Demo Data from VLDB 2026 / reviewers in the wild / expert
Sourav Mohapatra
dblp:401/6935
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0000-0001-5098-4069ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021
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 · 46% Embedded and real-time systems · 23% Distributed systems · 23% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems › fault tolerance
checkpointing |
0.9 | 1 | 2025 | Data Cache for Intermittent Computing Systems with Non-Volatile Main Memory · ASPLOS (2) 2025 |
Memory systems › cache › CPU cache
data cache |
0.9 | 1 | 2025 | Data Cache for Intermittent Computing Systems with Non-Volatile Main Memory · ASPLOS (2) 2025 |
Embedded and real-time systems
intermittent computing |
0.9 | 1 | 2025 | Data Cache for Intermittent Computing Systems with Non-Volatile Main Memory · ASPLOS (2) 2025 |
Memory systems
non-volatile memory |
0.9 | 1 | 2025 | Data Cache for Intermittent Computing Systems with Non-Volatile Main Memory · ASPLOS (2) 2025 |
Energy-efficient computing
energy harvesting |
0.3 | 1 | 2025 | Data Cache for Intermittent Computing Systems with Non-Volatile Main Memory · ASPLOS (2) 2025 |
Methods — techniques the papers use, named apart from their topics
write-after-read detection · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Data Cache for Intermittent Computing Systems with Non-Volatile Main MemoryabstractIntermittently-operating embedded computing platforms powered by energy harvesting must frequently checkpoint their computation state. Using non-volatile memory reduces checkpoint size by eliminating the need to checkpoint volatile memory but increases checkpoint frequency to cover Write After Read (WAR) dependencies. Additionally, non-volatile memory is significantly slower to access - while consuming more energy than its volatile counterpart - suggesting the use of a data cache. Unfortunately, existing data cache solutions do not fit the challenges of intermittent computing and often require additional hardware or software to detect WARs. In this paper, we extend the data cache by integrating it with WAR detection - dropping the need for an additional memory tracker. This idea forms the basis of NACHO: a data cache tailored to intermittent computing. NACHO, on average, reduces intermittent computing runtime overhead by 54% compared to state of the art cache-based systems. It also reduces the number of non-volatile memory writes by 82% compared to a data cache-less system, and 18% on average compared to multiple state of the art cache-based systems. Sourav Mohapatra, Vito Kortbeek, Marco Antonio van Eerden, Jochem Broekhoff, Saad Ahmed, Przemyslaw Pawelczak |
ASPLOS (2) | 1 |