Optimizing Attention on GPUs by Exploiting GPU Architectural NUMA Effects
NeutralArtificial Intelligence
The article discusses the challenges posed by non-uniform memory access (NUMA) in large-scale attention workloads on disaggregated AI GPUs. It highlights how multi-chiplet designs lead to varying memory latency and bandwidth, which can negatively impact the performance of traditional GPU kernel scheduling strategies.
— Curated by the World Pulse Now AI Editorial System







