Machine-Dependent Optimization is a code generation technique that performs target-architecture-specific improvements to the generated code by exploiting detailed knowledge of the processor's instruction set, pipeline characteristics, and register allocation constraints.
46
Publications
1.8K
Citations
87
Authors
42
Institutions
Publications per year
2017–2026
1
Parents
Publications
46
POET: Parameterized Optimizations for Empirical Tuning
Qing Yi, Keith Seymour, Haihang You et al. · 2007 · 109 citations · Full text
1–5 of 46
Authors
87
- JW
4
h-index
- CW
4
h-index
- MG
3
h-index
- RB
2
h-index
- PMPhitchaya Mangpo Phothilimthana
University of California, Berkeley, United States
2 publications · 93 citations
2
h-index
1–5 of 87
Institutions
42
University of Arizona
Tucson, United States
6 h-index
8 publications
467 citations
University of Washington
Seattle, United States
5 h-index
6 publications
271 citations
University of California, Berkeley
Berkeley, United States
4 h-index
7 publications
372 citations
Rice University
Houston, United States
2 h-index
2 publications
171 citations
Qualcomm (United States)
San Diego, United States
2 h-index
2 publications
93 citations
University of Wisconsin–Madison
Madison, United States
2 h-index
3 publications
136 citations
Case Western Reserve University
Cleveland, United States
2 h-index
2 publications
25 citations
Argonne National Laboratory
Lemont, United States
2 h-index
5 publications
104 citations
1–8 of 42
Venues
- ACM SIGPLAN Notices
3 publications · 109 citations
3
h-index
- ACM Transactions on Programming Languages and Systems
3 publications · 330 citations
3
h-index
- Communications of the ACM
3 publications · 76 citations
3
h-index
- Concurrency Practice and Experience
2 publications · 48 citations
2
h-index
- The International Journal of High Performance Computing Applications
2 publications · 119 citations
2
h-index
1–5