Leveraging PaRSEC for Task-Based Heterogeneous Computing in Python and Julia

We present Python and Julia bindings (PyPaRSEC and PaRSEC.jl) for the PaRSEC task-based runtime system, providing high-level access to its efficient and portable execution model on distributed heterogeneous architectures. The bindings expose PaRSEC ’s core abstractions—including data management, task creation, and runtime execution—allowing domain scientists to construct parallel workflows from high-level code while preserving PaRSEC ’s execution semantics. We evaluate these bindings using a 1D stencil workload (through PaRSEC ’s Parameterized Task Graph (PTG)) and a general matrix–matrix multiplication (GEMM) workload expressed via Dynamic Task Discovery (DTD). The results show that the bindings reproduce the official workflows with modest overhead, and that performance remains close to native implementations when leveraging optimized numerical backends on CPUs and vendor-provided accelerator libraries on GPUs, while callback-based user kernels provide flexibility at higher overhead. This work demonstrates that PaRSEC ’s high-performance runtime can be effectively leveraged from productive, high-level languages, bridging the gap between scientific prototyping and exascale execution.