Apollo: Reusable Models for Fast, Dynamic Tuning of Input-Dependent Code

Abstract

Increasing architectural diversity makes performance portability extremely important for parallel simulation codes. Emerging on-node parallelization frameworks such as Kokkos and RAJA decouple the work done in kernels from the parallelization mechanism, allowing for a single source kernel to be tuned for different architectures at compile time. However, computational demands in production applications change at runtime, and performance depends both on the architecture and the input problem, and tuning a kernel for one set of inputs may not improve its performance on another. The statically optimized versions need to be chosen dynamically to obtain the best performance. Existing auto-tuning approaches can handle slowly evolving applications effectively, but are too slow to tune highly input-dependent kernels. We developed Apollo, an auto-tuning extension for RAJA that uses pre-trained, reusable models to tune input-dependent code at runtime. Apollo is designed for highly dynamic applications; it generates sufficiently low-overhead code to tune parameters each time a kernel runs, making fast decisions. We apply Apollo to two hydrodynamics benchmarks and to a production multi-physics code, and show that it can achieve speedups from 1.2x to 4.8x.