Demo Data from VLDB 2026 / reviewers in the wild / expert
Dmitry Knyaginin
dblp:154/3324
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 2 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
1 paper |
Memory systems · 91% Performance modeling and evaluation · 9% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Memory systems › hybrid memory
hybrid main memory |
0.3 | 1 | 2018 | ProFess: A Probabilistic Hybrid Main Memory Management Framework for High Performance and Fairness · HPCA 2018 |
Memory systems
memory management |
0.3 | 1 | 2018 | ProFess: A Probabilistic Hybrid Main Memory Management Framework for High Performance and Fairness · HPCA 2018 |
Memory systems
non-volatile memory |
0.3 | 1 | 2018 | ProFess: A Probabilistic Hybrid Main Memory Management Framework for High Performance and Fairness · HPCA 2018 |
Methods — techniques the papers use, named apart from their topics
probabilistic migration decision · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | ProFess: A Probabilistic Hybrid Main Memory Management Framework for High Performance and FairnessabstractNon-Volatile Memory (NVM) technologies enable cost-effective hybrid main memories with two partitions: M1 (DRAM) and slower but larger M2 (NVM). This paper considers a flat migrating organization of hybrid memories. A challenging and open issue of managing such memories is to allocate M1 among co-running programs such that high fairness is achieved at the same time as high performance. This paper introduces ProFess: a Probabilistic hybrid main memory management Framework for high performance and fairness. It comprises: i) a Relative-Slowdown Monitor (RSM) that enables fair management by indicating which program suffers the most from competition for M1; and ii) a probabilistic Migration-Decision Mechanism (MDM) that unlocks high performance by realizing cost-benefit analysis that is individual for each pair of data blocks considered for migration. Within ProFess, RSM guides MDM towards high fairness. We show that for the multiprogrammed workloads evaluated, ProFess improves fairness by 15% (avg.; up to 29%), compared to the state-of-the-art, while outperforming it by 12% (avg.; up to 29%). Dmitry Knyaginin, Vassilis Papaefstathiou, Per Stenström |
HPCA | 1 |
| 2014 | Crystal: A Design-Time Resource Partitioning Method for Hybrid Main MemoryabstractNon-Volatile Memory (NVM) technologies can be used to reduce system-level execution time, energy, or cost but they add a new design dimension. Finding the best amounts of DRAM and NVM in hybrid main memory systems is a nontrivial design-time issue, the best solution to which depends on many factors. Such resource partitioning between DRAM and NVM can be framed as an optimization problem where the minimum of a target metric is sought, trends matter more than absolute values, and thus the precision of detailed modeling is overkill. Here we present Crystal, an analytic approach to early and rapid design-time resource partitioning of hybrid main memories. Crystal provides first-order estimates of system-level execution time and energy, sufficient to enable exhaustive search of the best amount and type of NVM for given workloads and partitioning goals. Crystal thus helps system designers to quickly find the most promising hybrid configurations for detailed evaluation. E.g., Crystal shows how for specific workloads higher system-level performance and energy efficiency can be achieved by employing an NVM with the speed and energy consumption of NAND Flash instead of a much faster and more energy efficient NVM like phase-change memory. Dmitry Knyaginin, Georgi Gaydadjiev, Per Stenström |
ICPP | 1 |