The benefits of event: driven energy accounting in power-sensitive systems

By Frank Bellosa. ACM SIGOPS European Workshop, 2000

422 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/d6d5f6d731c30c427680929b03ae16d755c4b350

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In Electrical and Electronic Engineering

In Hardware and Architecture

Citations in the corpus

Into the wild: Studying real user activity patterns to guide power optimizations for mobile architectures (2009, background): “Several researchers use performance counters to estimate the power consumption of both high-performance and embedded microprocessors [4, 5, 9, 18, 19].”
PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications (2010, background): “Related efforts estimate weighted power and energy consumption of threads or applications on a single-node computer system by counting performance events and system activities from system logs or simulation traces [5], [8], [14], [20].”
Run-time power estimation in high performance microprocessors (2001, methodology): “Because of the black box approach taken in [2], component power distributions cannot be generated.”
Energy-efficient processor design using multiple clock domains with dynamic voltage and frequency scaling (2002, background): “Bellosa [2, 3] describes a scheme to associate energy usage patterns with every process in order to control energy consumption for the purposes of both cooling and battery life.”
ECOSystem: managing energy as a first class operating system resource (2002, methodology): “Ideally, one would like to charge differently for different processor behavior (e.g., various types of instructions or the frequency of cache misses, etc.) and this would be compatible with our modeling approach (e.g., by using event counters [ 2 , 17]).”
The design, implementation, and evaluation of a compiler algorithm for CPU energy reduction (2003, background): “For example, nonideal memory behavior are identified in [5, 29], which may affect the choice of the scaling factor at each scaling point.”
Data Center Energy Consumption Modeling: A Survey (2016, methodology): “example by incorporating the model into techniques such as temperature or energy aware scheduling [18], dynamic”
Full-System Power Analysis and Modeling for Server Environments (2006, background): “Current approaches to power modeling fall into two broad classes: measurements of power at the hardware level [8], [1 3], [11] or modeling of power at the simulation level [5], [7], [9 ], [14], [22], [26], [27], [29].”
Estimating mobile application energy consumption using program analysis (2013, methodology): “Complementary methods for estimating the energy usage of applications have relied on operating system level instrumentation [9], [4], together with hardware support for energy measurement.”
Self-constructive high-rate system energy modeling for battery-powered mobile systems (2011, background): “, [7, 8, 36, 37] by providing an accurate, high-rate, and adaptive energy model.”
Runtime power monitoring in high-end processors: methodology and empirical data (2003, background): “Prior work has developed counter-based or profilebased estimates for much simpler processors [3, 16, 19, 28].”
Accurate online power estimation and automatic battery behavior based power model generation for smartphones (2010, result): “In contrast, other researchers developed “black-box” microprocessor power models [5, 6] that require no knowledge of hardware component implementation.”
RAPL: Memory power estimation and capping (2010, background): “Prior work has looked extensively at using activity based modeling for processor power estimation, with its benefits and shortcomings well studied and documented [19, 20].”