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Alexei Strelchenko

dblp:189/1140 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 50% Parallel and multicore computing · 28% GPUs and heterogeneous computing · 22%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › parallelization strategies
fine-grained parallelization
0.212016
Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016
GPUs and heterogeneous computing
GPU computing
0.212016
Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016
High-performance computing › sparse linear solver
multigrid solvers
0.212016
Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016
High-performance computing
scientific computing systems
0.212016
Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016
Parallel and multicore computing
parallel programming models
0.112016
Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016
High-performance computing
stencil computation
0.112016
Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016

Methods — techniques the papers use, named apart from their topics

hierarchical multigrid · 0.2fine-grained parallelism mapping · 0.2
YearPublicationVenuePosition
2016 Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization
abstract
The past decade has witnessed a dramatic acceleration of lattice quantum chromodynamics calculations in nuclear and particle physics. This has been due to both significant progress in accelerating the iterative linear solvers using multigrid algorithms, and due to the throughput improvements brought by GPUs. Deploying hierarchical algorithms optimally on GPUs is non-trivial owing to the lack of parallelism on the coarse grids, and as such, these advances have not proved multiplicative. Using the QUDA library, we demonstrate that by exposing all sources of parallelism that the underlying stencil problem possesses, and through appropriate mapping of this parallelism to the GPU architecture, we can achieve high efficiency even for the coarsest of grids. Results are presented for the Wilson-Clover discretization, where we demonstrate up to 10x speedup over present state-of-the-art GPU-accelerated methods on Titan. Finally, we look to the future, and consider the software implications of our findings.
Michael A. Clark, Bálint Joó, Alexei Strelchenko, Michael Cheng, Arjun Singh Gambhir, Richard C. Brower
SC3