Supporting CUDA for an extended RISC-V GPU architecture

Ruobing Han, Blaise Tine, Jaewon Lee, Jaewoong Sim, Hyesoon Kim
Georgia Institute of Technology, USA
arXiv:2109.00673 [cs.PL], (2 Sep 2021)


   title={Supporting CUDA for an extended RISC-V GPU architecture},

   author={Ruobing Han and Blaise Tine and Jaewon Lee and Jaewoong Sim and Hyesoon Kim},






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With the rapid development of scientific computation, more and more researchers and developers are committed to implementing various workloads/operations on different devices. Among all these devices, NVIDIA GPU is the most popular choice due to its comprehensive documentation and excellent development tools. As a result, there are abundant resources for hand-writing high-performance CUDA codes. However, CUDA is mainly supported by only commercial products and there has been no support for open-source H/W platforms. RISC-V is the most popular choice for hardware ISA, thanks to its elegant design and open-source license. In this project, we aim to utilize these existing CUDA codes with RISC-V devices. More specifically, we design and implement a pipeline that can execute CUDA source code on an RISC-V GPU architecture. We have succeeded in executing CUDA kernels with several important features, like multi-thread and atomic instructions, on an RISC-V GPU architecture.
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