted by their SDK.
In this work, we target the second iteration of the HARP platform (HARPv2) using HLS through porting of OpenCL kernels originally written for FPGAs connected via a PCIe bus. We evaluate the HARPv2 system’s performance against previously reported results, explore the portability of kernels through a hardware design space search, and empirically show the benefits of using the shared virtual memory (SVM) abstraction over explicit reads and writes.
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- PAR ID:
- 10108229
- Date Published:
- Journal Name:
- Proc. of 7th International Workshop on OpenCL
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3318170.3318180
