Colloquium
Torsten Hoefler
University of Illinois at Urbana-Champaign
Monday, January 23, 2012
1230 WEB
Refreshments 3:20 p.m.
Lecture 3:40 p.m.
Host: Ganesh Gopalakrishnan
Title: Energy-aware Software Development for Massive-Scale Systems
Torsten Hoefler
University of Illinois at Urbana-Champaign
Monday, January 23, 2012
1230 WEB
Refreshments 3:20 p.m.
Lecture 3:40 p.m.
Host: Ganesh Gopalakrishnan
Title: Energy-aware Software Development for Massive-Scale Systems
Abstract
The power consumption of HPC systems is an increasing concern as large-scale systems grow in size while voltage scaling slows down and leakage current increases. It is unreasonable to expect that a single system can consume more than 20 MW, which makes the road to larger scales harder. While we may be able to solve the challenge to build an Exascale machine in this power-budget, it's unclear if practical algorithms and implementations can operate at the required power-efficiency. In this talk, we show examples for power-aware analysis and modeling of parallel algorithms. We demonstrate that the network quickly becomes the key concern with regards to performance and power. We show hardware and software techniques to limit power consumption and describe how network-centric programming can potentially further mitigate those concerns. We then describe several techniques for power-aware parallel programming and power modeling as directions of future research in this area.
The power consumption of HPC systems is an increasing concern as large-scale systems grow in size while voltage scaling slows down and leakage current increases. It is unreasonable to expect that a single system can consume more than 20 MW, which makes the road to larger scales harder. While we may be able to solve the challenge to build an Exascale machine in this power-budget, it's unclear if practical algorithms and implementations can operate at the required power-efficiency. In this talk, we show examples for power-aware analysis and modeling of parallel algorithms. We demonstrate that the network quickly becomes the key concern with regards to performance and power. We show hardware and software techniques to limit power consumption and describe how network-centric programming can potentially further mitigate those concerns. We then describe several techniques for power-aware parallel programming and power modeling as directions of future research in this area.