Demo Data from VLDB 2026 / reviewers in the wild / expert
Michael Cheng
dblp:90/2447
· DBLP profile ↗
3ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 1 since 2021Systems, architecture and hardware · 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
1 paper |
High-performance computing · 50% Parallel and multicore computing · 28% GPUs and heterogeneous computing · 22% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Parallel and multicore computing › parallelization strategies
fine-grained parallelization |
0.2 | 1 | 2016 | Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016 |
GPUs and heterogeneous computing
GPU computing |
0.2 | 1 | 2016 | Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016 |
High-performance computing › sparse linear solver
multigrid solvers |
0.2 | 1 | 2016 | Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016 |
High-performance computing
scientific computing systems |
0.2 | 1 | 2016 | Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016 |
Parallel and multicore computing
parallel programming models |
0.1 | 1 | 2016 | Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016 |
High-performance computing
stencil computation |
0.1 | 1 | 2016 | Accelerating lattice QCD multigrid on GPUs using fine-grained parallelization · SC 2016 |
Methods — techniques the papers use, named apart from their topics
hierarchical multigrid · 0.2fine-grained parallelism mapping · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Pandemic Planning using Text Analytics on Hospital Outpatient Letters: a Case Study on Covid-19 Shielding for Rheumatology Patients
Meghna Jani, Ghada Alfattni, Maksim Belousov, Michael Cheng, Andrew S. Kanter, William G. Dixon, Goran Nenadic |
AMIA | 5 |
| 2019 | Data Requirements for a Patient-Centered Learning Health System
Lysanne Lessard, Antoine Sauré, Agnes Grudniewicz, William Gardner, Wojtek Michalowski, Kathleen Pajer, Elyse Schipper, Sinthuja Suntharalingam, Raphaël Ménard-Grenier, Michael Cheng |
AMIA | 12 |
| 2016 | Accelerating lattice QCD multigrid on GPUs using fine-grained parallelizationabstractThe past decade has witnessed a dramatic acceleration of lattice quantum chromodynamics calculations in nuclear and particle physics. This has been due to both significant progress in accelerating the iterative linear solvers using multigrid algorithms, and due to the throughput improvements brought by GPUs. Deploying hierarchical algorithms optimally on GPUs is non-trivial owing to the lack of parallelism on the coarse grids, and as such, these advances have not proved multiplicative. Using the QUDA library, we demonstrate that by exposing all sources of parallelism that the underlying stencil problem possesses, and through appropriate mapping of this parallelism to the GPU architecture, we can achieve high efficiency even for the coarsest of grids. Results are presented for the Wilson-Clover discretization, where we demonstrate up to 10x speedup over present state-of-the-art GPU-accelerated methods on Titan. Finally, we look to the future, and consider the software implications of our findings. Michael A. Clark, Bálint Joó, Alexei Strelchenko, Michael Cheng, Arjun Singh Gambhir, Richard C. Brower |
SC | 4 |