Demo Data from VLDB 2026 / reviewers in the wild / expert
K. M. Reddy
dblp:97/5638
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1995
—ORCID · unresolved
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.
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Compilers and program optimization › instruction scheduling › software pipelining
modulo scheduling |
0.0 | 1 | 1995 | Petri net versus modulo scheduling for software pipelining · MICRO 1995 |
Compilers and program optimization › instruction scheduling
software pipelining |
0.0 | 1 | 1995 | Petri net versus modulo scheduling for software pipelining · MICRO 1995 |
Methods — techniques the papers use, named apart from their topics
petri nets · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1995 | Petri net versus modulo scheduling for software pipeliningabstractSoftware pipelining is a technique that reforms the loop to improve execution time. Iterations are executed in overlapped fashion to increase parallelism. Modulo scheduling places each operation so that the schedule is legal when replicated and offset by a target initiation interval. This process is repeated with larger initiation intervals until success is achieved. Kernel recognition methods schedule operations as rapidly as possible until a pattern is recognized. These two distinctly different methods have various strengths and weaknesses. This paper explores the benefits and draw-backs of each. Vicki H. Allan, U. R. Shah, K. M. Reddy |
MICRO | 3 |