Demo Data from EDBT 2026 / reviewers in the wild / expert
Peter Feller
dblp:86/5152
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1997
—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 · 50% Program analysis · 25% Runtime systems and virtual machines · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Runtime systems and virtual machines
dynamic compilation |
0.0 | 1 | 1997 | Value Profiling · MICRO 1997 |
Compilers and program optimization › dynamic optimization
profile-guided optimization |
0.0 | 1 | 1997 | Value Profiling · MICRO 1997 |
Compilers and program optimization
program specialization |
0.0 | 1 | 1997 | Value Profiling · MICRO 1997 |
Program analysis › dynamic analysis › profiling
value profiling |
0.0 | 1 | 1997 | Value Profiling · MICRO 1997 |
Methods — techniques the papers use, named apart from their topics
profiling · 0.0convergent profiling · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | Value ProfilingabstractIdentifying variables as invariant or constant at compile-time allows the compiler to perform optimizations including constant folding, code specialization, and partial evaluation. Some variables, which cannot be labeled as constants, may exhibit semi-invariant behavior. A semi-invariant variable is one that cannot be identified as a constant at compile-time, but has a high degree of invariant behavior at run-time. If run-time information was available to identify these variables as semi-invariant, they could then benefit from invariant-based compiler optimizations. We examine the invariance found from profiling instruction values, and show that many instructions have semi-invariant values even across different inputs. We also investigate the ability to estimate the invariance for all instructions in a program from only profiling load instructions. In addition, we propose a new type of profiling called convergent profiling. Estimating the invariance from loads and convergent profiling are used to reduce the profiling time needed to generate an accurate value profile. The value profile can then be used to automatically guide code generation for dynamic compilation, adaptive execution, code specialization, partial evaluation and other compiler optimizations. Brad Calder, Peter Feller, Alan Eustace |
MICRO | 2 |