Analyzing the energy consumption of the storage data path

Llopis, Pablo; Dolz, Manuel F.; Blas, Javier García; Isaila, Florin; Heidari, Mohammad Reza; Kuhn, Michael

doi:10.1007/s11227-016-1729-4

Cited by 4 publications

(2 citation statements)

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“…Modeling individual storage systems covers various characteristics like energy consumption, resilience, and performance. Llopis et al [17] explored empiric means to determine power consumption for individual components with a focus on power consumption within the storage and I/O data paths. Various studies model resilience based on the distribution of data across storage devices and strategy; for example, in [21], resilience depending on RAID levels is investigated and visualized in 2D heatmaps depending on error rates of memory and storage.…”

Section: /13 2 Related Workmentioning

confidence: 99%

Cost and Performance Modeling for Earth System Data Management and Beyond

Lüttgau

Kunkel

2018

Lecture Notes in Computer Science

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Current and anticipated storage environments confront domain scientist and data center operators with usability, performance and cost challenges. The amount of data upcoming system will be required to handle is expected to grow exponentially, mainly due to increasing resolution and affordable compute power. Unfortunately, the relationship between cost and performance is not always well understood requiring considerable effort for educated procurement. Within the Centre of Excellence in Simulation of Weather and Climate in Europe (ESiWACE) models to better understand cost and performance of current and future systems are being explored. This paper presents models and methodology focusing on, but not limited to, data centers used in the context of climate and numerical weather prediction. The paper concludes with a case study of alternative deployment strategies and outlines the challenges anticipating their impact on cost and performance. By publishing these early results, we would like to make the case to work towards standard models and methodologies collaboratively as a community to create sufficient incentives for vendors to provide specifications in formats which are compatible to these modeling tools. In addition to that, we see application for such formalized models and information in I/O related middleware, which are expected to make automated but reasonable decisions in increasingly heterogeneous data centers.

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Section: /13 2 Related Workmentioning

confidence: 99%

Cost and Performance Modeling for Earth System Data Management and Beyond

Lüttgau

Kunkel

2018

Lecture Notes in Computer Science

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“…In fact, fetching data from the DRAM for an operation consumes an amount of energy which is orders of magnitude higher than the computation itself. Thus, as computation becomes more energy-efficient, the cost of data movement gradually becomes a more relevant issue (Llopis et al, 2016). To progress in this direction, time-energy models for memory-bounded, power-hungry algorithms, such as the S p MV kernel, are key pieces to enable the design of a new generation of energy-efficient memory architectures.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product

Barreda

Dolz

Castaño

2020

The International Journal of High Performance Computing Applica

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Modeling the performance and energy consumption of the sparse matrix-vector product (SpMV) is essential to perform off-line analysis and, for example, choose a target computer architecture that delivers the best performance-energy consumption ratio. However, this task is especially complex given the memory-bounded nature and irregular memory accesses of the SpMV, mainly dictated by the input sparse matrix. In this paper, we propose a Machine Learning (ML)-driven approach that leverages Convolutional Neural Networks (CNNs) to provide accurate estimations of the performance and energy consumption of the SpMV kernel. The proposed CNN-based models use a blockwise approach to make the CNN architecture independent of the matrix size. These models are trained to estimate execution time as well as total, package, and DRAM energy consumption at different processor frequencies. The experimental results reveal that the overall relative error ranges between 0.5% and 14%, while at matrix level is not superior to 10%. To demonstrate the applicability and accuracy of the SpMV CNN-based models, this study is complemented with an ad-hoc time-energy model for the PageRank algorithm, a popular algorithm for web information retrieval used by search engines, which internally realizes the SpMV kernel.

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