Electronic Resource
A task-based data-flow methodology for programming heterogeneous systems with multiple accelerator APIs
| Τίτλος: | A task-based data-flow methodology for programming heterogeneous systems with multiple accelerator APIs |
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| Συγγραφείς: | Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. PM - Programming Models, Boné Ribó, Aleix, Aguirre López, Alejandro, Álvarez Robert, David, Martínez Ferrer, Pedro José, Beltran Querol, Vicenç |
| Στοιχεία εκδότη: | Elsevier 2026-07 |
| Τύπος εγγράφου: | Electronic Resource |
| Περίληψη: | 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.g., cuBLAS and oneAPI. 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. Applications are expressed as a directed acyclic graph (DAG) of host tasks and device kernels managed by an OpenMP/OmpSs-2 runtime. We introduce Task-Aware SYCL (TASYCL) and leverage Task-Aware CUDA (TACUDA), which elevate individual accelerator invocations to first-class tasks. When multiple native runtimes coexist on the same multi-core CPU, they contend for threads, leading to oversubscription and performance variability. To address this, we unify their thread management under the nOS-V tasking and threading library, to which we contribute a new port of the PoCL (Portable OpenCL) runtime. The methodology is evaluated on a multi-core server and a GPU-accelerated node using two contrasting workloads: the GPT-2 pre-training phase, representative of modern AI pipelines, and the HPCCG conjugate-gradient benchmark, representative of traditional HPC. From a performance standpoint, monolithic-kernel and fork-join executions are comparable —in both execution time and memory footprint— to a coarse-grained task-based formulation on both GPU-accelerated and multi-core systems. On the latter, unifying all runtimes throug This work has received support from multiple funding sources. It is part of the ST4HPC project (PID2023-147979NB-C21), funded by MCIN/AEI/10.13039/501100011033 and by FEDER, UE. This work is also promoted by the Barcelona Zettascale Laboratory, backed by the Ministry for Digital Transformation and of Public Services, within the framework of the Recovery, Transformation, and Resilience Plan - funded by the European Union - NextGenerationEU. This work has received funding from the DARE SGA1 Project, from the European HighPerformance Computing Joint Undertaking (JU) under Grant Agreement No 101202459 and from PCI2024-161687-3 Project funded by MICIU/AEI/10.13039/501,100,011,033 and European’s Union NextGenerationEU/PRTR. Additional support was provided by a Ramón y Cajal fellowship (RYC2019-027592-I), funded by MCIN/AEI/10.13039/ 501,100,011,033 and ESF/10.13039/501100004895, and by the Severo Ochoa Centre of Excellence programme (CEX2021-001148-S), also funded by MCIN/AEI. Finally, the Programming Models research group at BSC-UPC received support from the Departament de Recerca i Universitats de la Generalitat de Catalunya under grants 2021 SGR 01007 and 2025 STEP 00523. 11 Peer Reviewed Postprint (author's final draft) |
| Όροι ευρετηρίου: | Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors, Heterogeneous computing, Task-based programming, Data-flow execution, Accelerator APIs, CUDA, SYCL, Triton, OpenMP offload, Task-aware libraries, Runtime interoperability, nOS-V, Article |
| Σύνδεσμος: | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-147979NB-C21/ES/HERRAMIENTAS SOFTWARE PARA HPC - BSC info:eu-repo/grantAgreement/EC/HE/101202459/EU/Digital Autonomy for RISC-V in Europe (Specific Grant Agreement 1)/DARE SGA 1 info:eu-repo/grantAgreement/AEI//PCI2024-161687-3 info:eu-repo/grantAgreement/AEI//CEX2021-001148-S |
| Διαθεσιμότητα: | Open access content. Open access content http://creativecommons.org/licenses/by-nc-nd/4.0 Restricted access - publisher's policy Attribution-NonCommercial-NoDerivatives 4.0 International |
| Σημείωση: | 13 p. application/pdf English |
| Other Numbers: | HGF oai:upcommons.upc.edu:2117/459534 Boné, A. [et al.]. A task-based data-flow methodology for programming heterogeneous systems with multiple accelerator APIs. «Future generation computer systems», Juliol 2026, vol. 180, núm. article 108383. 1872-7115 10.1016/j.future.2026.108383 1591765622 |
| Πηγή συνεισφοράς: | UNIV POLITECNICA DE CATALUNYA From OAIster®, provided by the OCLC Cooperative. |
| Αριθμός Καταχώρησης: | edsoai.on1591765622 |
| Βάση Δεδομένων: | OAIster |
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