ABSTRACP*: The use of performance indicators for the evaluation and comparison of efficiency in service provision in the public and related sectors of the economy i s continuously developing. While they often represent a step forward, to the extent that they focus attention on the objectives of the organization in question, it is frequently suspected that they fail to take into account non-controllable environmental factors. 5% do so requires multivariate techniques of analysis. This paper compares the results of three such methods with the raw performance indicators. It confirms the importance of nomcontrollable factors but also shows that different multivariate methodsgive results which do not always agree. Understanding the properties of different approaches is essential in drawing conclusions about performance.

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The leakage power dissipation has become one of the major concerns with technology scaling. The GPGPU register file has grown in size over last decade in order to support the parallel execution of thousands of threads. Given that each thread has its own dedicated set of physical registers, these registers remain idle when corresponding threads go for long latency operation. Existing research shows that the leakage energy consumption of the register file can be reduced by under volting the idle registers to a data-retentive low-leakage voltage (Drowsy Voltage) to ensure that the data is not lost while not in use. In this paper, we develop a realistic model for determining the wake-up time of registers from various under-volting and power gating modes. Next, we propose a hybrid energy saving technique where a combination of power-gating and under-volting can be used to save optimum energy depending on the idle period of the registers with a negligible performance penalty. Our simulation shows that the hybrid energy-saving technique results in 94% leakage energy savings in register files on an average when compared with the conventional clock gating technique and 9% higher leakage energy saving compared to the state-of-art technique.

The current trend of performance growth in HPC systems is accompanied by a massive increase in energy consumption. In this paper, we introduce GreenMD, an energy-efficient framework for heterogeneous systems for LU factorization utilizing multi-GPUs. LU factorization is a crucial kernel from the MAGMA library, which is highly optimized. Our aim is to apply DVFS to this application by leveraging slacks intelligently on both CPUs and multiple GPUs. To predict the slack times, accurate performance models are developed separately for both CPUs and GPUs based on the algorithmic knowledge and manufacturer’s specifications. Since DVFS does not reduce static energy consumption, we also develop undervolting techniques for both CPUs and GPUs. Reducing voltage below threshold values may give rise to errors; hence we extract the minimum safe voltages ( V safeMin ) for the CPUs and GPUs utilizing a low overhead profiling phase and apply them before execution. It is shown that GreenMD improves the CPU, GPUs, and total energy about 59%, 21%, and 31%, respectively, while delivering similar performance to the state-of-the-art linear algebra MAGMA library.