Energy conscious scheduling with controlled threshold for precedence-constrained tasks on heterogeneous clusters

2016 ◽  
Vol 25 (3) ◽  
pp. 276-286 ◽  
Author(s):  
Nirmal Kaur ◽  
Savina Bansal ◽  
Rakesh Kumar Bansal

Efficient task scheduling of concurrent tasks is one of the primary requirements for high-performance computing platforms. Recent advances in high-performance computing have resulted in widespread performance improvement though at the cost of increased energy consumption and other system resources. In this article, an energy conscious scheduling algorithm with controlled threshold has been developed for precedence-constrained tasks on heterogeneous cluster, which aims at lower makespan along with reduced energy consumption. Energy conscious scheduling with controlled threshold algorithm combines the benefits of dynamic voltage scaling with controlled threshold-based duplication strategy to achieve its objectives. Effectiveness of the proposed algorithm is analyzed in comparison with available duplication- and non-duplication-based scheduling algorithms (with and without dynamic voltage scaling approach) to ascertain its performance and energy consumption. Exhaustive simulation results on random and real-world graphs demonstrate that energy conscious scheduling algorithm with controlled threshold has the potential to reduce energy consumption and makespan.

2022 ◽  
Vol 21 ◽  
pp. 23-30
Author(s):  
E. M. Karanikolaou ◽  
M. P. Bekakos

The need for new and more reliable metrics is always in demand. In this paper, a new metric is proposed for the evaluation of high performance computing platforms in conjunction with their energy consumption. The aim of the new metric is to reliably compare different HPC systems concerning their energy efficiency. The metric provides a mean to rank supercomputers of similar capabilities, avoiding the misleading results of metrics like performance-per-watt, currently used for ranking systems, as in the Green500 list, where systems with totally different sizes and capabilities are ranked consecutively. An example of this misuse for two adjacent systems in the Green500 list, is discussed. A comparative study for the energy efficiency of three high performance computing platforms, with different architectures, using the proposed metric is presented.


2007 ◽  
Vol 16 (05) ◽  
pp. 745-767
Author(s):  
SUMITKUMAR N. PAMNANI ◽  
DEEPAK N. AGARWAL ◽  
GANG QU ◽  
DONALD YEUNG

Performance-enhancement techniques improve CPU speed at the cost of other valuable system resources such as power and energy. Software prefetching is one such technique, tolerating memory latency for high performance. In this article, we quantitatively study this technique's impact on system performance and power/energy consumption. First, we demonstrate that software prefetching achieves an average of 36% performance improvement with 8% additional energy consumption and 69% higher power consumption on six memory-intensive benchmarks. Then we combine software prefetching with a (unrealistic) static voltage scaling technique to show that this performance gain can be converted to an average of 48% energy saving. This suggests that it is promising to build low power systems with techniques traditionally known for performance enhancement. We thus propose a practical online profiling based dynamic voltage scaling (DVS) algorithm. The algorithm monitors system's performance and adapts the voltage level accordingly to save energy while maintaining the observed system performance. Our proposed online profiling DVS algorithm achieves 38% energy saving without any significant performance loss.


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