Microscale thermal engineering of electronic systems

The electronics industry is encountering thermal challenges and opportunities with lengthscales comparable to or much less than one micrometer. Examples include nanoscale phonon hotspots in transistors and the increasing temperature rise in onchip interconnects. Millimeter-scale hotspots on microprocessors, resulting from varying rates of power consumption, are being addressed using two-phase microchannel heat sinks. Nanoscale thermal data storage technology has received much attention recently. This paper provides an overview of these topics with a focus on related research at Stanford University.

Nanoscale heat transfer and information technology

The summary from Goodson’s group on their recent work on heat transfer issues in the microelectronics and data storage industries illustrate the critical role of heat transfer for some areas of information technology. In this article, we build on their work and discuss some directions worthy of further research.

Ameliorating the Overhead of Dynamic Optimization

Dynamic optimization has several key advantages. This includes the ability to work on binary code in the absence of sources and to perform optimization across module boundaries. However, it has a significant disadvantage viz-a-viz traditional static optimization: it has a significant runtime overhead. There can be performance gain only if the overhead can be amortized. In this paper, we will quantitatively analyze the runtime overhead introduced by a dynamic optimizer, DynamoRIO. We found that the major overhead does not come from the optimizer's operation. Instead, it comes from the extra code in the code cache added by DynamoRIO. After a detailed analysis, we will propose a method of trace construction that ameliorate the overhead introduced by the dynamic optimizer, thereby reducing the runtime overhead of DynamoRIO. We believe that the result of the study as well as the proposed solution is applicable to other scenarios such as dynamic code translation and managed execution that utilizes a framework similar to that of dynamic optimization.