Thesis (Ph. D.)--University of Rochester. Dept. of Computer Science, 2003. Simultaneously published in the Technical Report series.
Improvements in technology have resulted in steadily improving microprocessor performance. However, the shrinking of process technologies and increasing clock speeds introduce new bottlenecks to performance, viz, long wire delays on the chip and long memory latencies. We observe a number of trade-offs in the design of various microprocessor structures and the gap between the different trade-off points only widens as technologies improve and latencies of wires and memory increase. The emergence of power as a first-order design constraint also introduces trade-offs involving performance and power consumption. Microprocessor designs are optimized to balance these trade-offs in the average case, but are highly sub-optimal for most programs that run on the processor. The dissertation evaluates hardware reconfiguration as a means to providing a program with multiple trade-off points, thereby allowing the hardware to match the program's needs at run-time. In all cases, hardware reconfiguration exploits technology trends and is relatively non-intrusive. We examine a reconfigurable cache layout that varies the L1 data cache size and helps handle the trade-off between cache capacity and access time. We also study a highly clustered and communication-bound processor, where a subset of the total clusters yields optimal performance by balancing the extraction of distant parallelism with the inter-cluster communication costs. In a processor with limited resources, distant parallelism can be mined with the help of a pre-execution thread and the allocation of resources between the primary and pre-execution thread determines the trade-off between nearby and distant parallelism. In all of these cases, the dynamic management of on-chip resources can balance the different trade-offs. We propose and evaluate dynamic adaptation algorithms that detect changes in program behavior and select optimal hardware configurations. Our results demonstrate that the adaptation algorithms are very effective in adapting to changes in program behavior, allowing improved processor efficiency through hardware reconfiguration. Performance is improved and power consumption is reduced when compared with a static hardware design.
