A dynamic compilation framework for controlling microprocessor energy and performance

By Qiang Wu, Margaret Martonosi, Douglas W. Clark, Vijay Janappa Reddi, D. Connors, Youfeng Wu, Jin Lee, David M. Brooks. 2005

229 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Hardware and Architecture, ML Systems & Hardware

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In Electrical and Electronic Engineering

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Citations in the corpus

Chasing Carbon: The Elusive Environmental Footprint of Computing (2020, background): “Their work includes system-performance increases, power management and energy-efﬁciency optimization through dynamic voltage and frequency scaling (DVFS) [99], [100], [104]–[114], and specialized hardware [4]–[9].”
Techniques for Multicore Thermal Management: Classification and New Exploration (2006, background): “Formal feedback control has recently found numerous applications in architecture and systems [33, 34, 36, 37, 38].”
Dynamo: Facebook's Data Center-Wide Power Management System (2016, background): “Based on their control goals, they fall into the following groups: peak power management approaches [4], [8], [26]; thermal management approaches [9], [10], [27]; and average power or energy management approaches [28], [29], [30], [32], [33].”
Dynamic-Compiler-Driven Control for Microprocessor Energy and Performance (2006, background): “More details on this can be found in [15].”
Live, Runtime Phase Monitoring and Prediction on Real Systems with Application to Dynamic Power Management (2006, methodology): “[28] make use of event counter information to assign application routines to different DVFS settings under a dynamic instrumentation framework [19].”
GPUWattch: enabling energy optimizations in GPGPUs (2013, unknown): “Prior work [14, 37] leveraged memory/compute phase behavior for CPU energy optimizations.”
Tradeoffs between power management and tail latency in warehouse-scale applications (2014, background): “Both more complex phase detection mechanisms [12] and frequency selection policies [32] are certainly possible (and have been summarized previously [14]), but are not the purpose of this paper.”
Architecture support for disciplined approximate programming (2012, background): “Software efforts have explored managing energy as an explicit resource [30], shutting down unnecessary hardware, and energy-aware compiler optimizations [28].”