Analysis, verification and design for energy aware computation

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The gigahertz race is over.  For over two decades, microprocessors have been doubling in clock speed and performance every one or two years.  In the past two years, this exponential trend has been replaced by a slow climb.  Heat is the culprit -- the technological trends that drove CPUs up in speed also drove them up in power consumption.  Heat limits the CPU and graphics processor in a desktop computer; power limits the battery life of laptop computers, PDAs, and cell phones; and large computing companies such as Google and e-bay are limited airconditioning for their warehouses full of computers.  Parallelism is the most promising solution.  The physics of the transistors that make the chips work mean that if a task can be divided between multiple CPUs, the total energy can be much less than if the task were done on a single CPU.  This is why every computer manufacturer is coming out with multi-core processors.To make effective use of parallel processors, programmers need to understand the trade-offs of parallel algorithms.  While there have been many models proposed for parallel computation, we are aware of none that take into account the fundamental trade-off between energy and time described above.  We have started looking at this problem and have found some surprising results.  For example, we can show examples of parallel algorithms that perform more operations but use less time and energy than their sequential counterparts.We propose to develop this theory of energy aware algorithm design.  We will look at how it applies to large-scale computational problems from biology and web-searching.  We will also connect this with our ongoing research into integrated circuit design.  By combining theoretical modeling with practical design, we expect to produce results that are appealing to the theoreticians and useful to computer system designers.