Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM
英文摘要：
We implemented and optimized matrix multiplications between dense and block-sparse matrices on CUDA. We leveraged TVM, a deep learning compiler, to explore the schedule space of the operation and generate efficient CUDA code. With the automatic parameter tuning in TVM, our cross-thread reduction based implementation achieved competitive or better performance compared with other state-of-the-art frameworks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
