Optimized Multi-threading To Balance Energy and Performance Efficiency
Sri Vidya B1, Harini Sriraman2, Rukmani P3
1Sri vidya B, MCA from Vellore Institute of Technology. She has completed her BCA from Madras University with distinction. Chennai, (Tamil Nadu), India.
2Dr. Harini Sriraman, B.E from Madras University and M.E from College of Engineering, Guindy, Chennai, (Tamil Nadu), India.
3Dr. P Rukmani, B.Tech IT from Madras University and M.Tech IT from College of Engineering, Guindy, Chennai, (Tamil Nadu), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 599-604 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2385037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Energy efficiency is an important aspect of high performance computing today. Energy is the integration of power over time. Power consumption in a system depends on power consumption of processing units, memory and other peripherals. One of the recent advancements in energy efficiency is through parallel computing. Ideally in a system the number of software threads should be equal to the number of hardware threads. But in real time systems the ideal ratio cannot be always maintained. Moreover the ideal value will change depending on the workload and the dynamic characteristics of the system. In this work, a detailed study to understand the effect of multi-threading on power efficiency is carried out. The results of these benchmark analysis show optimal number of threads for different categories of workload, to achieve a fine balance between energy efficiency and performance. These results of the analysed benchmark applications are stored in secondary disks. When a new application is submitted for execution on the system, around 12 characteristics of the submitted application is compared with the analysed benchmark applications. Analysed benchmark application with the least hamming distance from the submitted application is chosen and its corresponding optimal thread value is read from the storage. This data is communicated to the compiler of the submitted application for improving the balance between energy efficiency and performance. For the experimental analysis the compilers of C and Java are used. The results show an improved power efficiency of up to 30% when optimal numbers of threads are used.
Keywords: Energy Efficiency, Power dissipation, Multi-threading, Benchmarks, Power efficiency.
Scope of the Article: RF Energy Harvesting