Data and Thread Placement in NUMA Architectures: A Statistical Learning Approach

Nicolas Denoyelle 1 Brice Goglin 2 Emmanuel Jeannot 2 Thomas Ropars 3
1 ANL - Argonne National Laboratory [Lemont]
2 TADAAM - Topology-Aware System-Scale Data Management for High-Performance Computing
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest
3 UGA - Université Grenoble Alpes
Abstract : Nowadays, NUMA architectures are common in compute-intensive systems. Achieving high performance for multi-threaded application requires both a careful placement of threads on computing units and a thorough allocation of data in memory. Finding such a placement is a hard problem to solve, because performance depends on complex interactions in several layers of the memory hierarchy. In this paper we propose a black-box approach to decide if an application execution time can be impacted by the placement of its threads and data, and in such a case, to choose the best placement strategy to adopt. We show that it is possible to reach near-optimal placement policy selection. Furthermore, solutions work across several recent processor architectures and decisions can be taken with a single run of low overhead profiling.
Keywords : Multicore processors High-performance computing NUMA Machine learning Threads Placement Data
Document type :
Conference papers
Complete list of metadatas

Cited literature [32 references]  Display  Hide  Download
https://hal.inria.fr/hal-02135545
Contributor : Brice Goglin <>
Submitted on : Tuesday, July 30, 2019 - 11:10:07 AM
Last modification on : Monday, August 5, 2019 - 3:00:00 PM

File

paper.pdf
Files produced by the author(s)

Identifiers

Collections

UNIV-BORDEAUX | LABRI | INRIA | INRIA-BORDEAUX

Citation

Nicolas Denoyelle, Brice Goglin, Emmanuel Jeannot, Thomas Ropars. Data and Thread Placement in NUMA Architectures: A Statistical Learning Approach. ICPP 2019 - 48th International Conference on Parallel Processing, Aug 2019, Kyoto, Japan. pp.1-10, ⟨10.1145/3337821.3337893⟩. ⟨hal-02135545v3⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

85

Files downloads

298

Contact


archive-ouverte@inria.fr