SPMPool

Abstract

Distributed Scratchpad Memories (SPMs) in embedded many-core systems require careful selection of data placement to achieve good performance. Applications mapped to these platforms have varying memory requirements based on their runtime behavior, resulting in under- or overutilization of the local SPMs. We propose SPMPool to share the available on-chip SPMs on many-cores among concurrently executing applications in order to reduce the overall memory access latency. By pooling SPM resources, we can assign underutilized memory resources, due to idle cores or low memory usage, to applications dynamically. SPMPool is the first workload-aware SPM mapping solution for many-cores that dynamically allocates data at runtime—using profiled data—to address the unpredictable set of concurrently executing applications. Our experiments on workloads with varying interapplication memory intensity show that SPMPool can achieve up to 76% reduction in memory access latency for configurations ranging from 16 to 256 cores, compared to the traditional approach that limits executing cores to use their local SPMs.