Demo Data from EDBT 2026 / reviewers in the wild / expert
Nikolai Sakharnykh
dblp:157/2858
· DBLP profile ↗
1ranked-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 · 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 |
Integrated circuit design · 46% High-performance computing · 23% Energy-efficient computing · 23% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › supercomputing
exascale computing |
0.2 | 1 | 2014 | Scaling the Power Wall: A Path to Exascale · SC 2014 |
Integrated circuit design
low-power circuit design |
0.2 | 1 | 2014 | Scaling the Power Wall: A Path to Exascale · SC 2014 |
Integrated circuit design › memory circuit design
low-voltage SRAM |
0.2 | 1 | 2014 | Scaling the Power Wall: A Path to Exascale · SC 2014 |
Energy-efficient computing
power wall |
0.2 | 1 | 2014 | Scaling the Power Wall: A Path to Exascale · SC 2014 |
Methods — techniques the papers use, named apart from their topics
power modeling · 0.2architectural simulation · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Scaling the Power Wall: A Path to ExascaleabstractModern scientific discovery is driven by an insatiable demand for computing performance. The HPC community is targeting development of supercomputers able to sustain 1 ExaFlops by the year 2020 and power consumption is the primary obstacle to achieving this goal. A combination of architectural improvements, circuit design, and manufacturing technologies must provide over a 20× improvement in energy efficiency. In this paper, we present some of the progress NVIDIA Research is making toward the design of Exascale systems by tailoring features to address the scaling challenges of performance and energy efficiency. We evaluate several architectural concepts for a set of HPC applications demonstrating expected energy efficiency improvements resulting from circuit and packaging innovations such as low-voltage SRAM, low-energy signalling, and on-package memory. Finally, we discuss the scaling of these features with respect to future process technologies and provide power and performance projections for our Exascale research architecture. Oreste Villa, Daniel R. Johnson, Mike O'Connor, Evgeny Bolotin, David W. Nellans, Justin Luitjens, Nikolai Sakharnykh, Paulius Micikevicius, Anthony Scudiero, Stephen W. Keckler, William J. Dally |
SC | 7 |