MKPipe: A Compiler Framework for Optimizing Multi-Kernel Workloads in OpenCL for FPGA

TL;DR

MKPipe is a compiler framework that optimizes multi-kernel workloads in OpenCL for FPGA, achieving significant speedups by employing novel pipeline schemes, remapping techniques, and resource balancing algorithms.

Contribution

The paper introduces MKPipe, a source-to-source compiler framework with new multi-kernel pipeline schemes and optimization algorithms for FPGA-based OpenCL workloads.

Findings

Achieves up to 3.6x speedup over baseline

Employs novel workitem/workgroup-id remapping for efficiency

Balances throughput and resources for optimized multi-kernel execution

Abstract

OpenCL for FPGA enables developers to design FPGAs using a programming model similar for processors. Recent works have shown that code optimization at the OpenCL level is important to achieve high computational efficiency. However, existing works either focus primarily on optimizing single kernels or solely depend on channels to design multi-kernel pipelines. In this paper, we propose a source-to-source compiler framework, MKPipe, for optimizing multi-kernel workloads in OpenCL for FPGA. Besides channels, we propose new schemes to enable multi-kernel pipelines. Our optimizing compiler employs a systematic approach to explore the tradeoffs of these optimizations methods. To enable more efficient overlapping between kernel execution, we also propose a novel workitem/workgroup-id remapping technique. Furthermore, we propose new algorithms for throughput balancing and resource balancing to tune the optimizations upon individual kernels in the multi-kernel workloads. Our results show that our compiler-optimized multi-kernels achieve up to 3.6x (1.4x on average) speedup over the baseline, in which the kernels have already been optimized individually.