A scheduler to foster data locality for GPU and out-of-core task-based linear algebra applications

Hardware accelerators like GPUs now provide a large part of the computational power used for scientific simulations. Despite their efficacy, GPUs possess limited memory and are connected to the main memory of the machine via a bandwidth limited bus. Scientific simulations often operate on very large data, that surpasses the GPU's memory capacity. Therefore, one has to turn to out-of-core computing: data is kept in a remote, slower memory (CPU memory), and moved back and forth from/to the device memory (GPU memory), a process also present for multicore CPUs with limited memory. In both cases, data movement quickly becomes a performance bottleneck. Task-based runtime schedulers have emerged as a convenient and efficient way to manage large applications on such heterogeneous platforms. We propose a scheduler for task-based runtimes that improves data locality for out-of-core linear algebra computations, to reduce data movement. We design a data-aware strategy for both task scheduling and data eviction from limited memories. We compare this scheduler to existing schedulers in runtime systems. Using StarPU, we show that our new scheduling strategy achieves comparable performance when memory is not a constraint, and significantly better performance when application input data exceeds memory, on both GPUs and CPU cores.