JouleGuard: energy guarantees for approximate applications
暂无分享,去创建一个
[1] Michel Tokic,et al. Adaptive epsilon-Greedy Exploration in Reinforcement Learning Based on Value Difference , 2010, KI.
[2] Henry Hoffmann,et al. A generalized software framework for accurate and efficient management of performance goals , 2013, 2013 Proceedings of the International Conference on Embedded Software (EMSOFT).
[3] William S. Levine,et al. The Control Handbook , 2005 .
[4] Klara Nahrstedt,et al. A control-based middleware framework for quality-of-service adaptations , 1999, IEEE J. Sel. Areas Commun..
[5] Andrew S. Tanenbaum,et al. Modern Operating Systems: Jumpstart Sampling Edition , 2008 .
[6] Gargi Dasgupta,et al. Server Workload Analysis for Power Minimization using Consolidation , 2009, USENIX Annual Technical Conference.
[7] Michel Tokic. Adaptive ε-greedy Exploration in Reinforcement Learning Based on Value Differences , 2010 .
[8] Fangzhe Chang,et al. Automatic configuration and run-time adaptation of distributed applications , 2000, Proceedings the Ninth International Symposium on High-Performance Distributed Computing.
[9] Ladan Tahvildari,et al. Self-adaptive software: Landscape and research challenges , 2009, TAAS.
[10] PalemKrishna,et al. Ten Years of Building Broken Chips , 2013 .
[11] Mahadev Satyanarayanan,et al. Managing battery lifetime with energy-aware adaptation , 2004, TOCS.
[12] Engin Ipek,et al. Coordinated management of multiple interacting resources in chip multiprocessors: A machine learning approach , 2008, 2008 41st IEEE/ACM International Symposium on Microarchitecture.
[13] Henry Hoffmann. Racing and pacing to idle: an evaluation of heuristics for energy-aware resource allocation , 2013, HotPower '13.
[14] Henry Hoffmann,et al. Dynamic knobs for responsive power-aware computing , 2011, ASPLOS XVI.
[15] Mark D. Corner,et al. Eon: a language and runtime system for perpetual systems , 2007, SenSys '07.
[16] Alan Edelman,et al. Language and compiler support for auto-tuning variable-accuracy algorithms , 2011, International Symposium on Code Generation and Optimization (CGO 2011).
[17] Peter D. Düben,et al. On the use of inexact, pruned hardware in atmospheric modelling , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[18] David Wentzlaff,et al. The sharing architecture: sub-core configurability for IaaS clouds , 2014, ASPLOS.
[19] Luis Ceze,et al. Neural Acceleration for General-Purpose Approximate Programs , 2012, 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture.
[20] Steven Hand,et al. Self-adaptive and self-configured CPU resource provisioning for virtualized servers using Kalman filters , 2009, ICAC '09.
[21] Douglas L. Jones,et al. GRACE-2: integrating fine-grained application adaptation with global adaptation for saving energy , 2009, Int. J. Embed. Syst..
[22] Karl-Erik Årzén,et al. A Game-Theoretic Resource Manager for RT Applications , 2013, 2013 25th Euromicro Conference on Real-Time Systems.
[23] Henry Hoffmann,et al. Power Optimization in Embedded Systems via Feedback Control of Resource Allocation , 2013, IEEE Transactions on Control Systems Technology.
[24] Calton Pu,et al. A feedback-driven proportion allocator for real-rate scheduling , 1999, OSDI '99.
[25] Kaushik Velusamy,et al. Modern Operating Systems , 2015 .
[26] Henry Hoffmann,et al. Patterns and statistical analysis for understanding reduced resource computing , 2010, OOPSLA.
[27] J. Flinn,et al. Energy-aware adaptation for mobile applications , 1999, SOSP.
[28] Lingamneni Avinash,et al. Ten Years of Building Broken Chips: The Physics and Engineering of Inexact Computing , 2013, TECS.
[29] Srinivas Devadas,et al. Selecting Spatiotemporal Patterns for Development of Parallel Applications , 2012, IEEE Transactions on Parallel and Distributed Systems.
[30] David C. Snowdon,et al. Koala: a platform for OS-level power management , 2009, EuroSys '09.
[31] Manuel Prieto,et al. Survey of Energy-Cognizant Scheduling Techniques , 2013, IEEE Transactions on Parallel and Distributed Systems.
[32] Karl Johan Åström,et al. Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.
[33] Woongki Baek,et al. Green: a framework for supporting energy-conscious programming using controlled approximation , 2010, PLDI '10.
[34] Nikil D. Dutt,et al. Exploiting Partially-Forgetful Memories for Approximate Computing , 2015, IEEE Embedded Systems Letters.
[35] Una-May O'Reilly,et al. Siblingrivalry: online autotuning through local competitions , 2012, CASES '12.
[36] Michael F. P. O'Boyle,et al. A Predictive Model for Dynamic Microarchitectural Adaptivity Control , 2010, 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture.
[37] Alon Naveh,et al. Power management architecture of the 2nd generation Intel® Core microarchitecture, formerly codenamed Sandy Bridge , 2011, IEEE Hot Chips Symposium.
[38] Martin C. Rinard,et al. Verifying quantitative reliability for programs that execute on unreliable hardware , 2013, OOPSLA.
[39] Asit K. Mishra,et al. METE: meeting end-to-end QoS in multicores through system-wide resource management , 2011, PERV.
[40] Yixin Diao,et al. Feedback Control of Computing Systems , 2004 .
[41] Daniel P. Siewiorek,et al. A resource allocation model for QoS management , 1997, Proceedings Real-Time Systems Symposium.
[42] Luis Ceze,et al. Architecture support for disciplined approximate programming , 2012, ASPLOS XVII.
[43] Klara Nahrstedt,et al. Energy-efficient soft real-time CPU scheduling for mobile multimedia systems , 2003, SOSP '03.
[44] Xiao Zhang,et al. Power containers: an OS facility for fine-grained power and energy management on multicore servers , 2013, ASPLOS '13.
[45] Ragunathan Rajkumar,et al. Critical power slope: understanding the runtime effects of frequency scaling , 2002, ICS '02.
[46] Philip Levis,et al. Energy management in mobile devices with the cinder operating system , 2011, EuroSys '11.
[47] Calton Pu,et al. SWiFT: a feedback control and dynamic reconfiguration toolkit , 1998 .
[48] Melanie Kambadur,et al. Trading Functionality for Power within Applications , 2014 .
[49] Henry Hoffmann,et al. A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints , 2015, ASPLOS.
[50] Lingamneni Avinash,et al. Designing Energy-Efficient Arithmetic Operators Using Inexact Computing , 2013, J. Low Power Electron..
[51] Henry Hoffmann,et al. POET: a portable approach to minimizing energy under soft real-time constraints , 2015, 21st IEEE Real-Time and Embedded Technology and Applications Symposium.
[52] Tarek F. Abdelzaher,et al. AdaptGuard: guarding adaptive systems from instability , 2009, ICAC '09.
[53] Henry Hoffmann,et al. PCP: A Generalized Approach to Optimizing Performance Under Power Constraints through Resource Management , 2014, ICAC.
[54] Steven Hand,et al. Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters , 2014, TAAS.
[55] Gernot Heiser,et al. Slow Down or Sleep, That Is the Question , 2011, USENIX Annual Technical Conference.
[56] Scott Shenker,et al. Scheduling for reduced CPU energy , 1994, OSDI '94.
[57] Luca Faust,et al. Modern Operating Systems , 2016 .
[58] Martin C. Rinard. Probabilistic accuracy bounds for fault-tolerant computations that discard tasks , 2006, ICS '06.
[59] Henry Hoffmann,et al. Automated design of self-adaptive software with control-theoretical formal guarantees , 2014, Software Engineering & Management.
[60] Xue Liu,et al. Dynamic Voltage Scaling in Multitier Web Servers with End-to-End Delay Control , 2007, IEEE Transactions on Computers.
[61] Martin Rinard,et al. Using Code Perforation to Improve Performance, Reduce Energy Consumption, and Respond to Failures , 2009 .
[62] Song Liu,et al. Flikker: saving DRAM refresh-power through critical data partitioning , 2011, ASPLOS XVI.
[63] Thomas L. Magnanti,et al. Applied Mathematical Programming , 1977 .
[64] Martin C. Rinard,et al. Proving acceptability properties of relaxed nondeterministic approximate programs , 2012, PLDI.
[65] Krishna V. Palem,et al. Energy aware algorithm design via probabilistic computing: from algorithms and models to Moore's law and novel (semiconductor) devices , 2003, CASES '03.
[66] Nikil D. Dutt,et al. xTune: A formal methodology for cross-layer tuning of mobile embedded systems , 2012, TECS.
[67] Henry Hoffmann,et al. Managing performance vs. accuracy trade-offs with loop perforation , 2011, ESEC/FSE '11.
[68] Henry Hoffmann,et al. Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems , 2012, TAAS.
[69] Dan Grossman,et al. EnerJ: approximate data types for safe and general low-power computation , 2011, PLDI '11.
[70] Michael N. Katehakis,et al. The Multi-Armed Bandit Problem: Decomposition and Computation , 1987, Math. Oper. Res..
[71] Xiaodong Li,et al. Cross-component energy management: Joint adaptation of processor and memory , 2007, TACO.
[72] Thomas F. Wenisch,et al. Power management of online data-intensive services , 2011, 2011 38th Annual International Symposium on Computer Architecture (ISCA).
[73] Henry Hoffmann,et al. CoAdapt: Predictable Behavior for Accuracy-Aware Applications Running on Power-Aware Systems , 2014, 2014 26th Euromicro Conference on Real-Time Systems.
[74] Chenyang Lu,et al. ControlWare: a middleware architecture for feedback control of software performance , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.
[75] Melanie Kambadur,et al. Energy Exchanges: Internal Power Oversight for Applications , 2014 .