Abstract
This paper describes a platform independent optimisation approach based on feedback-directed program restructuring. We have developed two strategies that search the optimisation space by means of profiling to find the best possible program variant. These strategies have no a priori knowledge of the target machine and can be run on any platform. In this paper our approach is evaluated on three full SPEC benchmarks, rather than the kernels evaluated in earlier studies where the optimisation space is relatively small. This approach was evaluated on six different platforms, where it is shown that we obtain on average a 20.5% reduction in execution time compared to the native compiler with full optimisation. By using training data instead of reference data for the search procedure, we can reduce compilation time and still give on average a 16.5% reduction in time when running on reference data. We show that our approach is able to give similar significant reductions in execution time over a state of the art high level restructurer based on static analysis and a platform specific profile feedback directed compiler that employs the same transformations as our iterative system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bilmes, J., Asanović, K., Chin, C.W., Demmel, J.: Optimizing matrix multiply using PHiPAC: A portable, high-performance, C coding methodology. In: ICS 1997 (1997)
Bodin, F., Kisuki, T., Knijnenburg, P.M.W., O’Boyle, M.F.P., Rohou, E.: Iterative Compilation in a Non-Linear Optimisation Space. In: Profile and Feedback Directed Compilation, PACT (1998)
Burke, M., et al.: The Jalapeno Dynamic Optimizing Compiler for Java. In: Proc. of ACM 1999 Java Grande Conference (June 1999)
Chow, K., Wu, Y.: Feedback-Directed Selection and Characterization of Compiler Optimizations, FDO (1999)
Cohn, R., Lowney, P.G.: Feedback Directed Optimization in Compaq’s Compilation Tools for Alpha, FDO (1999)
Han, H., Rivera, G., Tseng, C.-W.: Software Support for Improving Locality in Scientific Codes. In: CPC (2000)
Kisuki, T., Knijnenburg, P.M.W., O’Boyle, M.F.P.: Combined Selection of Tile Sizes and Unroll Factors Using Iterative Compilation. In: PACT (2000)
McKinley, K.S., Temam, O.: A Quantative Analysis of Loop Nest Locality. In: ASPLOS (1996)
Moura, J., Johnson, J., Johnson, R., Padua, D., Prasanna, V., Puschel, M., Singer, B., Veloso, M., Xiong, J.: Generating Platform-Adapted DSP Libraries using SPIRAL. In: Proc. HPEC 2001. MIT Lincoln Laboratories (2001)
Rivera, G., Tseng, C.-W.: Data Transformations for Eliminating Conflict Misses. In: PLDI (1998)
Smith, M.: Overcoming the Challenges to Feedback-Directed Optimizations. In: Dynamo 2000 (2000)
Voss, M.J., Eigenmann, R.: A framework for remote dynamic Program Optimization. In: Dynamo (2000)
Whaley, R.C., Dongarra, J.J.: Automatically tuned linear algebra software. In: Proc. Alliance (1998)
Wolf, M.E., Maydan, D.E., Chen, D.-K.: Combining loop transformations considering caches and scheduling. Int’l. J. of Parallel Programming 26(4), 479–503 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fursin, G.G., O’Boyle, M.F.P., Knijnenburg, P.M.W. (2005). Evaluating Iterative Compilation. In: Pugh, B., Tseng, CW. (eds) Languages and Compilers for Parallel Computing. LCPC 2002. Lecture Notes in Computer Science, vol 2481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596110_24
Download citation
DOI: https://doi.org/10.1007/11596110_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30781-5
Online ISBN: 978-3-540-31612-1
eBook Packages: Computer ScienceComputer Science (R0)