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István Kolossváry

dblp:66/4817 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2007
0000-0002-0424-4577ORCID · corroborated

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

Systems, architecture and hardware · 2Software engineering, systems software and programming languages · 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
2 papers
High-performance computing · 76% Parallel and multicore computing · 24%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › scientific computing systems
molecular dynamics simulation
0.122007
Anton, a special-purpose machine for molecular dynamics simulation · ISCA 2007
Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters · SC 2006
High-performance computing
scientific computing systems
0.122007
Anton, a special-purpose machine for molecular dynamics simulation · ISCA 2007
Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters · SC 2006
Parallel and multicore computing › parallelization strategies
parallel decomposition
0.112006
Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters · SC 2006
Parallel and multicore computing
parallel programming models and runtimes
0.112006
Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters · SC 2006
High-performance computing › performance optimization at scale
parallel scalability
0.112006
Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters · SC 2006
High-performance computing
performance optimization at scale
0.112006
Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters · SC 2006
Computational science and engineering › computational chemistry › molecular simulation
molecular dynamics
0.012007
Anton, a special-purpose machine for molecular dynamics simulation · ISCA 2007

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

vector instructions · 0.1single-precision computation · 0.1message passing · 0.1
YearPublicationVenuePosition
2007 Anton, a special-purpose machine for molecular dynamics simulation
abstract
The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macro-molecules could in principle provide answers to some of the most important currently outstanding questions in the fields of biology, chemistry and medicine. A wide range of biologically interesting phenomena, however, occur over time scales on the order of a millisecond--about three orders of magnitude beyond the duration of the longest current MD simulations.
David E. Shaw, Martin M. Deneroff, Ron O. Dror, Jeffrey Kuskin, Richard H. Larson, John K. Salmon, Cliff Young, Brannon Batson, Kevin J. Bowers, Jack C. Chao, Michael P. Eastwood, Joseph Gagliardo, J. P. Grossman, Richard Ho 0001, Doug Ierardi, István Kolossváry, John L. Klepeis, Timothy Layman, Christine McLeavey, Mark A. Moraes, Edward C. Priest, Yibing Shan, Jochen Spengler, Michael Theobald, Brian Towles, Stanley C. Wang
ISCA16
2006 Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters
abstract
Although molecular dynamics (MD) simulations of biomolecular systems often run for days to months, many events of great scientific interest and pharmaceutical relevance occur on long time scales that remain beyond reach. We present several new algorithms and implementation techniques that significantly accelerate parallel MD simulations compared with current state-of-the-art codes. These include a novel parallel decomposition method and message-passing techniques that reduce communication requirements, as well as novel communication primitives that further reduce communication time. We have also developed numerical techniques that maintain high accuracy while using single precision computation in order to exploit processor-level vector instructions. These methods are embodied in a newly developed MD code called Desmond that achieves unprecedented simulation throughput and parallel scalability on commodity clusters. Our results suggest that Desmond's parallel performance substantially surpasses that of any previously described code. For example, on a standard benchmark, Desmond's performance on a conventional Opteron cluster with 2K processors slightly exceeded the reported performance of IBM's Blue Gene/L machine with 32K processors running its Blue Matter MD code.
Kevin J. Bowers, Edmond Chow, Huafeng Xu, Ron O. Dror, Michael P. Eastwood, Brent A. Gregersen, John L. Klepeis, István Kolossváry, Mark A. Moraes, Federico D. Sacerdoti, John K. Salmon, Yibing Shan, David E. Shaw
SC8