EDBT 2026 Demo / reviewers in the wild / expert
Jeffrey Kuskin
dblp:46/262 · also Jeffrey S. Kuskin
· DBLP profile ↗
11ranked-venue papers
2as first author
2since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 11 · 2 first-author · 2 since 2021Software engineering, systems software and programming languages · 4 · 1 first-author
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
10 papers |
High-performance computing · 60% Hardware accelerators and domain-specific architectures · 24% Processor architecture and microarchitecture · 6% | |
| Interdisciplinary, comprehensive, and emerging computing
5 papers |
Computational science and engineering · 52% Bioinformatics and computational biology · 48% |
Topics — the 13 heaviest of 19, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › scientific computing systems
molecular dynamics simulation |
1.1 | 7 | 2021 | Anton 3: twenty microseconds of molecular dynamics simulation before lunch · SC 2021 Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer · SC 2014 Exploiting 162-Nanosecond End-to-End Communication Latency on Anton · SC 2010 |
High-performance computing
scientific computing systems |
1.0 | 5 | 2021 | Anton 3: twenty microseconds of molecular dynamics simulation before lunch · SC 2021 Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer · SC 2014 Millisecond-scale molecular dynamics simulations on Anton · SC 2009 |
Hardware accelerators and domain-specific architectures › scientific computing accelerator
molecular dynamics accelerator |
0.9 | 4 | 2021 | Anton 3: twenty microseconds of molecular dynamics simulation before lunch · SC 2021 Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer · SC 2014 Millisecond-scale molecular dynamics simulations on Anton · SC 2009 |
Bioinformatics and computational biology › molecular informatics › molecular modeling
biomolecular simulation |
0.3 | 4 | 2021 | Anton 3: twenty microseconds of molecular dynamics simulation before lunch · SC 2021 Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer · SC 2014 Millisecond-scale molecular dynamics simulations on Anton · SC 2009 |
Computational science and engineering › computational chemistry
molecular simulation |
0.3 | 4 | 2021 | Anton 3: twenty microseconds of molecular dynamics simulation before lunch · SC 2021 Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer · SC 2014 Millisecond-scale molecular dynamics simulations on Anton · SC 2009 |
Emerging computing paradigms
event-driven computing |
0.2 | 1 | 2013 | Hardware support for fine-grained event-driven computation in Anton 2 · ASPLOS 2013 |
Processor architecture and microarchitecture › multiprocessor architecture
heterogeneous multiprocessor |
0.0 | 1 | 2008 | Incorporating flexibility in Anton, a specialized machine for molecular dynamics simulation · HPCA 2008 |
Processor architecture and microarchitecture
multiprocessor architecture |
0.0 | 2 | 1994 | The Stanford FLASH Multiprocessor · ISCA 1994 The Performance Impact of Flexibility in the Stanford FLASH Multiprocessor · ASPLOS 1994 |
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 |
Memory systems › cache coherence
cache-coherent shared memory |
0.0 | 1 | 1994 | The Stanford FLASH Multiprocessor · ISCA 1994 |
Parallel and multicore computing › parallel programming models
message passing |
0.0 | 1 | 1994 | The Stanford FLASH Multiprocessor · ISCA 1994 |
Memory systems › cache coherence
cache coherence protocol |
0.0 | 1 | 1994 | The Performance Impact of Flexibility in the Stanford FLASH Multiprocessor · ASPLOS 1994 |
Interconnection networks and networks-on-chip
network interface |
0.0 | 1 | 1994 | The Stanford FLASH Multiprocessor · ISCA 1994 |
Methods — techniques the papers use, named apart from their topics
application-specific hardware · 1.8custom chip design · 1.0event-driven architecture · 0.4co-design · 0.4event-driven scheduling · 0.2fine-grained communication · 0.1counted remote writes · 0.1specialized instruction design · 0.1hardware synchronization support · 0.1simulation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | The ΛNTON 3 ASIC: a Fire-Breathing Monster for Molecular Dynamics Simulationsabstract• Understand biomolecular systems through their motions •Numerical integration of Newton's laws of motion — Model atoms as point masses — Compute forces on every atom based on current positions — Update atom velocities and positions in discrete time steps of a few femtoseconds • Force computation described by a model: the force field Peter J. Adams, Brannon Batson, Alistair Bell, Jhanvi Bhatt, J. Adam Butts, Timothy Correia, Bruce Edwards, Peter Feldmann, Christopher H. Fenton, Anthony Forte, Joseph Gagliardo, Gennette Gill, Maria Gorlatova, Brian Greskamp, J. P. Grossman, Jeremy Hunt, Bryan L. Jackson, Mollie M. Kirk, Jeffrey Kuskin, Roy J. Mader, Richard McGowen, Adam McLaughlin, Mark A. Moraes, Mohamed Nasr, Lawrence J. Nociolo, Lief O'Donnell, Jon L. Peticolas, Terry Quan, T. Carl Schwink, Keun Sup Shim, Naseer Siddique, Jochen Spengler, Michael Theobald, Brian Towles, William Vick, Stanley C. Wang, Michael E. Wazlowski, Madeleine J. Weingarten, John M. Williams, David E. Shaw |
HCS | 19 |
| 2021 | Anton 3: twenty microseconds of molecular dynamics simulation before lunchabstractAnton 3 is the newest member in a family of supercomputers specially designed for atomic-level simulation of molecules relevant to biology (e.g., DNA, proteins, and drug molecules). Anton 3 achieves order-of-magnitude improvements in time-to-solution over its predecessor, Anton 2 (the current state of the art), and is over 100-fold faster than any other currently available supercomputer, thereby enabling broad new avenues of research on critical questions in biology and drug discovery. This speedup means that a 512-node Anton 3 simulates a million atoms at over 100 microseconds per day. Furthermore, Anton 3 attains this performance while consuming an order of magnitude less energy per simulated microsecond than any other machine. Like its predecessors, Anton 3 was designed from the ground up around a new custom chip to best exploit the capabilities offered by new technologies. We present here the main architectural and algorithmic developments that were necessary to achieve such significant advances. David E. Shaw, Peter J. Adams, Asaph Azaria, Joseph A. Bank, Brannon Batson, Alistair Bell, Michael Bergdorf, Jhanvi Bhatt, J. Adam Butts, Timothy Correia, Robert M. Dirks, Ron O. Dror, Michael P. Eastwood, Bruce Edwards, Amos Even, Peter Feldmann, Michael Fenn, Christopher H. Fenton, Anthony Forte, Joseph Gagliardo, Gennette Gill, Maria Gorlatova, Brian Greskamp, J. P. Grossman, Justin Gullingsrud, Anissa Harper, William Hasenplaugh, Mark Heily, Benjamin Colin Heshmat, Jeremy Hunt, Doug Ierardi, Lev Iserovich, Bryan L. Jackson, Nick P. Johnson, Mollie M. Kirk, John L. Klepeis, Jeffrey Kuskin, Kenneth M. Mackenzie, Roy J. Mader, Richard McGowen, Adam McLaughlin, Mark A. Moraes, Mohamed H. Nasr, Lawrence J. Nociolo, Lief O'Donnell, Jon L. Peticolas, Goran Pocina, Cristian Predescu, Terry Quan, John K. Salmon, Carl Schwink, Keun Sup Shim, Naseer Siddique, Jochen Spengler, Tamas Szalay, Raymond Tabladillo, Reinhard Tartler, Andrew G. Taube, Michael Theobald, Brian Towles, William Vick, Stanley C. Wang, Michael Wazlowski, Madeleine J. Weingarten, John M. Williams, Kevin A. Yuh |
SC | 37 |
| 2014 | Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics SupercomputerabstractAnton 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 |
SC | 18 |
| 2013 | Hardware support for fine-grained event-driven computation in Anton 2abstractExploiting 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 |
ASPLOS | 2 |
| 2010 | Exploiting 162-Nanosecond End-to-End Communication Latency on AntonabstractStrong scaling of scientific applications on parallel architectures is increasingly limited by communication latency. This paper describes the techniques used to mitigate latency in Anton, a massively parallel special-purpose machine that accelerates molecular dynamics (MD) simulations by orders of magnitude compared with the previous state of the art. Achieving this speedup required a combination of hardware mechanisms and software constructs to reduce network latency, sender and receiver overhead, and synchronization costs. Key elements of Anton's approach, in addition to tightly integrated communication hardware, include formulating data transfer in terms of counted remote writes, leveraging fine-grained communication, and establishing fixed, optimized communication patterns. Anton delivers software-to-software inter-node latency significantly lower than any other large-scale parallel machine, and the total critical-path communication time for an Anton MD simulation is less than 4% that of the next fastest MD platform. Ron O. Dror, J. P. Grossman, Kenneth M. Mackenzie, Brian Towles, Edmond Chow, John K. Salmon, Cliff Young, Joseph A. Bank, Brannon Batson, Martin M. Deneroff, Jeffrey Kuskin, Richard H. Larson, Mark A. Moraes, David E. Shaw |
SC | 11 |
| 2009 | Millisecond-scale molecular dynamics simulations on AntonabstractAnton is a recently completed special-purpose supercomputer designed for molecular dynamics (MD) simulations of biomolecular systems. The machine's specialized hardware dramatically increases the speed of MD calculations, making possible for the first time the simulation of biological molecules at an atomic level of detail for periods on the order of a millisecond---about two orders of magnitude beyond the previous state of the art. Anton is now running simulations on a timescale at which many critically important, but poorly understood phenomena are known to occur, allowing the observation of aspects of protein dynamics that were previously inaccessible to both computational and experimental study. Here, we report Anton's performance when executing actual MD simulations whose accuracy has been validated against both existing MD software and experimental observations. We also discuss the manner in which novel algorithms have been coordinated with Anton's co-designed, application-specific hardware to achieve these results. David E. Shaw, Ron O. Dror, John K. Salmon, J. P. Grossman, Kenneth M. Mackenzie, Joseph A. Bank, Cliff Young, Martin M. Deneroff, Brannon Batson, Kevin J. Bowers, Edmond Chow, Michael P. Eastwood, Doug Ierardi, John L. Klepeis, Jeffrey Kuskin, Richard H. Larson, Kresten Lindorff-Larsen, Paul Maragakis, Mark A. Moraes, Stefano Piana, Yibing Shan, Brian Towles |
SC | 15 |
| 2009 | Millisecond-scale molecular dynamics simulations on AntonabstractAnton is a recently completed special-purpose supercomputer designed for molecular dynamics (MD) simulations of biomolecular systems. The machine's specialized hardware dramatically increases the speed of MD calculations, making possible for the first time the simulation of biological molecules at an atomic level of detail for periods on the order of a millisecond---about two orders of magnitude beyond the previous state of the art. Anton is now running simulations on a timescale at which many critically important, but poorly understood phenomena are known to occur, allowing the observation of aspects of protein dynamics that were previously inaccessible to both computational and experimental study. Here, we report Anton's performance when executing actual MD simulations whose accuracy has been validated against both existing MD software and experimental observations. We also discuss the manner in which novel algorithms have been coordinated with Anton's co-designed, application-specific hardware to achieve these results. David E. Shaw, Ron O. Dror, John K. Salmon, J. P. Grossman, Kenneth M. Mackenzie, Joseph A. Bank, Cliff Young, Martin M. Deneroff, Brannon Batson, Kevin J. Bowers, Edmond Chow, Michael P. Eastwood, Doug Ierardi, John L. Klepeis, Jeffrey Kuskin, Richard H. Larson, Kresten Lindorff-Larsen, Paul Maragakis, Mark A. Moraes, Stefano Piana, Yibing Shan, Brian Towles |
SC | 15 |
| 2008 | Incorporating flexibility in Anton, a specialized machine for molecular dynamics simulationabstractAn effective special-purpose supercomputer for molecular dynamics (MD) requires much more than high-performance acceleration of computational kernels: such accelerators must be balanced with general-purpose computation and communication resources. Achieving this balance was a significant challenge in the design of Anton, a parallel machine that will accelerate MD simulations by several orders of magnitude. Anton executes its most computationally demanding calculations on a highly specialized, enormously parallel, but largely non-programmable high-throughput interaction subsystem (HTIS). Other elements of the simulation have a less uniform algorithmic structure, and may also change in response to future advances in physical models and simulation techniques. Such calculations are executed on Antonpsilas flexible subsystem, which combines programmability with the computational power required to avoid ldquoAmdahlpsilas Lawrdquo bottlenecks arising from the extremely high throughput of the HTIS. Antonpsilas flexible subsystem is a heterogeneous multiprocessor with 12 cores, each organized around a 128-bit data path. This subsystem includes hardware support for synchronization, data transfer and certain types of particle interactions, along with specialized instructions for geometric operations. All aspects of the flexible subsystem were designed specifically to accelerate MD simulations, and although it relies primarily on what may be regarded as ldquogeneral-purposerdquo processors, even this subsystem contains more application-specific features than many recently proposed ldquospecializedrdquo architectures. Jeffrey Kuskin, Cliff Young, J. P. Grossman, Brannon Batson, Martin M. Deneroff, Ron O. Dror, David E. Shaw |
HPCA | 1 |
| 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 | 4 |
| 1994 | The Performance Impact of Flexibility in the Stanford FLASH MultiprocessorabstractA flexible communication mechanism is a desirable feature in multiprocessors because it allows support for multiple communication protocols, expands performance monitoring capabilities, and leads to a simpler design and debug process. In the Stanford FLASH multiprocessor, flexibility is obtained by requiring all transactions in a node to pass through a programmable node controller, called MAGIC. In this paper, we evaluate the performance costs of flexibility by comparing the performance of FLASH to that of an idealized hardwired machine on representative parallel applications and a multiprogramming workload. To measure the performance of FLASH, we use a detailed simulator of the FLASH and MAGIC designs, together with the code sequences that implement the cache-coherence protocol. We find that for a range of optimized parallel applications the performance differences between the idealized machine and FLASH are small. For these programs, either the miss rates are small or the latency of the programmable protocol can be hidden behind the memory access time. For applications that incur a large number of remote misses or exhibit substantial hot-spotting, performance is poor for both machines, though the increased remote access latencies or the occupancy of MAGIC lead to lower performance for the flexible design. In most cases, however, FLASH is only 2%–12% slower than the idealized machine. Mark A. Heinrich, Jeffrey Kuskin, David Ofelt, John Heinlein, Joel Baxter, Jaswinder Pal Singh, Richard Simoni, Kourosh Gharachorloo, David Nakahira, Mark Horowitz, Anoop Gupta, Mendel Rosenblum, John L. Hennessy |
ASPLOS | 2 |
| 1994 | The Stanford FLASH MultiprocessorabstractThe FLASH multiprocessor efficiently integrates support for cache-coherent shared memory and high-performance message passing, while minimizing both hardware and software overhead. Each node in FLASH contains a microprocessor, a portion of the machine's global memory, a port to the interconnection network, The MAGIC chip handles all communication both within the node and among nodes, using hardwired data paths for efficient data movement and a programmable processor optimized for executing protocol operations. The use of the protocol processor makes FLASH very flexible/spl minus/it can support a variety of different communication mechanisms/spl minus/and simplifies the design and implementation. This paper presents the architecture of FLASH and MAGIC, and discusses the base cache-coherence and message-passing protocols. Latency and occupancy numbers, which are derived from our system-level simulator and our Verilog code, are given for several common protocol operations. The paper also describes our software strategy and FLASH's current status.> Jeffrey Kuskin, David Ofelt, Mark A. Heinrich, John Heinlein, Richard Simoni, Kourosh Gharachorloo, John Chapin, David Nakahira, Joel Baxter, Mark Horowitz, Anoop Gupta, Mendel Rosenblum, John L. Hennessy |
ISCA | 1 |