Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge. This paper develops a novel resource allocation scheme for memory-bound applications running on High-Performance Computing (HPC) clusters, aiming to improve application performance without breaching peak power constraints and total energy consumption. Our scheme estimates how the number of processor cores and CPU frequency setting affects the application performance. It then uses the estimate to provide additional compute nodes to memory-bound applications if it is profitable to do so. We implement and apply our algorithm to 12 representative benchmarks from the NAS parallel benchmark and HPC Challenge (HPCC) benchmark suites and evaluate it on a representative HPC cluster. Experimental results show that our approach can effectively mitigate memory contention to improve application performance, and it achieves this without significantly increasing the peak power and overall energy consumption. Our approach obtains on average 12.69% performance improvement over the default resource allocation strategy, but uses 7.06% less total power, which translates into 17.77% energy savings.

Reducing the energy consumption of the storage system’s disk read/write requests plays an important role in improving the overall energy efficiency of high-performance computing systems. We propose a method to reduce disk energy consumption by delaying the dispatch of disk requests to the end of a time window, which we call time window-based lazy scheduling. We prove that sorting requests within a single time window can reduce the disk energy consumption, and we discuss the relationship between the size of the time window and the disk energy consumption, proving that the energy consumption is highly likely to decrease with increasing window size. To exploit this opportunity, we propose the Lazy Scheduling based Disk Energy Optimization (LSDEO) algorithm, which adopts a feedback method to periodically adjust the size of the time window, and minimizes the local disk energy consumption by sorting disk requests within each time window. We implement the LSDEO algorithm in an OS kernel and conduct both simulations and actual measurements on the algorithm, confirming that increasing the time window increases disk energy savings. When the average request arrival rate is 300 and the threshold of average request response time is 50 ms, LSDEO can yield disk energy savings of 21.5%.

Component overclocking is an effective approach to speed up the components of a system to realize a higher program performance; it includes processor overclocking or memory overclocking. However, overclocking will unavoidably result in increase in power consumption. Our goal is to optimally improve the performance of scientific computing applications without increasing the total power consumption for a processor-memory system. We built a processor-memory energy efficiency model for multicore-based systems, which coordinates the performance and power of processor and memory. Our model exploits performance boost opportunities for a processor-memory system by adopting processor overclocking, processor Dynamic Voltage and Frequency Scaling (DVFS), memory active ratio adjustment, and memory overclocking, according to different scientific applications. This model also provides a total power control method by considering the same four factors mentioned above. We propose a processor and memory Coordination-based holistic Energy-Efficient (CEE) algorithm, which achieves performance improvement without increasing the total power consumption. The experimental results show that an average of 9.3% performance improvement was obtained for all 14 benchmarks. Meanwhile the total power consumption does not increase. The maximal performance improvement was up to 13.1% from dedup benchmark. Our experiments validate the effectiveness of our holistic energy-efficient model and technology.