Vivekanand Vellanki

dblp:116/7940 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 1999
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 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
Storage systems · 61% High-performance computing · 30% Performance modeling and evaluation · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Storage systems › i/o optimization › i/o prefetching
disk prefetching
0.011999
A Cost-Benefit Scheme for High Performance Predictive Prefetching · SC 1999
Storage systems › i/o optimization
i/o prefetching
0.011999
A Cost-Benefit Scheme for High Performance Predictive Prefetching · SC 1999
High-performance computing
parallel i/o
0.011999
A Cost-Benefit Scheme for High Performance Predictive Prefetching · SC 1999
Performance modeling and evaluation
workload characterization
0.011999
A Cost-Benefit Scheme for High Performance Predictive Prefetching · SC 1999

Methods — techniques the papers use, named apart from their topics

probability tree · 0.0cost-benefit analysis · 0.0
YearPublicationVenuePosition
1999 A Cost-Benefit Scheme for High Performance Predictive Prefetching
abstract
High-performance computing systems will increasingly rely on prefetching data from disk to overcome long disk access times and maintain high utilization of parallel I/O systems. This paper evaluates a prefetching technique that chooses which blocks to prefetch based on their probability of access and decides whether to prefetch a particular block at a given time using a cost-benefit analysis. The algorithm uses a probability tree to record past accesses and to predict future access patterns. We simulate this prefetching algorithm with a variety of I/O traces. We show that our predictive prefetching scheme combined with simple one-block-lookahead prefetching produces good performance for a variety of workloads. The scheme reduces file cache miss rates by up to 36% for workloads that receive no benefit from sequential prefetching. We show that the memory requirements for building the probability tree are reasonable, requiring about a megabyte for good performance. The probabilit...
Vivekanand Vellanki, Ann L. Chervenak
SC1