Modeling power and energy consumption of dense matrix factorizations on multicore processors

Alonso, Pedro; Dolz, Manuel F.; Mayo, Rafael; Quintana–Ort́ı, Enrique S.

doi:10.1002/cpe.3162

Cited by 4 publications

(8 citation statements)

References 12 publications

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“…Validated energy models for specific algorithms have been reported recently [2,23]. Alonso et al [2] provided an accurate energy model for three key dense matrix factorizations.…”

Section: Related Work -Overview Of Energy Modelsmentioning

confidence: 99%

“…Validated energy models for specific algorithms have been reported recently [2,23]. Alonso et al [2] provided an accurate energy model for three key dense matrix factorizations. Malossi et al [23] focused on basic linear-algebra kernels and characterized the kernels by the number of arithmetic operations, memory accesses, reduction and barrier steps.…”

Section: Related Work -Overview Of Energy Modelsmentioning

confidence: 99%

“…Significant efforts have been devoted to developing power and energy models in literature [2,8,7,17,18,16,24,26]. However, there are no analytic models for multithreaded algorithms that are both applicable to a wide range of algorithms and comprehensively validated yet (cf.…”

Section: Introductionmentioning

confidence: 99%

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ICE: A General and Validated Energy Complexity Model for Multithreaded Algorithms

Tran

2016

2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS)

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Like time complexity models that have significantly contributed to the analysis and development of fast algorithms, energy complexity models for parallel algorithms are desired as crucial means to develop energy efficient algorithms for ubiquitous multicore platforms. Ideal energy complexity models should be validated on real multicore platforms and applicable to a wide range of parallel algorithms. However, existing energy complexity models for parallel algorithms are either theoretical without model validation or algorithm-specific without ability to analyze energy complexity for a wide-range of parallel algorithms. This paper presents a new general validated energy complexity model for parallel (multithreaded) algorithms. The new model abstracts away possible multicore platforms by their static and dynamic energy of computational operations and data access, and derives the energy complexity of a given algorithm from its work, span and I/O complexity. The new model is validated by different sparse matrix vector multiplication (SpMV) algorithms and dense matrix multiplication (matmul) algorithms running on high performance computing (HPC) platforms (e.g., Intel Xeon and Xeon Phi). The new energy complexity model is able to characterize and compare the energy consumption of SpMV and matmul kernels according to three aspects: different algorithms, different input matrix types and different platforms. The prediction of the new model regarding which algorithm consumes more energy with different inputs on different platforms, is confirmed by the experimental results. In order to improve the usability and accuracy of the new model for a wide range of platforms, arXiv:1605.08222v2 [cs.DC] 4 Oct 2016 the platform parameters of ICE model are provided for eleven platforms including HPC, accelerator and embedded platforms.

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“…Validated energy models for specific algorithms have been reported recently [2,23]. Alonso et al [2] provided an accurate energy model for three key dense matrix factorizations.…”

Section: Related Work -Overview Of Energy Modelsmentioning

confidence: 99%

Section: Related Work -Overview Of Energy Modelsmentioning

confidence: 99%

See 1 more Smart Citation

ICE: A General and Validated Energy Complexity Model for Multithreaded Algorithms

Tran

2016

2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS)

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“…One of the main approaches to understand the energy complexity of algorithms is to devise energy models. Significant efforts have been devoted to developing power and energy models in literature [17,41,40,92,93,86,109,123]. However, there are no analytic models for multithreaded algorithms that are both applicable to a wide range of algorithms and comprehensively validated yet (cf.…”

Section: Introductionmentioning

confidence: 99%

“…The energy complexity model ICE proposed in this study is for general multithreaded algorithms and validated on three aspects: different algorithms for a given problem, different input types and different platforms. The proposed model is an analytic model which characterizes both algorithms (e.g., representing algorithms by their work, span and I/O complexity) and platforms (e.g., representing platforms by their static and dynamic energy No General Yes POET [86] No General Yes Koala [123] No General Yes Roofline [41,40] No General Yes Energy scalability [92,93] Yes General No Sequential energy complexity [116] No General Yes Alonso et al [17] Yes Algorithm-specific Yes Malossi et al [104] Yes Algorithm-specific Yes ICE model (this study) Yes General Yes of memory accesses and computational operations). By considering work, span and I/O complexity, the new ICE model is applicable to any multithreaded algorithms.…”

Section: Introductionmentioning

confidence: 99%

D2.4 Report on the final prototype of programming abstractions for energy-efficient inter-process communication

Ha¹,

Tran²,

Ibrahim³

et al. 2018

Preprint

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Work package 2 (WP2) aims to develop libraries for energy-efficient inter-process communication and data sharing on the EXCESS platforms. The Deliverable D2.4 reports on the final prototype of programming abstractions for energy-efficient interprocess communication. Section 1 is the updated overview of the prototype of programming abstraction and devised power/energy models. The Section 2-6 contain the latest results of the four studies:D2.4: Report on the final prototype of programming abstractions 6 D2.3, this model proposed using Ideal Cache memory model to compute I/O complexity of the algorithms. Besides a case study of SpMV to demonstrate how to apply the ICE model to find energy complexity of parallel algorithms, Deliverable D2.4 also reports a case study to apply the ICE model to Dense Matrix Multiplication (matmul). The model is then validated with both data-intensive (i.e., SpMV) and computationintensive (i.e., matmul) algorithms according to three aspects: different algorithms, different input types/sizes and different platforms. In order to make the reading flow easy to follow, we include in this report a complete study of ICE model along with latest results.D2.4: Report on the final prototype of programming abstractions Contents 1.3 Energy Model on CPU for Lock-free Data-structures in Dynamic EnvironmentsIn this section, we firstly consider the modeling and the analysis of the performance of lockfree data structures. Then, we combine the perfomance analysis with our power model that is introduced in D2.1 [75] and D2.3 [73] to estimate the energy efficiency of lock-free data structures that are used in various settings.Lock-free data structures are based on retry loops and are called by application-specific routines. In contrast to the model and analysis provided in D2.3, we consider here the lock-free data structures in dynamic environments. The size of each of the retry loops, and the size of the application routines invoked in between, are not constant but may change dynamically.We present two analytical frameworks for calculating the performance of lock-free data structures. The new frameworks follow two different approaches. The first framework, the simplest one, is based on queuing theory. It introduces an average-based approach that facilitates a more coarse-grained analysis, with the benefit of being ignorant of size distributions. Because of this independence from the distribution nature it covers a set of complicated designs. The second approach, instantiated with an exponential distribution for the size of the application routines, uses Markov chains, and is tighter because it constructs stochastically the execution, step by step.Both frameworks provide a performance estimate which is close to what we observe in practice. We have validated our analysis on (i) several fundamental lock-free data structures such as stacks, queues, deques and counters, some of them employing dynamic helping mechanisms, and (ii) synthetic tests covering a wide range of possible lock-free designs. We show the ap...

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Are our dense linear algebra libraries energy-friendly?

et al. 2014

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Registro de acceso restringido Este recurso no está disponible en acceso abierto por política de la editorial. No obstante, se puede acceder al texto completo desde la Universitat Jaume I o si el usuario cuenta con suscripción. Registre d'accés restringit Aquest recurs no està disponible en accés obert per política de l'editorial. No obstant això, es pot accedir al text complet des de la Universitat Jaume I o si l'usuari compta amb subscripció. Restricted access item This item isn't open access because of publisher's policy. The full--text version is only available from Jaume I University or if the user has a running suscription to the publisher's contents.

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Modeling power and energy consumption of dense matrix factorizations on multicore processors

Cited by 4 publications

References 12 publications

ICE: A General and Validated Energy Complexity Model for Multithreaded Algorithms

ICE: A General and Validated Energy Complexity Model for Multithreaded Algorithms

D2.4 Report on the final prototype of programming abstractions for energy-efficient inter-process communication

Are our dense linear algebra libraries energy-friendly?

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