Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Workload Design: Selecting Representative Program-Input Pairs
Proceedings of the 2002 International Conference on Parallel Architectures and Compilation Techniques
MinneSPEC: A New SPEC Benchmark Workload for Simulation-Based Computer Architecture Research
IEEE Computer Architecture Letters
Evaluating the correspondence between training and reference workloads in SPEC CPU2006
ACM SIGARCH Computer Architecture News
Analysis of redundancy and application balance in the SPEC CPU2006 benchmark suite
Proceedings of the 34th annual international symposium on Computer architecture
MiDataSets: creating the conditions for a more realistic evaluation of Iterative optimization
HiPEAC'07 Proceedings of the 2nd international conference on High performance embedded architectures and compilers
Reducing training time in a one-shot machine learning-based compiler
LCPC'09 Proceedings of the 22nd international conference on Languages and Compilers for Parallel Computing
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Feedback-directed optimization is an effective technique to improve program performance, but it may result in program performance and compiler behavior that is sensitive to both the selection of inputs used for training and the actual input in each run of the program. Cross-validation over a workload of inputs can address the input-sensitivity problem, but introduces the need to select a representative workload of minimal size from the population of available inputs. We present a compiler-centric clustering methodology to group similar inputs so that redundant inputs can be eliminated from the training workload. Input similarity is determined based on the compile-time code transformations made by the compiler after training separately on each input. Differences between inputs are weighted by a performance metric based on cross-validation in order to account for code transformation differences that have little impact on performance. We introduce the CrossError metric that allows the exploration of correlations between transformations based on the results of clustering. The methodology is applied to several SPEC benchmark programs, and illustrated using selected case studies.