First, although representative sampling is slightly less effective in characterizing average behavior than statistical sampling, it is substantially more effective in finding stress patterns.
For doing so, we borrow from sampled simulation theory and we provide two key insights. This paper closes the gap between these two extremes by studying techniques for the automated identification of stress patterns (worst-case application behaviors) in typical workloads. These workloads range from benchmarks that represent typical behavior up to hand-tuned stress benchmarks (so called stressmarks) that stress the microprocessor to its extreme power consumption.
Understanding the power characteristics of a microprocessor under design requires a careful study using a variety of workloads. Power consumption has emerged as a key design concern across the entire computing range, from low-end embedded systems to high-end supercomputers.
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