Demo Data from SIGIR 2026 / reviewers in the wild / expert
David F. Richards
dblp:73/4528
· DBLP profile ↗
12ranked-venue papers
2as first author
2since 2021 · last 2022
0000-0003-2047-3780ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 12 · 2 first-author · 2 since 2021Software 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
5 papers |
High-performance computing · 50% Parallel and multicore computing · 30% GPUs and heterogeneous computing · 15% | |
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Medical and health informatics · 60% Computational science and engineering · 40% |
Topics — the 16 heaviest of 18, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
GPUs and heterogeneous computing
GPU runtime systems |
0.5 | 1 | 2021 | CharminG: A Scalable GPU-resident Runtime System · HPDC 2021 |
Parallel and multicore computing
parallel programming models |
0.5 | 1 | 2021 | CharminG: A Scalable GPU-resident Runtime System · HPDC 2021 |
High-performance computing
application porting |
0.4 | 1 | 2019 | Preparation and optimization of a diverse workload for a large-scale heterogeneous system · SC 2019 |
Parallel and multicore computing
programming models |
0.4 | 1 | 2019 | Preparation and optimization of a diverse workload for a large-scale heterogeneous system · SC 2019 |
High-performance computing
supercomputing |
0.2 | 2 | 2021 | CharminG: A Scalable GPU-resident Runtime System · HPDC 2021 Extending stability beyond CPU millennium: a micron-scale atomistic simulation of Kelvin-Helmholtz instability · SC 2007 |
High-performance computing › scientific computing systems
molecular dynamics simulation |
0.2 | 2 | 2009 | Beyond homogeneous decomposition: scaling long-range forces on Massively Parallel Systems · SC 2009 Extending stability beyond CPU millennium: a micron-scale atomistic simulation of Kelvin-Helmholtz instability · SC 2007 |
High-performance computing
performance optimization at scale |
0.1 | 1 | 2012 | Toward real-time modeling of human heart ventricles at cellular resolution: simulation of drug-induced arrhythmias · SC 2012 |
High-performance computing › large-scale simulation
petascale simulation |
0.1 | 1 | 2012 | Toward real-time modeling of human heart ventricles at cellular resolution: simulation of drug-induced arrhythmias · SC 2012 |
Performance modeling and evaluation
benchmarking |
0.1 | 1 | 2019 | Preparation and optimization of a diverse workload for a large-scale heterogeneous system · SC 2019 |
Parallel and multicore computing › parallelization strategies
parallel decomposition |
0.1 | 1 | 2009 | Beyond homogeneous decomposition: scaling long-range forces on Massively Parallel Systems · SC 2009 |
High-performance computing
scientific computing systems |
0.1 | 1 | 2009 | Beyond homogeneous decomposition: scaling long-range forces on Massively Parallel Systems · SC 2009 |
Distributed systems
fault tolerance |
0.1 | 1 | 2007 | Extending stability beyond CPU millennium: a micron-scale atomistic simulation of Kelvin-Helmholtz instability · SC 2007 |
High-performance computing
parallel i/o |
0.1 | 1 | 2007 | Extending stability beyond CPU millennium: a micron-scale atomistic simulation of Kelvin-Helmholtz instability · SC 2007 |
High-performance computing
scientific computing |
0.1 | 1 | 2007 | Extending stability beyond CPU millennium: a micron-scale atomistic simulation of Kelvin-Helmholtz instability · SC 2007 |
Computational science and engineering › computational physics
plasma physics simulation |
0.0 | 1 | 2009 | Beyond homogeneous decomposition: scaling long-range forces on Massively Parallel Systems · SC 2009 |
High-performance computing › supercomputing
bluegene/l |
0.0 | 1 | 2007 | Extending stability beyond CPU millennium: a micron-scale atomistic simulation of Kelvin-Helmholtz instability · SC 2007 |
Methods — techniques the papers use, named apart from their topics
processor virtualization · 0.5parallel scaling · 0.3cellular-resolution simulation · 0.3long-range force computation · 0.2heterogeneous decomposition · 0.2interatomic force calculation optimization · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Accelerating communication for parallel programming models on GPU systems
Zane Fink, Sam White, Nitin Bhat, David F. Richards, Laxmikant V. Kalé |
Parallel Comput. | 5 |
| 2021 | CharminG: A Scalable GPU-resident Runtime SystemabstractHost-driven execution of applications on modern GPU-accelerated systems suffer from frequent host-device synchronizations, data movement and limited flexibility in scheduling user tasks. We present CharminG, a runtime system designed to run entirely on the GPU without any interaction with the host. CharminG takes inspiration from the Charm++ parallel programming system and implements processor virtualization and message-driven execution on the GPU. We evaluate the composability and preliminary performance of CharminG with a proxy application that performs the Jacobi iterative method in a two-dimensional grid, using the Lassen supercomputer at Lawrence Livermore National Laboratory. David F. Richards, Laxmikant V. Kalé |
HPDC | 2 |
| 2020 | End-to-end performance modeling of distributed GPU applicationsabstractWith the growing number of GPU-based supercomputing platforms and GPU-enabled applications, the ability to accurately model the performance of such applications is becoming increasingly important. Most current performance models for GPU-enabled applications are limited to single node performance. In this work, we propose a methodology for end-to-end performance modeling of distributed GPU applications. Our work strives to create performance models that are both accurate and easily applicable to any distributed GPU application. We combine trace-driven simulation of MPI communication using the TraceR-CODES framework with a profiling-based roofline model for GPU kernels. We make substantial modifications to these models to capture the complex effects of both on-node and off-node networks in today's multi-GPU supercomputers. We validate our model against empirical data from GPU platforms and also vary tunable parameters of our model to observe how they might affect application performance. David F. Richards, Laxmikant V. Kalé, Abhinav Bhatele |
ICS | 2 |
| 2019 | Preparation and optimization of a diverse workload for a large-scale heterogeneous systemabstractProductivity from day one on supercomputers that leverage new technologies requires significant preparation. An institution that procures a novel system architecture often lacks sufficient institutional knowledge and skills to prepare for it. Thus, the "Center of Excellence" (CoE) concept has emerged to prepare for systems such as Summit and Sierra, currently the top two systems in the Top 500. This paper documents CoE experiences that prepared a workload of diverse applications and math libraries for a heterogeneous system. We describe our approach to this preparation, including our management and execution strategies, and detail our experiences with and reasons for using different programming approaches. Our early science and performance results show that the project enabled significant early seismic science with up to a l4X throughput increase over Cori. In addition to our successes, we discuss our challenges and failures so others may benefit from our experience. Ian Karlin, Yoonho Park, Bronis R. de Supinski, Bert Still, D. A. Beckingsale, Robert Blake, Tong Chen 0001, Guojing Cong, Carlos H. A. Costa, Johann Dahm, Giacomo Domeniconi, Thomas Epperly, Aaron Fisher, Sara Kokkila Schumacher, Steve H. Langer, Hai Le, Naoya Maruyama, Xinyu Que, David F. Richards, Björn Sjögreen, Jonathan Wong, Carol S. Woodward, Ulrike Meier Yang, Bob Anderson, David Appelhans, Levi Barnes, Peter D. Barnes Jr., Sorin Bastea, David Böhme, Jamie A. Bramwell, James M. Brase, José R. Brunheroto, Barry Chen, Charway R. Cooper, Tony Degroot, Robert D. Falgout, Todd Gamblin, David J. Gardner, James N. Glosli, John A. Gunnels, Max P. Katz, Tzanio V. Kolev, I-Feng W. Kuo, Matthew P. LeGendre, Pei-Hung Lin, Shelby Lockhart, Kathleen McCandless, Claudia Misale, Jaime H. Moreno, Rob Neely, Jarom Nelson, Rao Nimmakayala, Kathryn M. O'Brien, Kevin O'Brien, Ramesh Pankajakshan, Roger A. Pearce, Slaven Peles, Phil Regier, Steven C. Rennich, Martin Schulz 0001, Howard Scott, James C. Sexton, Kathleen Shoga, Shiv Sundram, Guillaume Thomas-Collignon, Brian Van Essen, Alexey Voronin, Bob Walkup, Chris Ward, Hui-Fang Wen, Daniel A. White, Christopher Young, Cyril Zeller, Edward Zywicz |
SC | 21 |
| 2019 | Exploring dynamic load imbalance solutions with the CoMD proxy application
Olga Pearce, Hadia Ahmed, Rasmus W. Larsen, Peter Pirkelbauer, David F. Richards |
Future Gener. Comput. Syst. | 5 |
| 2017 | Quicksilver: A Proxy App for the Monte Carlo Transport Code MercuryabstractLike many other code teams, the developers of the Mercury Monte Carlo Transport code at Lawrence Livermore National Laboratory are being forced by the arrival of GPUbased supercomputers to substantially refactor their application to obtain acceptable performance on new architectures. This paper describes how we have designed, developed, and used Quicksilver, a proxy application for Mercury, to assist in this process. We explain the goals and principles that informed Quicksilver’s design and describe the features of Mercury that are and are not represented by the proxy. We recount some experiences testing Quicksilver and changes made to improve its ability to represent Mercury. Finally we explore how we have used Quicksilver and QS lite, a proxy app for Quicksilver, as prototypes to test potential refactorings and provide performance results on GPU and CPU platforms. David F. Richards, Ryan Bleile, Patrick S. Brantley, Shawn A. Dawson, Michael Scott McKinley, Matthew J. OBrien |
CLUSTER | 1 |
| 2016 | IPAS: intelligent protection against silent output corruption in scientific applicationsabstractThis paper presents IPAS, an instruction duplication technique that protects scientific applications from silent data corruption (SDC) in their output. The motivation for IPAS is that, due to natural error masking, only a subset of SDC errors actually affects the output of scientific codes—we call these errors silent output corruption (SOC) errors. Thus applications require duplication only on code that, when affected by a fault, yields SOC. We use machine learning to learn code instructions that must be protected to avoid SOC, and, using a compiler, we protect only those vulnerable instructions by duplication, thus significantly reducing the overhead that is introduced by instruction duplication. In our experiments with five workloads, IPAS reduces the percentage of SOC by up to 90% with a slowdown that ranges between 1.04x and 1.35x, which corresponds to as much as 47% less slowdown than state-of-the-art instruction duplication techniques. Ignacio Laguna, Martin Schulz 0001, David F. Richards, Jon Calhoun 0001, Luke N. Olson |
CGO | 3 |
| 2013 | Exploring Traditional and Emerging Parallel Programming Models Using a Proxy ApplicationabstractParallel machines are becoming more complex with increasing core counts and more heterogeneous architectures. However, the commonly used parallel programming models, C/C++ with MPI and/or OpenMP, make it difficult to write source code that is easily tuned for many targets. Newer language approaches attempt to ease this burden by providing optimization features such as automatic load balancing, overlap of computation and communication, message-driven execution, and implicit data layout optimizations. In this paper, we compare several implementations of LULESH, a proxy application for shock hydrodynamics, to determine strengths and weaknesses of different programming models for parallel computation. We focus on four traditional (OpenMP, MPI, MPI+OpenMP, CUDA) and four emerging (Chapel, Charm++, Liszt, Loci) programming models. In evaluating these models, we focus on programmer productivity, performance and ease of applying optimizations. Ian Karlin, Abhinav Bhatele, Jeff Keasler, Bradford L. Chamberlain, Jonathan D. Cohen 0001, Zach DeVito, Riyaz Haque, Daniel E. Laney, Edward Luke, Felix Wang, David F. Richards, Martin Schulz 0001, Charles H. Still |
IPDPS | 11 |
| 2013 | Analysis of scalable data-privatization threading algorithms for hybrid MPI/OpenMP parallelization of molecular dynamics
Manaschai Kunaseth, David F. Richards, James N. Glosli, Rajiv K. Kalia, Aiichiro Nakano, Priya Vashishta |
J. Supercomput. | 2 |
| 2012 | Toward real-time modeling of human heart ventricles at cellular resolution: simulation of drug-induced arrhythmiasabstractWe have developed a highly efficient and scalable cardiac electrophysiology simulation capability that supports groundbreaking resolution and detail to elucidate the mechanisms of sudden cardiac death from arrhythmia. We can simulate thousands of heartbeats at a resolution of 0.1 mm, comparable to the size of cardiac cells, thereby enabling scientific inquiry not previously possible. Based on scaling results from the partially deployed Sequoia IBM Blue Gene/Q machine at Lawrence Livermore National Laboratory and planned optimizations, we estimate that by SC12 we will simulate 8 -- 10 heartbeats per minute -- a time-to-solution 400 -- 500 times faster than the state-of-the-art. Performance between 8 and 11 PFlop/s on the full 1,572,864 cores is anticipated, representing 40 -- 55 percent of peak. The power of the model is demonstrated by illuminating the subtle arrhythmogenic mechanisms of anti-arrhythmic drugs that paradoxically increase arrhythmias in some patient populations. Arthur A. Mirin, David F. Richards, James N. Glosli, Erik W. Draeger, Bor Chan, Jean-Luc Fattebert, William D. Krauss, Tomas Oppelstrup, John Jeremy Rice, John A. Gunnels, Viatcheslav Gurev, Changhoan Kim, John Magerlein, Matthias Reumann, Hui-Fang Wen |
SC | 2 |
| 2009 | Beyond homogeneous decomposition: scaling long-range forces on Massively Parallel SystemsabstractWith supercomputers anticipated to expand from thousands to millions of cores, one of the challenges facing scientists is how to effectively utilize this ever-increasing number. We report here an approach that creates a heterogeneous decomposition by partitioning effort according to the scaling properties of the component algorithms. We demonstrate our strategy by developing a capability to model hot dense plasma. We have performed benchmark calculations ranging from millions to billions of charged particles, including a 2.8 billion particle simulation that achieved 259.9 TFlop/s (26% of peak performance) on the 294,912 cpu JUGENE computer at the Jülich Supercomputing Centre in Germany. With this unprecedented simulation capability we have begun an investigation of plasma fusion physics under conditions where both theory and experiment are lacking--in the strongly-coupled regime as the plasma begins to burn. David F. Richards, James N. Glosli, Bor Chan, Milo R. Dorr, Erik W. Draeger, Jean-Luc Fattebert, William D. Krauss, Thomas E. Spelce, Frederick H. Streitz, Michael P. Surh, John A. Gunnels |
SC | 1 |
| 2007 | Extending stability beyond CPU millennium: a micron-scale atomistic simulation of Kelvin-Helmholtz instabilityabstractWe report the computational advances that have enabled the first micron-scale simulation of a Kelvin-Helmholtz (KH) instability using molecular dynamics (MD). The advances are in three key areas for massively parallel computation such as on BlueGene/L (BG/L): fault tolerance, application kernel optimization, and highly efficient parallel I/O. In particular, we have developed novel capabilities for handling hardware parity errors and improving the speed of interatomic force calculations, while achieving near optimal I/O speeds on BG/L, allowing us to achieve excellent scalability and improve overall application performance. As a result we have successfully conducted a 2-billion atom KH simulation amounting to 2.8 CPU-millennia of run time, including a single, continuous simulation run in excess of 1.5 CPU-millennia. We have also conducted 9-billion and 62.5-billion atom KH simulations. The current optimized ddcMD code is benchmarked at 115.1 TFlop/s in our scaling study and 103.9 TFlop/s in a sustained science run, with additional improvements ongoing. These improvements enabled us to run the first MD simulations of micron-scale systems developing the KH instability. James N. Glosli, David F. Richards, K. J. Caspersen, R. E. Rudd, John A. Gunnels, Frederick H. Streitz |
SC | 2 |