Power dissipation is unevenly distributed in modern microprocessors leading to localized hot spots with significantly greater die temperature than surrounding cooler regions. Excessive junction temperature reduces reliability and can lead to catastrophic failure. We examine the use of activity migration which reduces peak junction temperature by moving computation between multiple replicated units. Using a thermal model that includes the temperature dependence of leakage power, we show that sustainable power dissipation can be increased by nearly a factor of two for a given junction temperature limit. Alternatively, peak die temperature can be reduced by 12.4 Ó C at the same clock frequency. The model predicts that migration intervals of around 20-200 s are required to achieve the maximum sustainable power increase. We evaluate several different forms of replication and migration policy control.

Wireless transmission of a bit can require over 1000 times more energy than a single 32-bit computation. It would therefore seem desirable to perform significant computation to reduce the number of bits transmitted. If the energy required to compress data is less than the energy required to send it, there is a net energy savings and consequently, a longer battery life for portable computers. This paper reports on the energy of lossless data compressors as measured on a StrongARM SA-110 system. We show that with several typical compression tools, there is a net energy increase when compression is applied before transmission. Reasons for this increase are explained, and hardwareaware programming optimizations are demonstrated. When applied to Unix compress, these optimizations improve energy efficiency by 51%. We also explore the fact that, for many usage models, compression and decompression need not be performed by the same algorithm. By choosing the lowest-energy compressor and decompressor on the test platform, rather than using default levels of compression, overall energy to send compressible web data can be reduced 31%. Energy to send harder-to-compress English text can be reduced 57%. Compared with a system using a single optimized application for both compression and decompression, the asymmetric scheme saves 11% or 12% of the total energy depending on the dataset.

Wireless transmission of a bit can require over 1000 times more energy than a single 32-bit computation. It may therefore be desirable to perform additional computation to reduce the number of bits transmitted. If the energy required to compress data is less than the energy required to send it, there is a net energy savings and consequently, a longer battery life for portable computers. This thesis is a study of the energy profiles of lossless data compression algorithms. Several distinct algorithms have been selected and are measured on a StrongARM SA-1 10 processor. This work demonstrates that with several typical compression tools, there is a net energy increase when compression is applied before transmission. Reasons for this increase are explained and suggestions are made to avoid it. Compression and decompression need not be performed by the same algorithm. By choosing the lowest-energy compressor and decompressor on the test platform, rather than using default levels of compression, overall energy to send data can be reduced 57%. Compared with a system using the same optimized application for both compression and decompression, the asymmetric scheme saves 11% of the total energy.

Abstract

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