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  • >[Colloquium] Robustness in Depth: Rebalancing Efficiency and Reliability in the Computational Stack

[Colloquium] Robustness in Depth: Rebalancing Efficiency and Reliability in the Computational Stack

December 4, 2009

Watch Colloquium: 

The talk can be found here. (PDF)
Quicktime file (380 MB)
AVI file (373 MB)


  • Date: Friday, December 4th, 2009 
  • Time: 12 pm — 12:50 pm
  •  Place: Centennial Engineering Center, Room 1041


Da
vid Ackley
Associate Professor 
Dept. of Computer Science University of New Mexico

Abstract: The growth of the serial digital computer—making a CPU faster with a memory vaster—has now stalled, even as our ability to manufacture more, denser, and cheaper chips continues to expand. Although the efficiency of parallel architectures for general-purpose computation is often questioned, parallel hardware also offers the possibility of improving computational robustness. The future, somehow, will be massively parallel and distributed, components will come and go while computations continue, and—though our ability to pose ever larger computations will remain prodigious—it will often be as important to spend effort on robustness as on efficiency.

In this talk I will suggest that the traditional roles assigned to computer hardware and computer software—so revolutionary in the middle of the last century—are increasingly counterproductive and need to be renegotiated. Although hints of that process can already be seen, I will argue that for computer science as well as society at large, we would be better off recognizing the sea change that is now upon us.

A tabletop computational grid involving dozens of processors will be assembled and demonstrated.

Bio: David Ackley received his Ph.D. in Computer Science from Carnegie Mellon, and was a member of the technical staff at Bellcore before joining the faculty at UNM. Research interests include artificial life and the connections between computation and biology, distributed and adaptive systems, and making things that do things by themselves.