A Machine Learning Approach to Automatic Production of Compiler Heuristics
AIMSA '02 Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Compiler optimization-space exploration
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Finding effective optimization phase sequences
Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool for embedded systems
SMARTS: accelerating microarchitecture simulation via rigorous statistical sampling
Proceedings of the 30th annual international symposium on Computer architecture
Inducing heuristics to decide whether to schedule
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Finding effective compilation sequences
Proceedings of the 2004 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
A First-Order Superscalar Processor Model
Proceedings of the 31st annual international symposium on Computer architecture
Control Flow Modeling in Statistical Simulation for Accurate and Efficient Processor Design Studies
Proceedings of the 31st annual international symposium on Computer architecture
Predicting Unroll Factors Using Supervised Classification
Proceedings of the international symposium on Code generation and optimization
TurboSMARTS: accurate microarchitecture simulation sampling in minutes
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Fine-grained application source code profiling for ASIP design
Proceedings of the 42nd annual Design Automation Conference
ACME: adaptive compilation made efficient
LCTES '05 Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Probabilistic source-level optimisation of embedded programs
LCTES '05 Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Using Machine Learning to Focus Iterative Optimization
Proceedings of the International Symposium on Code Generation and Optimization
Fast, automatic, procedure-level performance tuning
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
Efficiently exploring architectural design spaces via predictive modeling
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
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
A practical method for quickly evaluating program optimizations
HiPEAC'05 Proceedings of the First international conference on High Performance Embedded Architectures and Compilers
Evaluating iterative compilation
LCPC'02 Proceedings of the 15th international conference on Languages and Compilers for Parallel Computing
Fast cycle-approximate instruction set simulation
SCOPES '08 Proceedings of the 11th international workshop on Software & compilers for embedded systems
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Comprehensive cache performance tuning with a toolset
Future Generation Computer Systems
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
Constructing an optimisation phase using grammatical evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Brainy: effective selection of data structures
Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation
Effective feature set construction for SVM-based hot method prediction and optimisation
International Journal of Computational Science and Engineering
An evaluation of different modeling techniques for iterative compilation
CASES '11 Proceedings of the 14th international conference on Compilers, architectures and synthesis for embedded systems
Approximate graph clustering for program characterization
ACM Transactions on Architecture and Code Optimization (TACO) - HIPEAC Papers
Statistical Performance Modeling in Functional Instruction Set Simulators
ACM Transactions on Embedded Computing Systems (TECS)
ACM Transactions on Embedded Computing Systems (TECS)
Predictive modeling in a polyhedral optimization space
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
Using machines to learn method-specific compilation strategies
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
Using graph-based program characterization for predictive modeling
Proceedings of the Tenth International Symposium on Code Generation and Optimization
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
Continuous learning of compiler heuristics
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
Future Generation Computer Systems
Inferred Models for Dynamic and Sparse Hardware-Software Spaces
MICRO-45 Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture
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Performance tuning is an important and time consuming task which may have to be repeated for each new application and platform. Although iterative optimisation can automate this process, it still requires many executions of different versions of the program. As execution time is frequently the limiting factor in the number of versions or transformed programs that can be considered, what is needed is a mechanism that can automatically predict the performance of a modified program without actually having to run it. This paper presents a new machine learning based technique to automatically predict the speedup of a modified program using a performance model based on the code features of the tuned programs. Unlike previous approaches it does not require any prior learning over a benchmark suite. Furthermore, it can be used to predict the performance of any tuning and is not restricted to a prior seen trans-formation space. We show that it can deliver predictions with a high correlation coefficient and can be used to dramatically reduce the cost of search.