TY - GEN

T1 - Minimizing the cost of iterative compilation with active learning

AU - Ogilvie, William

AU - Petoumenos, Pavlos

AU - Wang, Zheng

AU - Leather, Hugh

N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2017/2/4

Y1 - 2017/2/4

N2 - Since performance is not portable between platforms, engineers must fine-tune heuristics for each processor in turn. This is such a laborious task that high-profile compilers, supporting many architectures, cannot keep up with hardware innovation and are actually out-of-date. Iterative compilation driven by machine learning has been shown to be efficient at generating portable optimization models automatically. However, good quality models require costly, repetitive, andextensive training which greatly hinders the wide adoption of this powerful technique.In this work, we show that much of this cost is spent collecting training data, runtime measurements for different optimization decisions, which contribute little to the final heuristic. Current implementations evaluate randomly chosen, often redundant, training examples a pre-configured, almost always excessive, number of times – a large source of wasted effort. Our approach optimizes not only the selection of training examples but also the number of samplesper example, independently. To evaluate, we construct 11 high-quality models which use a combination of optimization settings to predict the runtime of benchmarks from the SPAPTsuite. Our novel, broadly applicable, methodology is able to reduce the training overhead by up to 26x compared to an approach with a fixed number of sample runs, transforming what is potentially months of work into days.

AB - Since performance is not portable between platforms, engineers must fine-tune heuristics for each processor in turn. This is such a laborious task that high-profile compilers, supporting many architectures, cannot keep up with hardware innovation and are actually out-of-date. Iterative compilation driven by machine learning has been shown to be efficient at generating portable optimization models automatically. However, good quality models require costly, repetitive, andextensive training which greatly hinders the wide adoption of this powerful technique.In this work, we show that much of this cost is spent collecting training data, runtime measurements for different optimization decisions, which contribute little to the final heuristic. Current implementations evaluate randomly chosen, often redundant, training examples a pre-configured, almost always excessive, number of times – a large source of wasted effort. Our approach optimizes not only the selection of training examples but also the number of samplesper example, independently. To evaluate, we construct 11 high-quality models which use a combination of optimization settings to predict the runtime of benchmarks from the SPAPTsuite. Our novel, broadly applicable, methodology is able to reduce the training overhead by up to 26x compared to an approach with a fixed number of sample runs, transforming what is potentially months of work into days.

KW - Active Learning

KW - Compilers

KW - Iterative Compilation

KW - Machine Learning

KW - Sequential Analysis

U2 - 10.1109/CGO.2017.7863744

DO - 10.1109/CGO.2017.7863744

M3 - Conference contribution/Paper

SN - 9781509049325

SP - 245

EP - 256

BT - International Symposium on Code Generationand Optimization (CGO), 2017

PB - IEEE

ER -