Collaborative compilation

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 105-110).Modern optimizing compilers use many heuristic solutions that must be tuned empirically. This tuning is usually done "at the factory" using standard benchmarks. However, applications that are not in the benchmark suite will not achieve the best possible performance, because they are not considered when tuning the compiler. Collaborative compilation alleviates this problem by using local profiling information for at-the-factory style training, allowing users to tune their compilers based on the applications that they use most. It takes advantage of the repeated compilations performed in Java virtual machines to gather performance information from the programs that the user runs. For a single user, this approach may cause undue overhead; for this reason, collaborative compilation allows the sharing of profile information and publishing of the results of tuning. Thus, users see no performance degradation from profiling, only performance improvement due to tuning. This document describes the challenges of implementing collaborative compilation and the solutions we have developed. We present experiments showing that collaborative compilation can be used to gain performance improvements on several compilation problems. In addition, we relate collaborative compilation to previous research and describe directions for future work.by Benjamin R. Wagner.M.Eng.and S.B

Automating the construction of a complier heuristics using machine learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 153-162).Compiler writers are expected to create effective and inexpensive solutions to NP-hard problems such as instruction scheduling and register allocation. To make matters worse, separate optimization phases have strong interactions and competing resource constraints. Compiler writers deal with system complexity by dividing the problem into multiple phases and devising approximate heuristics for each phase. However, to achieve satisfactory performance, developers are forced to manually tweak their heuristics with trial-and-error experimentation. In this dissertation I present meta optimization, a methodology for automatically constructing high quality compiler heuristics using machine learning techniques. This thesis describes machine-learned heuristics for three important compiler optimizations: hyperblock formation, register allocation, and loop unrolling. The machine-learned heuristics outperform (by as much as 3x in some cases) their state-of-the-art hand-crafted counterparts. By automatically collecting data and systematically analyzing them, my techniques discover subtle interactions that even experienced engineers would likely overlook. In addition to improving performance, my techniques can significantly reduce the human effort involved in compiler design.(cont.) Machine learning algorithms can design critical portions of compiler heuristics, thereby freeing the human designer to focus on compiler correctness. The progression of experiments I conduct in this thesis leads to collaborative compilation, an approach which enables ordinary users to transparently train compiler heuristics by running their applications as they normally would. The collaborative system automatically adapts itself to the applications in which a community of users is interested.by Mark W. Stephenson.Ph.D

Adapting Convergent Scheduling Using Machine-Learning

Convergent scheduling is a general framework for instruction scheduling and cluster assignment for parallel, clustered architectures. A convergent scheduler is composed of many independent passes, each of which implements a specific compiler heuristic. Each of the passes shares a common interface, which allows them to be run multiple times, and in any order. Because of this, a convergent scheduler is presented with a vast number of legal pass orderings