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Benchmark design for robust profile-directed optimization (2007)
| Content Provider | CiteSeerX |
|---|---|
| Author | Berube, Paul Amaral, Jose ́ Nelson |
| Description | Profile-guided code transformations specialize program code according to the profile provided by execution on train-ing data. Consequently, the performance of the code gener-ated usind this feedback is sensitive to the selection of train-ing data. Used in this fashion, the principle behind profile-guided optimization techniques is the same as off-line learn-ing commonly used in the field of machine learning. How-ever, scant use of proper validation techniques for profile-guided optimizations have appeared in the literature. Given the broad use of SPEC benchmarks in the computer archi-tecture and optimizing compiler communities, SPEC is in a position to influence the proper evaluation and valida-tion of profile-guided optimizations. Thus, we propose an evaluation methodology appropriate for profile-guided op-timization based on cross-validation. Cross-validation is a methodology from machine learning that takes input sensi-tivity into account, and provides a measure of the general-izability of results. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2007-01-01 |
| Publisher Institution | In Standard Performance Evaluation Corporation (SPEC) Workshop |
| Access Restriction | Open |
| Subject Keyword | Profile-guided Op-timization Proper Validation Technique Program Code Compiler Community Input Sensi-tivity Train-ing Data Robust Profile-directed Optimization Machine Learning Spec Benchmark Profile-guided Code Transformation Computer Archi-tecture Benchmark Design Profile-guided Optimization Off-line Learn-ing Commonly Scant Use Evaluation Methodology Appropriate Proper Evaluation Broad Use Profile-guided Optimization Technique |
| Content Type | Text |
| Resource Type | Article |
