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Milepost GCC: machine learning enabled self-tuning compiler
, 2009
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Collective optimization: A practical collaborative approach
- ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION
, 2010
"... Iterative optimization is a popular and efficient research approach to optimize programs using feedback-directed compilation. However, one of the key limitations that prevented widespread use in production compilers and day-to-day practice is the necessity to perform a large number of program runs w ..."
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Cited by 9 (1 self)
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Iterative optimization is a popular and efficient research approach to optimize programs using feedback-directed compilation. However, one of the key limitations that prevented widespread use in production compilers and day-to-day practice is the necessity to perform a large number of program runs with the same dataset and environment (architecture, OS, compiler) to test many different combinations of optimizations. In this article, we propose to overcome such a practical obstacle using collective optimization, where the task of optimizing a program or tuning default compiler optimization heuristic leverages the experience of many other users continuously, rather than being performed in isolation, and often redundantly, by each user. During this unobtrusive approach, performance information is sent to a central database after each run and statistically combined with the data from all users to suggest most profitable optimizations for a given program and an architecture, or to gradually improve default optimization level of a compiler for a given architecture. In this article, we address two key challenges of collective optimization. We show that it is possible to simultaneously learn and improve performance while avoiding long training phases. We also demonstrate how to use our approach with static compilers to learn optimizations across
Transforming GCC into a research-friendly environment: plugins for optimization tuning and reordering, function cloning and program instrumentation
- 2ND INTERNATIONAL WORKSHOP ON GCC RESEARCH OPPORTUNITIES (GROW'10)
, 2010
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Collective Mind: towards practical and collaborative auto-tuning
, 2014
"... Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they are still far from widespread production use due t ..."
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Cited by 5 (1 self)
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Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they are still far from widespread production use due to lack of native support for auto-tuning in an ever changing and complex software and hardware stack, large and multi-dimensional optimization spaces, excessively long exploration times, and lack of unified mechanisms for preserving and sharing of optimization knowledge and research material. We present a possible collaborative approach to solve above problems using Collective Mind knowledge management system. In contrast with previous cTuning framework, this modular infrastructure allows to preserve and share through
unknown title
, 2010
"... Transforming GCC into a research-friendly environment: plugins for optimization tuning and reordering, function cloning and program instrumentation ..."
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Transforming GCC into a research-friendly environment: plugins for optimization tuning and reordering, function cloning and program instrumentation
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"... Transforming GCC into a research-friendly environment: plugins for optimization tuning and reordering, function cloning and program instrumentation ..."
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Transforming GCC into a research-friendly environment: plugins for optimization tuning and reordering, function cloning and program instrumentation
Project-Team Alchemy Architectures, Languages and Compilers to Harness the End of Moore Years
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• Compiler-controlled and Compiler-hinted Voltage Scaling Approaches ……………………………………………………………………………… … 61
, 2010
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