Demo Data from EDBT 2026 / reviewers in the wild / expert
István Kolossváry
dblp:66/4817
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › scientific computing systems
molecular dynamics simulation |
0.1 | 2 | 2007 | 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 |
0.1 | 2 | 2007 | 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 |
0.1 | 1 | 2006 | Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters · SC 2006 |
Parallel and multicore computing
parallel programming models and runtimes |
0.1 | 1 | 2006 | Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters · SC 2006 |
High-performance computing › performance optimization at scale
parallel scalability |
0.1 | 1 | 2006 | Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters · SC 2006 |
High-performance computing
performance optimization at scale |
0.1 | 1 | 2006 | Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters · SC 2006 |
Computational science and engineering › computational chemistry › molecular simulation
molecular dynamics |
0.0 | 1 | 2007 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Anton, a special-purpose machine for molecular dynamics simulationabstractThe 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 |
ISCA | 16 |
| 2006 | Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clustersabstractAlthough 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 |
SC | 8 |