Porting codes to GPU often requires major efforts. While several tools exist for automatically offload numerical libraries such as BLAS and LAPACK, they often prove impractical due to the high cost of mandatory data transfer. The new unified memory architecture in NVIDIA Grace-Hopper allows high bandwidth cache-coherent memory access of all memory from both CPU and GPU, potentially eliminating bottleneck faced in conventional architecture. This breakthrough opens up new avenues for application development and porting strategies. In this study, we introduce a new tool for automatic BLAS offload, the tool leverages the high speed cache coherent NVLink C2C interconnect in Grace-Hopper, and enables performant GPU offload for BLAS heavy applications with no code changes or recompilation. The tool was tested on two quantum chemistry or physics codes, great performance benefits were observed.

翻译：将代码移植到GPU通常需要大量工作。尽管已有多种工具可自动卸载数值库（如BLAS和LAPACK），但由于强制数据传输的高昂成本，这些工具往往难以实际应用。NVIDIA Grace-Hopper的新型统一内存架构支持CPU和GPU对所有内存进行高带宽缓存一致性访问，可能消除传统架构中的瓶颈。这一突破为应用开发和移植策略开辟了新途径。在本研究中，我们介绍了一种新的自动BLAS卸载工具。该工具利用Grace-Hopper的高速缓存一致性NVLink C2C互连，无需修改代码或重新编译即可为重度依赖BLAS的应用实现高性能GPU卸载。该工具已在两个量子化学或物理代码上进行了测试，并观察到显著的性能提升。