Meta-Programming Design-Flow Patterns for Automating Reusable Optimisations

Continuing advances in heterogeneous and parallel computing enable massive performance gains in domains such as AI and HPC. Such gains often involve using hardware accelerators, such as FPGAs and GPUs, to speed up specific workloads. However, to make effective use of emerging heterogeneous architectures, optimisation is typically done manually by highly-skilled developers with in-depth understanding of the target hardware. The process is tedious, error-prone, and must be repeated for each new application. This paper introduces Design-Flow Patterns, which capture modular, recurring application-agnostic elements involved in mapping and optimising application descriptions onto efficient CPU and GPU targets. Our approach is the first to codify and programmatically coordinate these elements into fully automated, customisable, and reusable end-to-end design-flows. We implement key design-flow patterns using the meta-programming tool Artisan, and evaluate automated design-flows applied to three sequential C++ applications. Compared to single-threaded implementations, our approach generates multi-threaded OpenMP CPU designs achieving up to 18 times speedup on a CPU platform with 32-threads, as well as HIP GPU designs achieving up to 1184 times speedup on an NVIDIA GeForce RTX 2080 Ti GPU.