Improving register allocation for subscripted variables
PLDI '90 Proceedings of the ACM SIGPLAN 1990 conference on Programming language design and implementation
Integration, the VLSI Journal
Tile size selection using cache organization and data layout
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Dynamic feedback: an effective technique for adaptive computing
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology
ICS '97 Proceedings of the 11th international conference on Supercomputing
SPL: a language and compiler for DSP algorithms
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Optimizing compilers for modern architectures: a dependence-based approach
Optimizing compilers for modern architectures: a dependence-based approach
Adaptive Optimizing Compilers for the 21st Century
The Journal of Supercomputing
Iteration Space Tiling for Memory Hierarchies
Proceedings of the Third SIAM Conference on Parallel Processing for Scientific Computing
Compiler optimization-space exploration
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
A comparison of empirical and model-driven optimization
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Improving Software Pipelining With Unroll-and-Jam
HICSS '96 Proceedings of the 29th Hawaii International Conference on System Sciences Volume 1: Software Technology and Architecture
A Dynamically Tuned Sorting Library
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
A framework for adaptive algorithm selection in STAPL
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Automatic performance model construction for the fast software exploration of new hardware designs
CASES '06 Proceedings of the 2006 international conference on Compilers, architecture and synthesis for embedded systems
Reducing search space of auto-tuners using parallel patterns
IWMSE '09 Proceedings of the 2009 ICSE Workshop on Multicore Software Engineering
Automating the generation of composed linear algebra kernels
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Automatic creation of tile size selection models
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
Parallel memory prediction for fused linear algebra kernels
ACM SIGMETRICS Performance Evaluation Review - Special issue on the 1st international workshop on performance modeling, benchmarking and simulation of high performance computing systems (PMBS 10)
Efficiently exploring compiler optimization sequences with pairwise pruning
Proceedings of the 1st International Workshop on Adaptive Self-Tuning Computing Systems for the Exaflop Era
Toward robust real-world inference: a new perspective on explanation-based learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
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Compilers employ system models, sometimes implicitly, to make code optimization decisions. These models are analytic; they reflect their implementor's understanding and beliefs of the system. While their decisions can be made almost instantaneously, unless the model is perfect their decisions may be flawed. To avoid exercising unique characteristics of a particular machine, such models are necessarily general and conservative. An alternative is to construct an empirical model. Building an empirical model involves extensive search of a parameter space to determine optimal settings. But this search is performed on the actual machine on which the compiler is to be deployed so that, once constructed, its decisions automatically reflect any eccentricities of the target system. Unfortunately, constructing accurate empirical models is expensive and, therefore, their applicability is limited to library generators such as ATLAS and FFTW. Here the high up-front installation cost can amortized over many future uses. In this paper we examine a hybrid approach. Active learning in an Explanation-Based paradigm allows the hybrid system to greatly increase the search range while drastically reducing the search time. Individual search points are analyzed for their information content using an known-imprecise qualitative analytic model. Next-search-points are chosen which have the highest expected information content with respect to refinement of the empirical model being constructed. To evaluate our approach we compare it with a leading analytic model and a leading empirical model. Our results show that the performance of the libraries generated using the hybrid approach is comparable to the performance of libraries generated via extensive search techniques and much better than that of the libraries generated by optimization based solely on an analytic model.