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High Performance OpenCL-Based GEMM Kernel Auto-Tuned by Bayesian Optimization
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DOI：10.1109/TPDS.2025.3587673.png)
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Abstract
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OpenCL has become the favored framework for emerging heterogeneous devices and FPGAs, owing to its versatility and portability. However, OpenCL-based math libraries still face challenges in fully leveraging device performance. When deploying high-performance arithmetic applications on these devices, the most important hot function is General Matrix-matrix Multiplication (GEMM). This study presents a meticulously optimized OpenCL GEMM kernel. Our enhanced GEMM kernel emphasizes two key improvements: 1) a three-level double buffer pipeline that efficiently overlaps data fetching with floating-point computations; 2) a fine-grained prefetching strategy of private memory to increase device occupancy by optimizing register unit utilization. Furthermore, this work presents a Bayesian Optimization (BO) tuner for kernel auto-tuning. Experimental results demonstrate considerable optimization improvement and performance advantages achieved on diverse OpenCL devices. Additionally, the BO tuner demonstrates superior efficiency and robustness, outperforming contemporary tuning methods.
Keywords:
OpenCL
GEMM
auto-tuning
Bayesian optimization
high-performance computing
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