High-Utilization GPGPU Design for Accelerating GEMM Workloads: An Incremental Approach

作者：Chongxi Wang, Penghao Song, Haoyu Zhao, Fuxin Zhang, Jian Wang, Longbing Zhang · 年份：2024 · DOI：10.1109/iscas58744.2024.10558334 · 被引用次数：3 · 研究领域：Parallel Computing and Optimization Techniques、Advanced Data Storage Technologies、Advancements in Photolithography Techniques

General Purpose Graphics Processing Units (GPGPUs) have been employed primarily in domains such as graphics acceleration and high-performance computing in the past. However, the rise of artificial intelligence (AI), particularly the computational demands associated with matrix multiplications in AI models, has presented formidable demands on the computational power of GPGPUs. Consequently, the design of matrix multiplication units within GPGPUs and ensuring their utilization have become key issues in optimizing AI workloads. This paper explores an incremental design approach, building upon a Single Instruction Multiple Threads (SIMT) GPGPU architecture, to facilitate General Matrix Multiply (GEMM) acceleration. This approach encompasses not only the design of matrix units within the stream processors but, more crucially, the optimization of the data path within the GPGPU to maximize the utilization of the matrix units. We present a practical demonstration of our approach through the fabrication of a GPGPU on a 12 nm CMOS process node, achieving a core clock speed of 1 GHz and INT8 peak performance of 8 TOPS with memory bandwidth limited to 32 GB/s LPDDR4-4000. Notably, this design results in only a 6.57% increase in chip area compared to the original GPGPU design. In a series of fair GEMM workload tests, the GPGPU implemented in this work outperforms the recent three generations of NVIDIA GPGPUs—V100, T4, and A100—in terms of matrix unit utilization.