MuMMI: Multiple Metrics Modeling Infrastructure

Wu, Xingfu; Lively, Charles; Taylor, Valerie; Chang, Hung-Ching; Su, Chun-Yi; Cameron, Kirk W.; Moore, Shirley; Terpstra, Dan; Weaver, Vince

doi:10.1109/snpd.2013.73

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…A similar fate occurred for the Multiple Metrics Modeling Infrastructure (MuMMI) [73] project, focused on integrating existing tools such as PAPI and PowerPack for facilitating measurement, modeling, and prediction of software for multicore systems.…”

Section: Profiling Toolsmentioning

confidence: 89%

Hardware and Software Solutions for Energy-Efficient Computing in Scientific Programming

D’Agostino

Merelli

Aldinucci

et al. 2021

Scientific Programming

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Energy consumption is one of the major issues in today’s computer science, and an increasing number of scientific communities are interested in evaluating the tradeoff between time-to-solution and energy-to-solution. Despite, in the last two decades, computing which revolved around centralized computing infrastructures, such as supercomputing and data centers, the wide adoption of the Internet of Things (IoT) paradigm is currently inverting this trend due to the huge amount of data it generates, pushing computing power back to places where the data are generated—the so-called fog/edge computing. This shift towards a decentralized model requires an equivalent change in the software engineering paradigms, development environments, hardware tools, languages, and computation models for scientific programming because the local computational capabilities are typically limited and require a careful evaluation of power consumption. This paper aims to present how these concepts can be actually implemented in scientific software by presenting the state of the art of powerful, less power-hungry processors from one side and energy-aware tools and techniques from the other one.

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Section: Profiling Toolsmentioning

confidence: 89%

Hardware and Software Solutions for Energy-Efficient Computing in Scientific Programming

D’Agostino

Merelli

Aldinucci

et al. 2021

Scientific Programming

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“…It should be mentioned that some of these non-additive PMCs such as P AP I L1 ICM and P AP I L2 ICM have been widely used in energy and performance predictive models [2,4,7,17,27,28]. These represent L1 and L2 instruction cache misses.…”

Section: Uops Executed Port Port 7 Core Uops Executed Port Data Portsmentioning

confidence: 99%

Additivity: A Selection Criterion for Performance Events for Reliable Energy Predictive Modeling

Shahid

Fahad

Reddy

et al. 2017

JSFI

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Performance events or performance monitoring counters (PMCs) are now the dominant predictor variables for modeling energy consumption. Modern hardware processors provide a large set of PMCs. Determination of the best subset of PMCs for energy predictive modeling is a nontrivial task given the fact that all the PMCs can not be determined using a single application run. Several techniques have been devised to address this challenge. While some techniques are based on a statistical methodology, some use expert advice to pick a subset (that may not necessarily be obtained in one application run) that, in experts' opinion, are significant contributors to energy consumption. However, the existing techniques have not considered a fundamental property of predictor variables that should have been applied in the first place to remove PMCs unfit for modeling energy. We address this oversight in this paper.We propose a novel selection criterion for PMCs called additivity, which can be used to determine the subset of PMCs that can potentially be used for reliable energy predictive modeling. It is based on the experimental observation that the energy consumption of a serial execution of two applications is the sum of energy consumptions observed for the individual execution of each application. A linear predictive energy model is consistent if and only if its predictor variables are additive in the sense that the vector of predictor variables for a serial execution of two applications is the sum of vectors for the individual execution of each application. The criterion, therefore, is based on a simple and intuitive rule that the value of a PMC for a serial execution of two applications is equal to the sum of its values obtained for the individual execution of each application. The PMC is branded as non-additive on a platform if there exists an application for which the calculated value differs significantly from the value observed for the application execution on the platform. The use of non-additive PMCs in a model renders it inconsistent.We study the additivity of PMCs offered by the popular state-of-the-art tools, Likwid and PAPI, by employing a detailed experimental methodology on a modern Intel Haswell multicore server CPU. We show that many PMCs in Likwid and PAPI that are widely used in models as key predictor variables are non-additive. This brings into question the reliability and the reported prediction accuracy of these models.

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“…Other profiling solutions also rely on specific hardware systems. The Multiple Metrics Modeling Infrastructure (MuMMI) [53] is one such case, building upon PAPI, PowerPack and the Prophesy [45] performance modeling and prediction framework.…”

Section: Power-aware Profiling Frameworkmentioning

confidence: 99%

Energy Measurement Tools for Ultrascale Computing: A Survey

Almeida

Arteaga

Blanco

et al. 2015

JSFI

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With energy efficiency one of the main challenges on the way towards ultrascale systems, there is a great need for access to high-quality energy consumption data. Such data would enable researchers and designers to pinpoint energy inefficiencies at all levels of the computing stack, from whole nodes down to critical regions of code. However, measurement capabilities are often missing, and significantly differ between platforms where they exist. A standard is yet to be established. To that end, this paper attempts an extensive survey of energy measurement tools currently available at both the hardware and software level, comparing their features with respect to energy monitoring.

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MuMMI: Multiple Metrics Modeling Infrastructure

Cited by 7 publications

References 10 publications

Hardware and Software Solutions for Energy-Efficient Computing in Scientific Programming

Hardware and Software Solutions for Energy-Efficient Computing in Scientific Programming

Additivity: A Selection Criterion for Performance Events for Reliable Energy Predictive Modeling

Energy Measurement Tools for Ultrascale Computing: A Survey

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