Towards a Systematic, Pragmatic and Architecture-Aware Program Optimization Process for Complex Processors

Parello, David; Temam, Olivier; Cohen, Albert; Verdun, Jean-Marie

doi:10.1109/sc.2004.61

Cited by 25 publications

(26 citation statements)

References 16 publications

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“…An innovative approach to iterative compilation was proposed by [33] where they used performance counters to propose new optimization sequences. The proposed sequences were evaluated and the performance counters are measured to choose the new optimizations to try.…”

Section: Related Workmentioning

confidence: 99%

A methodology pruning the search space of six compiler transformations by addressing them together as one problem and by exploiting the hardware architecture details

Kelefouras

2017

Computing

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Today's compilers have a plethora of optimizations-transformations to choose from, and the correct choice, order as well parameters of transformations have a significant/large impact on performance; choosing the correct order and parameters of optimizations has been a long standing problem in compilation research, which until now remains unsolved; the separate subproblems optimization gives a different schedule/binary for each sub-problem and these schedules cannot coexist, as by refining one degrades the other. Researchers try to solve this problem by using iterative compilation techniques but the search space is so big that it cannot be searched even by using modern supercomputers. Moreover, compiler transformations do not take into account the hardware architecture details and data reuse in an efficient way.In this paper, a new iterative compilation methodology is presented which reduces the search space of six compiler transformations by addressing the above problems; the search space is reduced by many orders of magnitude and thus an efficient solution is now capable to be found. The transformations are the following: loop tiling (including the number of the levels of tiling), loop unroll, register allocation, scalar replacement, loop interchange and data array layouts. The search space is reduced a) by addressing the aforementioned transformations together as one problem and not separately, b) by taking into account the custom hardware architecture details (e.g., cache size and associativity) and algorithm characteristics (e.g., data reuse).The proposed methodology has been evaluated over iterative compilation and gcc/icc compilers, on both embedded and general purpose processors; it achieves significant performance gains at many orders of magnitude lower compilation time.

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Section: Related Workmentioning

confidence: 99%

A methodology pruning the search space of six compiler transformations by addressing them together as one problem and by exploiting the hardware architecture details

Kelefouras

2017

Computing

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“…An innovative approach to iterative compilation was proposed by Parello et al [29] where they used performance counters at each stage to propose new optimization sequences. The proposed sequences were evaluated and the measured performance counters with them were used to choose new optimizations to try.…”

Section: Related Workmentioning

confidence: 99%

Predictive Modeling in a Polyhedral Optimization Space

Park

Cavazos²,

Pouchet³

et al. 2013

Int J Parallel Prog

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Abstract-Significant advances in compiler optimization have been made in recent years, enabling many transformations such as tiling, fusion, parallelization and vectorization on imperfectly nested loops. Nevertheless, the problem of finding the best combination of loop transformations remains a major challenge. Polyhedral models for compiler optimization have demonstrated strong potential for enhancing program performance, in particular for compute-intensive applications. But existing static cost models to optimize polyhedral transformations have significant limitations, and iterative compilation has become a very promising alternative to these models to find the most effective transformations. But since the number of polyhedral optimization alternatives can be enormous, it is often impractical to iterate over a significant fraction of the entire space of polyhedrally transformed variants. Recent research has focused on iterating over this search space either with manually-constructed heuristics or with automatic but very expensive search algorithms (e.g., genetic algorithms) that can eventually find good points in the polyhedral space.In this paper, we propose the use of machine learning to address the problem of selecting the best polyhedral optimizations. We show that these models can quickly find high-performance program variants in the polyhedral space, without resorting to extensive empirical search. We introduce models that take as input a characterization of a program based on its dynamic behavior, and predict the performance of aggressive high-level polyhedral transformations that includes tiling, parallelization and vectorization. We allow for a minimal empirical search on the target machine, discovering on average 83% of the searchspace-optimal combinations in at most 5 runs. Our end-to-end framework is validated using numerous benchmarks on two multi-core platforms.

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“…For example, performance counters have been used by Cavazos et al [10] for PathScale EkoPath compiler to predict good sequences to use for that compiler. Parello et al [21] also used performance counters to identify "anomalies" and iteratively apply optimizations to a program. These techniques have been shown to out-perform static characterization methods that use fixed-length feature vectors.…”

Section: Related Workmentioning

confidence: 99%

Using graph-based program characterization for predictive modeling

Park

Cavazos

Álvarez

2012

Proceedings of the Tenth International Symposium on Code Generation and Optimization

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Using machine learning has proven effective at choosing the right set of optimizations for a particular program. For machine learning techniques to be most effective, compiler writers have to develop expressive means of characterizing the program being optimized. The current state-of-the-art techniques for characterizing programs include using a fixed-length feature vector of either source code features extracted during compile time or performance counters collected when running the program. For the problem of identifying optimizations to apply, models constructed using performance counter characterizations of a program have been shown to outperform models constructed using source code features. However, collecting performance counters requires running the program multiple times, and this "dynamic" method of characterizing programs can be specific to inputs of the program. It would be preferable to have a method of characterizing programs that is as expressive as performance counter features, but that is "static" like source code features and therefore does not require running the program.In this paper, we introduce a novel way of characterizing programs using a graph-based characterization, which uses the program's intermediate representation and an adapted learning algorithm to predict good optimization sequences. To evaluate different characterization techniques, we focus on loop-intensive programs and construct prediction models that drive polyhedral optimizations, such as auto-parallelism and various loop transformation.We show that our graph-based characterization technique outperforms three current state-of-the-art characterization techniques found in the literature. By using the sequences predicted to be the best by our graph-based model, we achieved up to 73% of the speedup achievable in our search space for a particular platform, whereas we could only achieve up to 59% by other state-of-the-art techniques we evaluated.

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Towards a Systematic, Pragmatic and Architecture-Aware Program Optimization Process for Complex Processors

Cited by 25 publications

References 16 publications

A methodology pruning the search space of six compiler transformations by addressing them together as one problem and by exploiting the hardware architecture details

A methodology pruning the search space of six compiler transformations by addressing them together as one problem and by exploiting the hardware architecture details

Predictive Modeling in a Polyhedral Optimization Space

Using graph-based program characterization for predictive modeling

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