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通常需要大量工程投入。尽管已有若干工具可自动卸载BLAS和LAPACK等数值库，但受限于强制数据传输的高昂代价，这些工具在实践中往往缺乏实用性。NVIDIA Grace-Hopper采用的新型统一内存架构，使得CPU与GPU均可通过高带宽缓存一致性内存访问所有内存，有望消除传统架构中的瓶颈。这一突破为应用程序开发与移植策略开辟了新路径。本研究提出了一种新型自动BLAS卸载工具，该工具利用Grace-Hopper平台的高速缓存一致性NVLink C2C互连技术，无需修改代码或重新编译即可实现BLAS密集型应用的高性能GPU卸载。在量子化学与量子物理两类代码上的测试表明，该工具能带来显著的性能提升。