A significance-driven programming framework for energy-constrained approximate computing

Abstract

Approximate execution is a viable technique for energy-constrained environments, provided that applications have the mechanisms to produce outputs of the highest possible quality within the given energy budget.We introduce a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows users to express the relative importance of computations for the quality of the end result, as well as minimum quality requirements. The significance-aware runtime system uses an application-specific analytical energy model to identify the degree of concurrency and approximation that maximizes quality while meeting user-specified energy constraints.Evaluation on a dual-socket 8-core server shows that the proposed framework predicts the optimal configuration with high accuracy, enabling energy-constrained executions that result in significantly higher quality compared to loop perforation, a compiler approximation technique.

Towards an Approximation-Aware Computational Workflow Framework for Accelerating Large-Scale Discovery Tasks

“…In approximate computing the aim is to create a approximate version of a calculation (surrogate) and dynamically substitute it, where appropriate, in place of the exact or "golden" calculation. Typically, approximate computing deals with functions within a single HPC application, replacing them with optimized variants at the cost of accuracy [28,30,32,39,46,48]. In general, it aims to address three major technical challenges: a) identifying calculations for which to create surrogates [30,48]; b) generating well-performing surrogates [32,39]; and c) deciding when to replace a calculation with one of its surrogates (adjudication) [28,46].…”

Optimization of program execution using computational significance

“…The results presented in this Thesis have been partially published in [100,71,102,101,99,98]. This appendix discusses my contribution to each of the aforementioned publications.…”

“…For [101,99], I contributed the analytical models, designed and applied the machine learning approach, performed benchmarking, and analysed the results of the experimental campaigns. I also contributed to the design of the approximation extensions for our significance aware computing programming model.…”

System software techniques to enhance reliability of modern platforms

“…The results presented in this Thesis have been partially published in [68,97,99,141,140,139,138,96]. This appendix discusses my contribution to each of the aforementioned publications.…”

“…For [141,140,139,138], I performed benchmarking, and analysed the results of the experimental campaigns. I also contributed to the design of the approximation extensions for our significance aware computing programming model.…”