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：将代码移植至GPU通常需要大量工作。尽管存在多种用于自动卸载BLAS和LAPACK等数值计算库的工具，但由于强制数据传输的高昂代价，这些工具往往难以实际应用。NVIDIA Grace-Hopper平台的新型统一内存架构支持CPU和GPU以高带宽、缓存一致的方式访问全部内存，有望消除传统架构面临的瓶颈。这一突破为应用开发和移植策略开辟了新途径。本研究提出了一种新型自动BLAS卸载工具，该工具利用Grace-Hopper平台中高速缓存一致的NVLink C2C互连技术，无需代码修改或重新编译即可为BLAS密集型应用实现高性能GPU卸载。通过在两个量子化学/物理计算程序上的测试，观测到了显著的性能提升。