Benchmarking the Parallel 1D Heat Equation Solver in Chapel, Charm++, C++, HPX, Go, Julia, Python, Rust, Swift, and Java

Abstract

Many scientific high performance codes that simulate e.g. black holes, coastal waves, climate and weather, etc. rely on block-structured meshes and use finite differencing methods to iteratively solve the appropriate systems of differential equations. In this paper we investigate implementations of an extremely simple simulation of this type using various programming systems and languages. We focus on a shared memory, parallelized algorithm that simulates a 1D heat diffusion using asynchronous queues for the ghost zone exchange. We discuss the advantages of the various platforms and explore the performance of this model code on different computing architectures: Intel, AMD, and ARM64FX. As a result, Python was the slowest of the set we compared. Java, Go, Swift, and Julia were the intermediate performers. The higher performing platforms were C++, Rust, Chapel, Charm++, and HPX.

References in corpus (2)

• Array Programming with NumPy
• Evaluating performance and portability of high-level programming models: Julia, Python/Numba, and Kokkos on exascale nodes

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