EDBT 2026 Demo / reviewers in the wild / expert
Derek J. DeVries
dblp:34/3382
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1997
—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 |
Processor architecture and microarchitecture · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Processor architecture and microarchitecture
out-of-order execution |
0.0 | 1 | 1997 | Initial Results on the Performance and Cost of Vector Microprocessors · MICRO 1997 |
Processor architecture and microarchitecture
superscalar processor |
0.0 | 1 | 1997 | Initial Results on the Performance and Cost of Vector Microprocessors · MICRO 1997 |
Processor architecture and microarchitecture › vector processor
vector microprocessor |
0.0 | 1 | 1997 | Initial Results on the Performance and Cost of Vector Microprocessors · MICRO 1997 |
Compilers and program optimization › instruction scheduling
instruction-level parallelism |
0.0 | 1 | 1997 | Initial Results on the Performance and Cost of Vector Microprocessors · MICRO 1997 |
Compilers and program optimization › vectorization
vectorizing compiler |
0.0 | 1 | 1997 | Initial Results on the Performance and Cost of Vector Microprocessors · MICRO 1997 |
Methods — techniques the papers use, named apart from their topics
vector instruction encoding · 0.0performance-cost analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | Initial Results on the Performance and Cost of Vector MicroprocessorsabstractIncreasingly wider superscalar processors are experiencing diminishing performance returns while requiring larger portions of die area dedicated to control rather than datapath. As an alternative to using these processors to exploit parallelism effectively, we are investigating the viability of using single-chip vector microprocessors. This paper presents some initial results of our investigation where we compare the performance and cost of vector microprocessors to that of aggressive, out-of-order superscalar microprocessors. On the performance side, we show that vector processors are able to execute a highly parallel, integer-based application 1.5-7.3 times faster than superscalar processors can by exploiting parallelism more effectively. This ability stems from the use of vector instructions. Vector instructions exploit parallelism across loop iterations by implicitly re-scheduling operations and temporally localizing the parallelism. Vector instructions also reduce instruction bandwidth by more than an order of magnitude because they express an abundance of parallelism in a compact encoding. On the cost side we show that, to achieve these performance gains, highly parallel, integer-based vector microprocessors are no more costly to implement than existing in-order and out-of-order superscalar microprocessors. One reason for this is that the organization of a vector register file provides tremendous bandwidth without incurring a large area penalty. A second reason is that the control logic for issuing vector instructions is relatively simple. Both the performance gains and cost savings are possible because vector processors rely on a vectorizing compiler, rather than hardware, to detect parallelism and to express it in a compact form to the hardware. These initial results suggest that transferring this functionality to the compiler offers a tremendous performance/cost benefit. Corinna G. Lee, Derek J. DeVries |
MICRO | 2 |