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U. Ben Schafer

dblp:127/3132 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

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 · 45% Hardware accelerators and domain-specific architectures · 20% Emerging computing paradigms · 17%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 50% Bioinformatics and computational biology · 50%

Topics — the 6 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › scientific computing systems
molecular dynamics simulation
0.222014
Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer · SC 2014
Hardware support for fine-grained event-driven computation in Anton 2 · ASPLOS 2013
Hardware accelerators and domain-specific architectures › scientific computing accelerator
molecular dynamics accelerator
0.212014
Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer · SC 2014
High-performance computing
scientific computing systems
0.212014
Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer · SC 2014
Emerging computing paradigms
event-driven computing
0.212013
Hardware support for fine-grained event-driven computation in Anton 2 · ASPLOS 2013
Bioinformatics and computational biology › molecular informatics › molecular modeling
biomolecular simulation
0.112014
Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer · SC 2014
Computational science and engineering › computational chemistry
molecular simulation
0.112014
Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer · SC 2014

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

event-driven architecture · 0.4application-specific hardware · 0.4event-driven scheduling · 0.2
YearPublicationVenuePosition
2014 Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer
abstract
Anton 2 is a second-generation special-purpose supercomputer for molecular dynamics simulations that achieves significant gains in performance, programmability, and capacity compared to its predecessor, Anton 1. The architecture of Anton 2 is tailored for fine-grained event-driven operation, which improves performance by increasing the overlap of computation with communication, and also allows a wider range of algorithms to run efficiently, enabling many new software-based optimizations. A 512-node Anton 2 machine, currently in operation, is up to ten times faster than Anton 1 with the same number of nodes, greatly expanding the reach of all-atom bio molecular simulations. Anton 2 is the first platform to achieve simulation rates of multiple microseconds of physical time per day for systems with millions of atoms. Demonstrating strong scaling, the machine simulates a standard 23,558-atom benchmark system at a rate of 85 μs/day -- 180 times faster than any commodity hardware platform or general-purpose supercomputer.
David E. Shaw, J. P. Grossman, Joseph A. Bank, Brannon Batson, J. Adam Butts, Jack C. Chao, Martin M. Deneroff, Ron O. Dror, Amos Even, Christopher H. Fenton, Anthony Forte, Joseph Gagliardo, Gennette Gill, Brian Greskamp, Richard Ho 0001, Doug Ierardi, Lev Iserovich, Jeffrey Kuskin, Richard H. Larson, Timothy Layman, Li-Siang Lee, Adam K. Lerer, Chester Li, Daniel Killebrew, Kenneth M. Mackenzie, Shark Yeuk-Hai Mok, Mark A. Moraes, Lawrence J. Nociolo, Jon L. Peticolas, Terry Quan, Daniel Ramot, John K. Salmon, Daniele Paolo Scarpazza, U. Ben Schafer, Naseer Siddique, Christopher W. Snyder, Jochen Spengler, Ping Tak Peter Tang, Michael Theobald, Horia Toma, Brian Towles, Benjamin Vitale, Stanley C. Wang, Cliff Young
SC35
2013 Hardware support for fine-grained event-driven computation in Anton 2
abstract
Exploiting parallelism to accelerate a computation typically involves dividing it into many small tasks that can be assigned to different processing elements. An efficient execution schedule for these tasks can be difficult or impossible to determine in advance, however, if there is uncertainty as to when each task's input data will be available. Ideally, each task would run in direct response to the arrival of its input data, thus allowing the computation to proceed in a fine-grained event-driven manner. Realizing this ideal is difficult in practice, and typically requires sacrificing flexibility for performance.
J. P. Grossman, Jeffrey Kuskin, Joseph A. Bank, Michael Theobald, Ron O. Dror, Doug Ierardi, Richard H. Larson, U. Ben Schafer, Brian Towles, Cliff Young, David E. Shaw
ASPLOS8