949 research outputs found
MiCOMP: Mitigating the Compiler Phase-Ordering Problem Using Optimization Sub-Sequences and Machine Learning
Recent compilers offer a vast number of multilayered optimizations targeting different code segments of an application. Choosing among these optimizations can significantly impact the performance of the code being optimized. The selection of the right set of compiler optimizations for a particular code segment is a very hard problem, but finding the best ordering of these optimizations adds further complexity. Finding the best ordering represents a long standing problem in compilation research, named the phase-ordering problem. The traditional approach of constructing compiler heuristics to solve this problem simply cannot cope with the enormous complexity of choosing the right ordering of optimizations for every code segment in an application.
This article proposes an automatic optimization framework we call MiCOMP, which Mitigates the Compiler Phase-ordering problem. We perform phase ordering of the optimizations in LLVM’s highest optimization level using optimization sub-sequences and machine learning. The idea is to cluster the optimization passes of LLVM’s O3 setting into different clusters to predict the speedup of a complete sequence of all the optimization clusters instead of having to deal with the ordering of more than 60 different individual optimizations. The predictive model uses (1) dynamic features, (2) an encoded version of the compiler sequence, and (3) an exploration heuristic to tackle the problem.
Experimental results using the LLVM compiler framework and the Cbench suite show the effectiveness of the proposed clustering and encoding techniques to application-based reordering of passes, while using a number of predictive models. We perform statistical analysis on the results and compare against (1) random iterative compilation, (2) standard optimization levels, and (3) two recent prediction approaches. We show that MiCOMP’s iterative compilation using its sub-sequences can reach an average performance speedup of 1.31 (up to 1.51). Additionally, we demonstrate that MiCOMP’s prediction model outperforms the -O1, -O2, and -O3 optimization levels within using just a few predictions and reduces the prediction error rate down to only 5%. Overall, it achieves 90% of the available speedup by exploring less than 0.001% of the optimization space
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption
This paper presents the interesting observation that by performing fewer of
the optimizations available in a standard compiler optimization level such as
-O2, while preserving their original ordering, significant savings can be
achieved in both execution time and energy consumption. This observation has
been validated on two embedded processors, namely the ARM Cortex-M0 and the ARM
Cortex-M3, using two different versions of the LLVM compilation framework; v3.8
and v5.0. Experimental evaluation with 71 embedded benchmarks demonstrated
performance gains for at least half of the benchmarks for both processors. An
average execution time reduction of 2.4% and 5.3% was achieved across all the
benchmarks for the Cortex-M0 and Cortex-M3 processors, respectively, with
execution time improvements ranging from 1% up to 90% over the -O2. The savings
that can be achieved are in the same range as what can be achieved by the
state-of-the-art compilation approaches that use iterative compilation or
machine learning to select flags or to determine phase orderings that result in
more efficient code. In contrast to these time consuming and expensive to apply
techniques, our approach only needs to test a limited number of optimization
configurations, less than 64, to obtain similar or even better savings.
Furthermore, our approach can support multi-criteria optimization as it targets
execution time, energy consumption and code size at the same time.Comment: 15 pages, 3 figures, 71 benchmarks used for evaluatio
Towards an Achievable Performance for the Loop Nests
Numerous code optimization techniques, including loop nest optimizations,
have been developed over the last four decades. Loop optimization techniques
transform loop nests to improve the performance of the code on a target
architecture, including exposing parallelism. Finding and evaluating an
optimal, semantic-preserving sequence of transformations is a complex problem.
The sequence is guided using heuristics and/or analytical models and there is
no way of knowing how close it gets to optimal performance or if there is any
headroom for improvement. This paper makes two contributions. First, it uses a
comparative analysis of loop optimizations/transformations across multiple
compilers to determine how much headroom may exist for each compiler. And
second, it presents an approach to characterize the loop nests based on their
hardware performance counter values and a Machine Learning approach that
predicts which compiler will generate the fastest code for a loop nest. The
prediction is made for both auto-vectorized, serial compilation and for
auto-parallelization. The results show that the headroom for state-of-the-art
compilers ranges from 1.10x to 1.42x for the serial code and from 1.30x to
1.71x for the auto-parallelized code. These results are based on the Machine
Learning predictions.Comment: Accepted at the 31st International Workshop on Languages and
Compilers for Parallel Computing (LCPC 2018
MLGOPerf: An ML Guided Inliner to Optimize Performance
For the past 25 years, we have witnessed an extensive application of Machine
Learning to the Compiler space; the selection and the phase-ordering problem.
However, limited works have been upstreamed into the state-of-the-art
compilers, i.e., LLVM, to seamlessly integrate the former into the optimization
pipeline of a compiler to be readily deployed by the user. MLGO was among the
first of such projects and it only strives to reduce the code size of a binary
with an ML-based Inliner using Reinforcement Learning.
This paper presents MLGOPerf; the first end-to-end framework capable of
optimizing performance using LLVM's ML-Inliner. It employs a secondary ML model
to generate rewards used for training a retargeted Reinforcement learning
agent, previously used as the primary model by MLGO. It does so by predicting
the post-inlining speedup of a function under analysis and it enables a fast
training framework for the primary model which otherwise wouldn't be practical.
The experimental results show MLGOPerf is able to gain up to 1.8% and 2.2% with
respect to LLVM's optimization at O3 when trained for performance on SPEC
CPU2006 and Cbench benchmarks, respectively. Furthermore, the proposed approach
provides up to 26% increased opportunities to autotune code regions for our
benchmarks which can be translated into an additional 3.7% speedup value.Comment: Version 2: Added the missing Table 6. The short version of this work
is accepted at ACM/IEEE CASES 202
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