Energy-aware performance analysis methodologies for HPC architectures—An exploratory study

Benedict, Shajulin

doi:10.1016/j.jnca.2012.08.003

Cited by 37 publications

(20 citation statements)

References 28 publications

(21 reference statements)

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“…Productisation of products Abram (2007), Almond and Snelling (1999), Andriole (2010), Ayanoglu (1999), Bancroft (2005), Banhazi et al (2012), Belt et al (2010), Benedict (2012), Beuren et al (2013), Bowman and Swart (2007), Cheng et al (2009), Cornelissen et al (2012, Cummings and Haruyama (1999), Czuchry andCzuchry (2009), Cruz-Neira et al (2010), Danson et al (2005), Dondi et al (2002), Durugbo and Riedel (2013), Eliezer et al (2009), Eliezer and Staszewski (2011), Farra et al (2011), Fay (2003, Ferguson andKline (1997), Fey (1985), Flamholtz (1995), Flamholtz and Aksehirli (2000), Flamholtz (2002), Flamholtz and Hua (2002), Flamholtz and Hua (2003), Flamholtz and Kurland (2005), Floricel and Miller (2003), Fujishiro et al (2011), Geisler (1993, Hanninen et al (2013aHanninen et al ( , 2013bHanninen et al ( , 2014aHanninen et al ( , 2014b, Harkonen et al (2009), Henton (2005, Hicks (2001), Hossain (2012), …”

Section: Classification Category Referencesmentioning

confidence: 99%

Productisation: A review and research agenda

Härkönen

Haapasalo

Hänninen

2015

International Journal of Production Economics

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a b s t r a c tProductisation relates to the process of analysing a need, defining and combining suitable elements, tangible and/or intangible, into a product-like defined set of deliverables that is standardised, repeatable and comprehendible. This paper clarifies the concept of productisation by surveying the extant literature and reporting the origins, characteristics, benefits and features along with aiming to support future work. The analysed literature is categorised, its content is analysed, and the need for a clear framework is highlighted.The results indicate that productisation has a specific role in addressing the challenges of being able to describe and explain complex, often abstract offerings and producing them, involving activities both to ensure adequate standards and those activities leading to selling the products. A well productised product is easier to buy, sell and market, has stronger features and may enable benefits depending on the product type. Productisation has implications through having a complementary role amongst well established functions and in strengthening the linkage of market needs and engineering-oriented development. The concept requires further development. Potential future research directions are presented.

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Section: Classification Category Referencesmentioning

confidence: 99%

Productisation: A review and research agenda

Härkönen

Haapasalo

Hänninen

2015

International Journal of Production Economics

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“…Usually, lowering frequency permits operation at smaller voltages, and this has a cubic effect on energy savings. As for multicore and manycore processors, the power consumption increases linearly with the number of cores while clock frequency increases at a much slower pace [104]. Further, the use of identical processing elements in an homogeneous architecture reduces the overall hardware complexity and verification process in the entire development cycle.…”

Section: Network On Chip Thermal and Energy Aspectsmentioning

confidence: 99%

Manycore simulation for peta-scale system design: Motivation, tools, challenges and prospects

Zarrin

Aguiar

Barraca

2017

Simulation Modelling Practice and Theory

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe architecture design of peta-scale computing systems is complex and presents lots of difficulties to designs, as current tools lack support for relevant features of future scenarios. Novel systems must be designed with great care and tools, such as manycore architecture simulators, must be adapted accordingly. However, current simulation tools are very slow, often specific-purposeoriented, suffer from various issues and are rarely able to simulate thousands of cores. The emergence of peta-scale systems and the upcoming manycore era brings nevertheless new challenges to computing systems and architectures, adding further difficulties and requirements on the development of the corresponding simulators. Furthermore, the design of architecture simulators for manycore systems involve methods and techniques from various interdisciplinary research areas, which in turn brings more challenges in different aspects. As system complexity grows, the growth of the simulation capacity is being outpaced (reaching the so called simulation wall). In this paper, we present the challenges for simulating future large scale manycore environments, and we investigate the adequacy of current modeling and simulation tools, methodologies and techniques. The aim of this work is to highlight how current approaches can best deal with the identified problems, smoothing the challenges of research in future peta-scale systems.

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“…Energy measurement and profiling approaches can be classified as hardware-based, software-based and hybrid. In the following subsections the basic characteristics of hardware-based and software-based approaches are outlined, based on two extensive survey papers [27,28].…”

Section: Energy Measurement and Profilingmentioning

confidence: 99%

Energy Efficiency of Parallel Multicore Programs

Davidović¹,

Depolli²,

Lipić³

et al. 2016

SCPE

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Abstract. The increasing energy consumption of large-scale high performance resources raises technical and economical concerns. A reduction of consumed energy in multicore systems is possible to some extent with an optimized usage of computing and memory resources that is tailored to specific HPC applications. The essential step towards more sustainable consumption of energy is its reliable measurements for each component of the system and selection of optimally configured resources for specific applications. This paper briefly surveys the current approaches for measuring and profiling power consumption in large scale systems. Then, a practical case study of a real-time power measurement of multicore computing system is presented on two real HPC applications: maximum clique algorithm and numerical weather prediction model. We assume that the computing resources are allocated in a HPC cloud on a pay-per-use basis. The measurements demonstrate that the minimal energy is consumed when all available cores (up to the scaling limit for a particular application) are used on their maximal frequencies and with threads binded to the cores.Key words: Energy efficiency, high-performance, maximum clique, numerical weather prediction AMS subject classifications. 65Y05, 68U201. Introduction. Nowadays, a large number of hard problems is being solved efficiently on large-scale computing systems such as clusters, grids and clouds. The main building blocks of such systems are highperformance computing (HPC) clusters, since they are furthermore embedded both into grid computing systems as well as cloud computing systems [1]. A great advantage of Graphical Processor Unit (GPU) accelerators over multicore platforms was demonstrated in terms of energy efficiency [2] with an overall energy consumption for an order of magnitude smaller for the GPUs. Inside the clusters, the many-core architectures, such as GPUs [3], as well as even more specialized data-flow computers [4] are getting an increasingly large supporting role but the main processing is still performed by the multi-core main processors because of simple programming model.In this research, we analyze the energy consumption profile of the processors on a two processor multi-core computer, while varying its workload.With the increasing number of components in large-scale computing systems the energy consumption is steeply increasing [5]. In computer systems, energy (measured in Joules) is represented as the electricity resource that can power the hardware components to do computation. On the other hand, power is the amount of energy consumed per unit time (measured in Joules per second or Watts). The ever-increasing energy consumption of large-scale computing systems causes higher operation costs (e.g. electricity bills) and has negative environmental impacts (e.g. carbon dioxide emissions). Therefore, when designing large-scale computing systems, the focus is often shifting from performance improvements to energy efficiency.Energy can be reduced at several levels of the distrib...

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Energy-aware performance analysis methodologies for HPC architectures—An exploratory study

Cited by 37 publications

References 28 publications

Productisation: A review and research agenda

Productisation: A review and research agenda

Manycore simulation for peta-scale system design: Motivation, tools, challenges and prospects

Energy Efficiency of Parallel Multicore Programs

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