Modeling and optimizing NUMA effects and prefetching with machine learning

Both NUMA thread/data placement and hardware prefetcher configuration have significant impacts on HPC performance. Optimizing both together leads to a large and complex design space that has previously been impractical to explore at runtime. In this work we deliver the performance benefits of optimizing both NUMA thread/data placement and prefetcher configuration at runtime through careful modeling and online profiling. To address the large design space, we propose a prediction model that reduces the amount of input information needed and the complexity of the prediction required. We do so by selecting a subset of performance counters and application configurations that provide the richest profile information as inputs, and by limiting the output predictions to a subset of configurations that cover most of the performance. Our model is robust and can choose near-optimal NUMA+Pre-fetcher configurations for applications from only two profile runs. We further demonstrate how to profile online with low overhead, resulting in a technique that delivers an average of 1.68X performance improvement over a locality-optimized NUMA baseline with all prefetchers enabled.