scholarly journals Are GPUs Non-Green Computing Devices?

2018 ◽  
Vol 18 (02) ◽  
pp. e17
Author(s):  
Martín Pi Puig ◽  
Laura De Giusti ◽  
Marcelo Naiouf
Keyword(s):  
Energy Consumption ◽  
High Performance Computing ◽  
Execution Time ◽  
High Performance ◽  
Average Energy ◽  
Related Research ◽  
Instant Power ◽  
Benchmark Suite ◽  
Average Energy Consumption ◽  
Performance Computing

With energy consumption emerging as one of the biggest issues in the development of HPC (High Performance Computing) applications, the importance of detailed power-related research works becomes a priority. In the last years, GPU coprocessors have been increasingly used to accelerate many of these high-priced systems even though they are embedding millions of transistors on their chips delivering an immediate increase on power consumption necessities. This paper analyzes a set of applications from the Rodinia benchmark suite in terms of CPU and GPU performance and energy consumption. Specifically, it compares single-threaded and multi-threaded CPU versions with GPU implementations, and characterize the execution time, true instant power and average energy consumption to test the idea that GPUs are power-hungry computing devices.

scholarly journals Minimizing Energy and Computation in Long-Running Software

2021 ◽  
Vol 11 (3) ◽  
pp. 1169
Author(s):  
Erol Gelenbe ◽  
Miltiadis Siavvas
Keyword(s):  
Energy Consumption ◽  
Execution Time ◽  
High Performance ◽  
Average Energy ◽  
Computation Time ◽  
Hardware Failures ◽  
Average Energy Consumption ◽  
The One ◽  
Hardware Platforms ◽  
Time And Energy

Long-running software may operate on hardware platforms with limited energy resources such as batteries or photovoltaic, or on high-performance platforms that consume a large amount of energy. Since such systems may be subject to hardware failures, checkpointing is often used to assure the reliability of the application. Since checkpointing introduces additional computation time and energy consumption, we study how checkpoint intervals need to be selected so as to minimize a cost function that includes the execution time and the energy. Expressions for both the program’s energy consumption and execution time are derived as a function of the failure probability per instruction. A first principle based analysis yields the checkpoint interval that minimizes a linear combination of the average energy consumption and execution time of the program, in terms of the classical “Lambert function”. The sensitivity of the checkpoint to the importance attributed to energy consumption is also derived. The results are illustrated with numerical examples regarding programs of various lengths and showing the relation between the checkpoint interval that minimizes energy consumption and execution time, and the one that minimizes a weighted sum of the two. In addition, our results are applied to a popular software benchmark, and posted on a publicly accessible web site, together with the optimization software that we have developed.

scholarly journals Análise de Características Comportamentais de Aplicações OpenMP para Redução do Consumo de Energia

2018 ◽  
Author(s):  
Gabriel B. Moro ◽  
Lucas Mello Schnorr
Keyword(s):  
Operating System ◽  
Energy Consumption ◽  
High Performance Computing ◽  
High Performance ◽  
Previous Analysis ◽  
Computer Systems ◽  
Specific Knowledge ◽  
Reduce Energy Consumption ◽  
Performance Computing ◽  
Processor Frequency

Performance and energy consumption are fundamental requirements in computer systems. A very frequent challenge is to combine both aspects, searching to keep the high performance computing while consuming less energy. There are a lot of techniques to reduce energy consumption, but in general, they use modern processors resources or they require specific knowledge about application and platform used. In this paper, we propose a library that dynamically changes the processor frequency according to the application's computing behavior, using a previous analysis of its Memory-Bound regions. The results show a reduction of 1,89% in energy consumption for Lulesh application with an increase of 0,09% in runtime when we compare our approach against the governor Ondemand of the Linux Operating System.

scholarly journals Action: A New Metric for Evaluating the Energy Efficiency on High Performance Computing Platforms (ranked on Green500 List)

2022 ◽  
Vol 21 ◽  
pp. 23-30
Author(s):  
E. M. Karanikolaou ◽  
M. P. Bekakos
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Comparative Study ◽  
High Performance Computing ◽  
High Performance ◽  
Ranking Systems ◽  
Performance Per Watt ◽  
Computing Platforms ◽  
Performance Computing

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.

Runtime Adaptation Techniques for HPC Applications

2010 ◽  
pp. 583-605
Author(s):  
Edgar Gabriel
Keyword(s):  
High Performance Computing ◽  
Execution Time ◽  
High Performance ◽  
Source Code ◽  
System Software ◽  
Computing Systems ◽  
Runtime Adaptation ◽  
Abstract Data ◽  
Application Characteristics ◽  
Performance Computing

This chapter discusses runtime adaption techniques targeting high-performance computing applications. In order to exploit the capabilities of modern high-end computing systems, applications and system software have to be able to adapt their behavior to hardware and application characteristics. Using the Abstract Data and Communication Library (ADCL) as the driving example, the chapter shows the advantage of using adaptive techniques to exploit characteristics of the network and of the application. This allows to reduce the execution time of applications significantly and to avoid having to maintain different architecture dependent versions of the source code.

E-BaTS: Energy-Aware Scheduling for Bag-of-Task Applications in HPC Clusters

2015 ◽  
Vol 25 (03) ◽  
pp. 1541005
Author(s):  
Alexandra Vintila Filip ◽  
Ana-Maria Oprescu ◽  
Stefania Costache ◽  
Thilo Kielmann
Keyword(s):  
Energy Consumption ◽  
High Performance Computing ◽  
High Performance ◽  
Terms Of Trade ◽  
Exhaustive Search ◽  
Energy Aware ◽  
Trade Offs ◽  
Energy Aware Scheduling ◽  
And Performance ◽  
Performance Computing

High-Performance Computing (HPC) systems consume large amounts of energy. As the energy consumption predictions for HPC show increasing numbers, it is important to make users aware of the energy spent for the execution of their applications. Drawing from our experience with exposing cost and performance in public clouds, in this paper we present a generic mechanism to compute fast and accurate estimates for the tradeoffs between the performance (expressed as makespan) and the energy consumption of applications running on HPC clusters. We validate our approach by implementing it in a prototype, called E-BaTS and validating it with a wide variety of HPC bags-of-tasks. Our experiments show that E-BaTS produces conservative estimates with errors below 5%, while requiring at most 12% of the energy and time of an exhaustive search for providing configurations close to the optimal ones in terms of trade-offs between energy consumption and makespan.

Parallel performance of two applications in the Boeing high performance computing benchmark suite

2001 ◽  
Vol 27 (4) ◽  
pp. 457-475 ◽  
Author(s):  
Joseph W. Manke ◽  
G. David Kerlick ◽  
David Levine ◽  
Subhankar Banerjee ◽  
Eric Dillon
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Parallel Performance ◽  
Benchmark Suite ◽  
Performance Computing

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
Keyword(s):  
Energy Consumption ◽  
High Performance Computing ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Voltage Scaling ◽  
Dynamic Voltage Scaling ◽  
Heterogeneous Cluster ◽  
Dynamic Voltage ◽  
Computing Platforms ◽  
Performance Computing

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.

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