By Hyesoon Kim
CUDA is the dominant language for parallel programming on GPU architecture, though other GPU programming languages, such as OpenCL and OpenMP exist. However, its use has been largely limited to NVIDIA devices, leading to recent efforts to enable its use on other platforms, such as CPUs and AMD/Intel GPUs. We aim to extend the reach of CUDA to RISC-V systems.
Hyesoon Kim is professor in the School of Computer Science at the Georgia Institute of Technology and a co-director of center for novel computing hierarchy. Her research areas include the intersection of computer architectures and compilers, with an emphasis on heterogeneous architectures, such as GPUs and near-data-processing. She is a recipient of NSF Career award and is a member of Micro Hall of Fame. She is the chair of IEEE TCuARCH. Her research has been recognized with a best paper award at PACT 2015. She is an associate editor of Transactions on Architecture and Code Optimization and IEEE-CAL.