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Swimming training elicits significant reductions in arterial blood pressure at rest in individuals with hypertension. This is a clinically important finding since swimming can be a highly useful alternative to land-based exercises for hypertensive patients with obesity, exercise-induced asthma, or orthopedic injuries.

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Applications on today's high-end systems typically make varying load demands over time. A single application may have many different phases during its lifetime, and workload mixes show interleaved phases. Memory-intensive work or phases may exhibit performance saturation at frequencies below the maximum possible for the processors due to the disparity between processor and memory speeds. Performance saturation is a sign of overprovisioning and leads to energy-inefficient systems. Computers using heterogeneous processors, with the same ISA, but different implementation details, have been proposed as a way of reducing power while avoiding or limiting performance degradation. However, using heterogeneous processors effectively is complicated and requires intelligent scheduling.The research reported here explores the use of a heterogeneous system of processors with identical ISAs and implementation details, but with differing voltages and frequencies. The scheduler uses the execution characteristics of each application to predict its future processing needs and then schedule it to a processor which matches those needs if one is available. The predictions are used to minimize the performance loss to the system as a whole rather than that of a single application. The result limits system power while minimizing total performance loss. A prototype implementation on a Power4 four-processor system is presented. The prototype scheduler is validated using both synthetic and real-world benchmarks. The prototype shows reasonable predictor accuracy and significant power savings for memory-bound applications.