Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

arXiv:2405.08754v1 Announce Type: cross
Abstract: GPU-based heterogeneous architectures are now commonly used in HPC clusters. Due to their architectural simplicity specialized for data-level parallelism, GPUs can offer much higher computational throughput and memory bandwidth than CPUs in the same generation do. However, as the available resources in GPUs have increased exponentially over the past decades, it has become increasingly difficult for a single program to fully utilize them. As a consequence, the industry has started supporting several resource partitioning features …

abstract architectures arxiv bandwidth computational cpus cs.ar cs.dc cs.lg data gpu gpus hierarchical however hpc memory modern partitioning reinforcement reinforcement learning resources simplicity type