Auto-tuning for energy usage in scientific applications

35Citations
Citations of this article
25Readers
Mendeley users who have this article in their library.

Abstract

The power wall has become a dominant impeding factor in the realm of exascale system design. It is therefore important to understand how to most effectively create software to minimize its power usage while maintaining satisfactory levels of performance. This work uses existing software and hardware facilities to tune applications to minimize for several combinations of power and performance. The tuning is done with respect to software level performance-related tunables and for processor clock frequency. These tunable parameters are explored via an offline search to find the parameter combinations that are optimal with respect to performance (or delay, D), energy (E), energy×delay (E×D) and energy×delay×delay (E×D 2). These searches are employed on a parallel application that solves Poisson's equation using stencils. We show that the parameter configuration that minimizes energy consumption can save, on average, 5.4% energy with a performance loss of 4% when compared to the configuration that minimizes runtime. © 2012 Springer-Verlag Berlin Heidelberg.

References Powered by Scopus

Real-time dynamic voltage scaling for low-power embedded operating systems

868Citations
N/AReaders
Get full text

Power-aware microarchitecture: Design and modeling challenges for next-generation microprocessors

384Citations
N/AReaders
Get full text

OSKI: A library of automatically tuned sparse matrix kernels

364Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Automated design of self-adaptive software with control-theoretical formal guarantees

123Citations
N/AReaders
Get full text

Multi objective optimization of HPC kernels for performance, power, and energy

38Citations
N/AReaders
Get full text

A survey on software methods to improve the energy efficiency of parallel computing

36Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Tiwari, A., Laurenzano, M. A., Carrington, L., & Snavely, A. (2012). Auto-tuning for energy usage in scientific applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7156 LNCS, pp. 178–187). Springer Verlag. https://doi.org/10.1007/978-3-642-29740-3_21

Readers over time

‘12‘13‘14‘15‘16‘17‘18‘19‘20‘21‘22‘23‘2402468

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 13

62%

Researcher 7

33%

Professor / Associate Prof. 1

5%

Readers' Discipline

Tooltip

Computer Science 16

73%

Engineering 3

14%

Chemical Engineering 2

9%

Agricultural and Biological Sciences 1

5%

Save time finding and organizing research with Mendeley

Sign up for free
0