• Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 25 Feb 2026] Title:A task-based data-flow methodology for programming heterogeneous systems with multiple accelerator APIs View PDF HTML (experimental)Abstract:Heterogeneous nodes that combine multi-core CPUs with diverse accelerators are rapidly becoming the norm in both high-performance computing (HPC) and AI infrastructures • Exploiting these platforms, however, requires orchestrating several low-level accelerator APIs such as CUDA, SYCL, and Triton • In some occasions they can be combined with optimized vendor math libraries: e • Each API or library introduces its own abstractions, execution semantics, and synchronization mechanisms • Combining them within a single application is therefore error-prone and labor-intensive • We propose reusing a task-based data-flow methodology together with Task-Aware APIs (TA-libs) to overcome these limitations and facilitate the seamless integration of multiple accelerator programming models, while still leveraging the best-in-class kernels offered by each API

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  • Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 25 Feb 2026] Title:A task-based data-flow methodology for programming heterogeneous systems with multiple accelerator APIs View PDF HTML (experimental)Abstract:Heterogeneous nodes that combine multi-core CPUs with diverse accelerators are rapidly becoming the norm in both high-performance computing (HPC) and AI infrastructures. Exploiting these platforms, however, requires orchestrating several low-level accelerator APIs such as CUDA, SYCL, and Triton. In some occasions they can be combined with optimized vendor math

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