High-Productivity Parallelism with Python Plus Packages (but without a Cluster)

We present two computing projects, peridynamics simulation and numerical integration on implicit domains, for which we realized high performance implementations using Python with appropriate packages. The problems are sufficiently compute-intensive that a straightforward serial implementation is prohibitively slow. While conventional wisdom suggests moving such problems onto a computing cluster, we very directly produced high-performance parallel implementations that effectively perform the computing tasks on a single GPU. For the peridynamics application, the only package needed in addition to Numpy is Numba whose just-in-time compiler allows us to write kernel functions in Python and compile them to run in parallel on a CUDA-enabled GPU. Our approach to numerical integration on implicit domains invokes two additional packages to support interval arithmetic and dynamic parallelism to enable tree-structured recursive refinement. Use of Python (with only kernels requiring dynamic parallelism written in C) enabled rapid development of concise code that successfully achieves significant performance enhancement