Elastic Places: An Adaptive Resource Manager for Scalable and Portable Performance
Artikel i vetenskaplig tidskrift, 2018
The diversity and complexity of modern computing platforms makes the development of high-performance software challenging. Designing scalable software requires tuning for a large set of resources, including cores (parallelism), memory bandwidths, and various levels of private and shared caches, as well as developing strategies for optimizing locality. But highly optimized implementations are often inefficient when executed on a different platform. This is the performance portability problem. One approach to scalability and portability is to tune the amount of work per task based on runtime overheads and concurrency. This results in a better balance between parallelism and scheduling overheads, but it can neither tune data reuse nor avoid inter-task interference. We propose a complementary approach that consists in tuning the amount of resources allocated to tasks and combine it with software-defined task topologies to provide portable locality. These ideas are combined into a low-overhead resource management scheme called Elastic Places. Elastic Places is implemented in the XiTAO software framework but the core ideas are equally applicable to other languages and runtimes. Experimental results on an AMD-based NUMA machine and an Intel Knights Landing system show that elastic places provides both high scalability and performance portability, with speed-ups of up to 2.3x on both platforms compared to state-of-the-art runtimes.