Dynamic knobs for responsive power-aware computing

HoffmannHenry,; SidiroglouStelios,; CarbinMichael,; MisailovicSasa,; AgarwalAnant,; RinardMartin,

doi:10.1145/1961295.1950390

Cited by 89 publications

(119 citation statements)

References 45 publications

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“…Performance is specified as a target heart rate or a target latency between specially tagged heartbeats. Accuracy goals are specified as a distortion, or linear distance from an application defined nominal value [16], measured over some set of heartbeats. Power and energy goals can be specified as target average power for a given heartrate or as a target energy between tagged heartbeats.…”

Section: Observementioning

confidence: 99%

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Self-aware computing in the Angstrom processor

Hoffmann¹,

Holt²,

Kurian³

et al. 2012

Proceedings of the 49th Annual Design Automation Conference

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Addressing the challenges of extreme scale computing requires holistic design of new programming models and systems that support those models. This paper discusses the Angstrom processor, which is designed to support a new Self-aware Computing (SEEC) model. In SEEC, applications explicitly state goals, while other systems components provide actions that the SEEC runtime system can use to meet those goals. Angstrom supports this model by exposing sensors and adaptations that traditionally would be managed independently by hardware. This exposure allows SEEC to coordinate hardware actions with actions specified by other parts of the system, and allows the SEEC runtime system to meet application goals while reducing costs (e.g., power consumption).

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Section: Observementioning

confidence: 99%

“…SEEC supports a range of actions specified from the application-level [3,16], system software level [12,25], and the hardware level as exposed by the Angstrom processor (see Section 4).…”

Section: Actmentioning

confidence: 99%

Self-aware computing in the Angstrom processor

Hoffmann¹,

Holt²,

Kurian³

et al. 2012

Proceedings of the 49th Annual Design Automation Conference

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“…PowerDail [14] is a system that converts static configuration parameters that already exist in a program into dynamic knobs that can be tuned at runtime. Their system can then change these knobs at runtime to make the program meet performance and power usage goals.…”

Section: Related Workmentioning

confidence: 99%

Language and compiler support for auto-tuning variable-accuracy algorithms

Ansel

Wong

Chan

et al. 2011

International Symposium on Code Generation and Optimization (CGO 2011)

100

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Abstract-Approximating ideal program outputs is a common technique for solving computationally difficult problems, for adhering to processing or timing constraints, and for performance optimization in situations where perfect precision is not necessary. To this end, programmers often use approximation algorithms, iterative methods, data resampling, and other heuristics. However, programming such variable accuracy algorithms presents difficult challenges since the optimal algorithms and parameters may change with different accuracy requirements and usage environments. This problem is further compounded when multiple variable accuracy algorithms are nested together due to the complex way that accuracy requirements can propagate across algorithms and because of the size of the set of allowable compositions. As a result, programmers often deal with this issue in an ad-hoc manner that can sometimes violate sound programming practices such as maintaining library abstractions.In this paper, we propose language extensions that expose trade-offs between time and accuracy to the compiler. The compiler performs fully automatic compile-time and installtime autotuning and analyses in order to construct optimized algorithms to achieve any given target accuracy. We present novel compiler techniques and a structured genetic tuning algorithm to search the space of candidate algorithms and accuracies in the presence of recursion and sub-calls to other variable accuracy code. These techniques benefit both the library writer, by providing an easy way to describe and search the parameter and algorithmic choice space, and the library user, by allowing high level specification of accuracy requirements which are then met automatically without the need for the user to understand any algorithm-specific parameters. Additionally, we present a new suite of benchmarks, written in our language, to examine the efficacy of our techniques. Our experimental results show that by relaxing accuracy requirements, we can easily obtain performance improvements ranging from 1.1x to orders of magnitude of speedup.

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“…To get the energy benefits of matching processing speed to IO rates, two common techniques are: 1) To consolidate tasks by relocating tasks to fewer cores to improve core utilization and enable turning off idle cores [2,31]; 2) To vary processing speed by applying dynamic voltage and frequency scaling(DVFS) [3,9,16,35] or processor composition [8,21] or heterogeneous processors of different speeds [23,26,36]. Figure 2.…”

Section: Energy Optimizationmentioning

confidence: 99%

StreaMorph: A Case for Synthesizing Energy-Efficient Adaptive Programs Using High-Level Abstractions

Bui¹,

Lee²

2013

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. Copyright © 2013, by the author(s).All rights reserved.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. AcknowledgementThis work was supported in part by the Center for Hybrid and Embedded Software Systems (CHESS) at UC Berkeley, which receives support from the National Science Foundation (NSF awards \#0720882 (CSR-EHS: PRET), \#0931843 (CPS: Large: ActionWebs), and \#1035672 (CPS: Medium: Ptides)), the Naval Research Laboratory (NRL \#N0013-12-1-G015), and the following companies: Bosch, National Instruments, and Toyota. AbstractThis paper presents the concept of adaptive programs, whose computation and communication structures can morph to adapt to environmental and demand changes to save energy and computing resources. In this approach, programmers write one single program using a language at a higher level of abstraction. The compiler will exploit the properties of the abstractions to generate an adaptive program that is able to adjust computation and communication structures to environmental and demand changes. We develop a technique, called StreaMorph, that exploits the properties of stream programs' Synchronous Dataflow (SDF) programming model to enable runtime stream graph transformation. The StreaMorph technique can be used to optimize memory usage and to adjust core utilization leading to energy reduction by turning off idle cores or reducing operating frequencies. The main challenge for such a runtime transformation is to maintain consistent program states by copying states between different stream graph structures, because a stream program optimized for different numbers of cores often has different sets of filters and inter-filter channels. We propose an analysis that helps simplify program state copying processes by minimizing copying of states based on the properties of the SDF model.Finally, we implement the StreaMorph method in the StreamIt comp...

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Dynamic knobs for responsive power-aware computing

Cited by 89 publications

References 45 publications

Self-aware computing in the Angstrom processor

Self-aware computing in the Angstrom processor

Language and compiler support for auto-tuning variable-accuracy algorithms

StreaMorph: A Case for Synthesizing Energy-Efficient Adaptive Programs Using High-Level Abstractions

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