Deep Dive - CRI- RM Based CPU and NUMA Affinity to Achieve AI Task Acceleration

Deep Dive - CRI- RM Based CPU and NUMA Affinity to Achieve AI Task Acceleration

Explore a deep dive into CRI-RM based CPU and NUMA affinity for accelerating AI tasks in this conference talk. Learn how integrating CRI-RM components can enhance resource allocation within Kubernetes nodes, potentially improving AI task performance by over 50%. Discover the limitations of current CPU and NUMA features in Kubernetes and how CRI-RM addresses these issues. Gain insights into CPU-based AI task acceleration schemes, topology-aware resource alignment, and the advantages of using CRI-RM for customized development in both newer and older Kubernetes versions. Examine test cases using ResNet50 and CNN models to understand the practical applications and benefits of this approach in AI training clusters.