Demo Data from EDBT 2026 / reviewers in the wild / expert
Michael Frechtling
dblp:162/5784
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Performance modeling and evaluation · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation › numerical algorithms › numerical error analysis
round-off error analysis |
0.2 | 1 | 2015 | MCALIB: Measuring Sensitivity to Rounding Error with Monte Carlo Programming · ACM Trans. Program. Lang. Syst. 2015 |
Compilers and program optimization › program transformation
source-to-source transformation |
0.1 | 1 | 2015 | MCALIB: Measuring Sensitivity to Rounding Error with Monte Carlo Programming · ACM Trans. Program. Lang. Syst. 2015 |
Methods — techniques the papers use, named apart from their topics
source-to-source compilation · 0.7monte-carlo arithmetic · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | MCALIB: Measuring Sensitivity to Rounding Error with Monte Carlo ProgrammingabstractRuntime analysis provides an effective method for measuring the sensitivity of programs to rounding errors. To date, implementations have required significant changes to source code, detracting from their widespread application. In this work, we present an open source system that automates the quantitative analysis of floating point rounding errors through the use of C-based source-to-source compilation and a Monte Carlo arithmetic library. We demonstrate its application to the comparison of algorithms, detection of catastrophic cancellation, and determination of whether single precision floating point provides sufficient accuracy for a given application. Methods for obtaining quantifiable measurements of sensitivity to rounding error are also detailed. Michael Frechtling, Philip H. W. Leong |
ACM Trans. Program. Lang. Syst. | 1 |