Using Static Allocation Algorithms for Matrix Matrix Multiplication on Multicores and GPUs
| hal.structure.identifier | Reformulations based algorithms for Combinatorial Optimization [Realopt] | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | EYRAUD-DUBOIS, Lionel | |
| hal.structure.identifier | Reformulations based algorithms for Combinatorial Optimization [Realopt] | |
| hal.structure.identifier | School of Computer Science [Manchester] | |
| dc.contributor.author | LAMBERT, Thomas | |
| dc.date.accessioned | 2024-04-04T03:05:53Z | |
| dc.date.available | 2024-04-04T03:05:53Z | |
| dc.date.conference | 2018-08-13 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/193293 | |
| dc.description.abstractEn | We consider the problem of data allocation when performing matrix multiplication on a heterogeneous node, with multicores and GPUs. Classical (cyclic) allocations designed for homogeneous settings are not appropriate, but the advent of task-based runtime systems makes it possible to use more general allocations. Previous theoretical work has proposed square and cube partitioning algorithms aimed at minimizing data movement for matrix multiplication. We propose techniques to adapt these continuous square partitionings to allocating discrete tiles of a matrix, and strategies to adapt the static allocation at run-time. We use these techniques in an implementation of Matrix Multiplication based on the StarPU runtime system, and we show through extensive experiments that this implementation allows to consistently obtain a lower communication volume while improving slightly the execution time, compared to standard state-of-the-art dynamic strategies. | |
| dc.description.sponsorship | Solveurs pour architectures hétérogènes utilisant des supports d'exécution - ANR-13-MONU-0007 | |
| dc.language.iso | en | |
| dc.subject.en | Mathematics of computing → Solvers | |
| dc.subject.en | Computing methodologies → Linear algebra algorithms | |
| dc.subject.en | Parallel algorithms | |
| dc.subject.en | General and reference → Performance | |
| dc.title.en | Using Static Allocation Algorithms for Matrix Matrix Multiplication on Multicores and GPUs | |
| dc.type | Communication dans un congrès | |
| dc.identifier.doi | 10.1145/3225058.3225066 | |
| dc.subject.hal | Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC] | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.conference.title | ICPP 2018 - 47th International Conference on Parallel Processing | |
| bordeaux.country | US | |
| bordeaux.conference.city | Eugene, OR | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-01670678 | |
| hal.version | 1 | |
| hal.invited | non | |
| hal.proceedings | oui | |
| hal.conference.end | 2018-08-16 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-01670678v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=EYRAUD-DUBOIS,%20Lionel&LAMBERT,%20Thomas&rft.genre=unknown |
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