Research Papers
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A Divide and Conquer Algorithm for DAG Scheduling Under Power Constraints.
G. Demirci, I. Marincic, and H. Hoffmann. A divide and conquer algorithm for DAG scheduling under power constraints. In The International Conference for High Performance Computing, Networking, Storage, and Analysis, Supercomputing, 2018.
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A Generalized Software Framework for Accurate and Efficient Management of Performance Goals (2013)
H. Hoffmann, M. Maggio, M. D. Santambrogio, A. Leva, and A. Agarwal. A generalized software framework for accurate and efficient management of performance goals. In Proceedings of the International Conference on Embedded Software, EMSOFT, 2013.
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A Pattern for Efficient Parallel Computation on Multicore Processors with Scalar Operand Networks (2010)
H. Hoffmann, S. Devadas, and A. Agarwal. A pattern for efficient parallel computation on multicore processors with scalar operand networks. In Proceedings of the 2010 Workshop on Parallel Programming Patterns ParaPLoP, 2010.
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A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints (2015)
N. Mishra, H. Zhang, J. D. Lafferty, and H. Hoffmann. A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints. In Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS, 2015.
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A Self-Aware Processor SOC Using Energy Monitors Integrated Into Power Converters for Self-Adaptation
Y. Sinangil, S. M. Neuman, M. E. Sinangil, N. Ickes, G. Bezerra, E. Lau, J. E. Miller, H. C. Hoffmann, S. Devadas, and A. P. Chandraksan. A self-aware processor soc using energy monitors integrated into power converters for self-adaptation. In VLSI Circuits Digest of Technical Papers, 2014 Symposium on. IEEE, 2014.
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A Simple Cache Coherence Scheme for Integrated CPU-GPU Systems
A. W. B. Yudha, R. Pulungan, H. Hoffmann, and Y. Solihin. A simple cache coherence scheme for integrated CPU-GPU systems. In 57th ACM/IEEE Design Automation Conference, DAC, 2020.
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A STT-RAM-based Low-Power hybrid register file for GPGPUs (2015)
G. Li, X. Chen, G. Sun, H. Hoffmann, Y. Liu, Y. Wang, and H. Yang. A stt-ram-based low-power hybrid register file for GPGPUs. In Proceedings of the 52nd Annual Design Automation Conference DAC, 2015.
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Adapt & Cap: Coordinating System and Application-Level Adaptation for Power Constrained Systems
C. Hankendi, H. Hoffmann, and A. Coskun. Adapt & Cap: Coordinating system and application-level adaptation for power constrained systems. IEEE Design & Test, to appear.
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AgileCtrl: A Self-Adaptive Framework for Configuration Tuning (2022)
S. Wang, H. Hoffmann, and S. Lu. AgileCtrl: A self-adaptive framework for configuration tuning. In The 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering ESEC/FSE, 2022.
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ALERT: Accurate Learning for Energy and Timeliness (2020)
C. Wan, M. H. Santriaji, E. Rogers, H. Hoffmann, M. Maire, and S. Lu. ALERT: accurate learning for energy and timeliness. In A. Gavrilovska and E. Zadok, editors, USENIX Annual Technical Conference, USENIX ATC, 2020.
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Approximation Algorithms for Scheduling with Resource and Precedence Constraints
G. Demirci, H. Hoffmann, and D. H. K. Kim. Approximation algorithms for scheduling with resource and precedence constraints. In 35th International Symposium on Theoretical Aspects of Computer Science, STACS, 2018.
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ARCc: A Case for an Architecturally Redundant Cache-Coherence Architecture for Large Multicores
O. Khan, H. Hoffmann, M. Lis, F. Hijaz, A. Agarwal, and S. Devadas. ARCc: A case for an architecturally redundant cache-coherence architecture for large multicores. In Computer Design, International Conference on ICCD, 2011.
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Automated Control of Multiple Software Goals Using Multiple Actuators
M. Maggio, A. V. Papadopoulos, A. Filieri, and H. Hoffmann. Automated control of multiple software goals using multiple actuators. In Symposium on the Foundations of Software Engineering FSE, 2017
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Automated Design of Self-adaptive Software with Control-theoretical Formal Guarantees
A. Filieri, H. Hoffmann, and M. Maggio. Automated Design of Self-adaptive Software with Control-theoretical Formal Guarantees. In 36th International Conference on Software Engineering, ICSE, 2014.
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Automated Multi-Objective Control Strategies for Self-Adaptive Software Design (2015)
A. Filieri, H. Hoffmann, and M. Maggio. Automated multi-objective control for self-adaptive software design. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE, 2015.
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Bard: A Unified Framework for Managing Soft Timing and Power Constraints on Embedded Systems (2015)
C. Imes and H. Hoffmann. Bard: A unified framework for managing soft timing and power constraints on embedded systems. 2015.
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Budget RNNs: Multi-Capacity Neural Networks to Improve In-Sensor Inference under Energy Budgets (2021)
Kannan and H. Hoffmann. Budget RNNs: Multi-Capacity Neural Networks to Improve In-Sensor Inference under Energy Budgets. In 27th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, 2021.
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CAFQA: A Classical Simulation Bootstrap for VariationalQuantum Algorithms
G. S. Ravi, P. Gokhale, Y. Ding, W. M. Kirby, K. N. Smith, J. M. Baker, P. J. Love, H. Hoffmann, K. R. Brown, and F. T. Chong. CAFQA: clifford ansatz for quantum accuracy. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems ASPLOS, 2023.
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CALOREE: Learning Control for Predictable Latency and Low Energy (2018)
N. Mishra, C. Imes, J. D. Lafferty, and H. Hoffmann. CALOREE: Learning Control for Predictable Latency and Low Energy. In Proceedings of the Twenty-third International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS, 2018.
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CASH: Supporting IaaS Customers with a Sub-core Configurable Architecture (2016)
Y. Zhou, H. Hoffmann and D. Wentzlaff. CASH: Supporting IaaS Customers with a Sub-core Configurable Architecture. In Proceedings of the Twenty-first International Symposium on Computer Architecture, ISCA, 2016.
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Combining Machine Learning and Control to Manage Computing System Complexity and Dynamics (2018)
N. Mishra, C. Imes, J. Lafferty and H. Hoffmann. Combining Machine Learning and Control to Manage Computing System Complexity and Dynamics, 2018
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Creating a Scalable Microprocessor: A 16-issue Multiple-Program-Counter Microprocessor with Point-to-Point Scalar Operand Network
Taylor, J. Kim, J. Miller, D. Wentzlaff, F. Ghodrat, B. Greenwald, H. Hoffmann, P. Johnson, W. Lee, A. Saraf, N. Shnidman, V. Strumpen, S. Amarasinghe, and A. Agarwal. Creating a Scalable Microprocessor: A 16-issue Multiple-Program-Counter Microprocessor with Point-to-Point Scalar Operand Network. In Proceedings of the IEEE International Solid-State Circuits Conference ISSCC, February 2003.
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Decision Making in Autonomic Computing Systems: Comparison of Different Approaches and Techniques
M. Maggio, H. Hoffmann, A. V. Papadopoulos, J. Panerati, M. D. Santambrogio, A. Agarwal, and A. Leva. Comparison of decision-making strategies for self-optimization in autonomic computing systems. ACM Trans. Auton. Adapt. Syst., 7(4), Dec. 2012.
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Design of an Accelerator-Rich Architecture by Integrating Multiple Heterogeneous Coarse Grain Reconfigurable Arrays Over a Network-on-Chip
W. Hussain, R. Airoldi, H. Hoffmann, T. Ahonen, and J. Nurmi. Design of an accelerator-rich architecture by integrating multiple heterogeneous coarse grain reconfigurable arrays over a network-on-chip. In IEEE 25th International Conference on Application-Specific Systems, Architectures and Processors, ASAP, 2014.
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Dynamic Knobs for Responsive Power-aware Computing
H. Hoffmann, S. Sidiroglou, M. Carbin, S. Misailovic, A. Agarwal, and M. C. Rinard. Dynamic Knobs for Responsive Power-aware Computing. In Proceedings of the 16th International Conference on Architectural Support for Program-ming Languages and Operating Systems, ASPLOS, 2011.
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Embodied Self-Aware Computing Systems
H. Hoffmann, A. Jantsch, and N. D. Dutt. Embodied self-aware computing systems. Proc. IEEE, 108(7):1027–1046, 2020.
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Energy-Efficient Application Resource Scheduling Using Machine Learning Classifiers
C. Imes, S. A. Hofmeyr, and H. Hoffmann. Energy-efficient application resource scheduling using machine learning classifiers. In Proceedings of the International Conference on Parallel Processing, ICPP, 2018.
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ESP: A Machine Learning Approach to Predicting Application Interference
N. Mishra, J. D. Lafferty, and H. Hoffmann. ESP: A machine learning approach to predicting application interference. In 14th International Conference on Autonomic Computing, ICAC, 2017.
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Generalizable and Interpretable Learning for Configuration Extrapolation
Y. Ding, A. Pervaiz, M. Carbin, and H. Hoffmann. Generalizable and interpretable learning for configuration extrapolation. In 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, FSE, 2021.
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Generative and Multi-phase Learning for Computer Systems Optimization
Y. Ding, N. Mishra, and H. Hoffmann. Generative and multi-phase learning for computer systems optimization. In International Symposium on Computer Architecture, ISCA, 2019.
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GOAL: Supporting General and Dynamic Adaptation in Computing Systems
A. Pervaiz, Y. H. Yang, A. Duracz, F. Bartha, R. Sai, C. Imes, R. Cartwright, K. Palem, S. Lu, and H. Hoffmann. GOAL: Supporting general and dynamic adaptation in computing systems. In Proceedings of the ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software Onward!, 2022.
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GRAPE: Minimizing Energy for Interactive GPU Applications (2016)
M. H. Santriaji and H. Hoffmann. GRAPE: Minimizing Energy for Interactive GPU Applications. In 49th Annual IEEE/ACM International Symposium on Microarchitecture MICRO, 2016.
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JouleGuard: Energy Guarantees for Approximate Applications (2015)
H. Hoffmann. JouleGuard: Energy Guarantees for Approximate Applications. In Proceedings of the 25th Symposium on Operating Systems Principles, SOSP, 2015.
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LinnOS: Predictability on Unpredictable Flash Storage With a Light Neural Network
M. Hao, L. Toksoz, N. Li, E. E. Halim, H. Hoffmann, and H. S. Gunawi. LinnOS: Predictability on unpredictable flash storage with a light neural network. In 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI, 2020.
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Managing Performance vs. Accuracy Trade-offs With Loop Perforation
S. Sidiroglou-Douskos, S. Misailovic, H. Hoffmann, and M. C. Rinard. Managing performance vs. accuracy trade-offs with loop perforation. In SIGSOFT/FSE’11 19th ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE-19) and ESEC’11: 13rd European Software Engineering Conference (ESEC-13), Szeged, Hungary, September 5-9, 2011, 2011.
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Maximizing Performance Under a Power Cap: A Comparison of Hardware, Software, and Hybrid Techniques (2016)
H. Zhang and H. Hoffmann. Maximizing Performance Under a Power Cap: A Comparison of Hardware, Software, and Hybrid Techniques. In Proceedings of the Twenty-first International Conference on Architectural Support for Program-ming Languages and Operating Systems, ASPLOS, 2016.
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MEANTIME: Achieving Both Minimal Energy and Timeliness With Approximate Computing
A. Farrell and H. Hoffmann. Meantime: Achieving both minimal energy and timeliness with approximate computing. In USENIX Annual Technical Conference, USENIX ATC, 2016.
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Memory Cocktail Therapy: A General Learning-based Framework to Optimize Dynamic Tradeoffs in NVMs
Z. Deng, L. Zhang, N. Mishra, H. Hoffmann, and F. Chong. Memory cocktail therapy: A general learning-based framework to optimize dynamic tradeoffs in nvms. In 50th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO, 2017.
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MERLOT: Architectural Support for Energy-Efficient Real-time Processing in GPUs
M. H. Santriaji and H. Hoffmann. MERLOT: Architectural Support for Energy-Efficient Real-time Processing in GPUs. In 24th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, 2018.
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Navigating the Dynamic Noise Landscape of Variational Quantum Algorithms With QISMET
G. S. Ravi, K. N. Smith, J. M. Baker, T. Kannan, N. Earnest, A. Javadi-Abhari, H. Hoffmann, and F. T. Chong. Navigating the dynamic noise landscape of variational quantum algorithms with QISMET. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems ASP-LOS, 2023.
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NURD: Negative-unlabeled Learning for Online Datacenter Straggler Prediction
Y. Ding, A. Rao, H. Song, R. Willett, and H. Hoffmann. NURD: negative-unlabeled learning for online datacenter straggler prediction. In The Proceedings of the Conference on Machine Learning and Systems MLSys, 2022.
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On-Chip Interconnection Architecture of the Tile Processor
D. Wentzlaff, P. Griffin, H. Hoffmann, L. Bao, B. Edwards, C. Ramey, M. Mattina, C. Miao, J. F. B. III, and A. Agarwal. On-chip interconnection architecture of the tile processor. IEEE Micro, 27(5), 2007.
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Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers
Y. Shi, N. Leung, P. Gokhale, Z. Rossi, D. I. Schuster, H. Hoffmann, and F. T. Chong. Optimized compilation of aggregated instructions for realistic quantum computers. In Proceedings of the Twenty-fourth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS, 2019.
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Orthogonalized SGD and Nested Architectures for Anytime Neural Networks (2020)
C. Wan, H. Hoffmann, S. Lu, and M. Maire. Orthogonalized SGD and nested architectures for anytime neural networks. In Proceedings of the 37th International Conference on Machine Learning, ICML, 2020.
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Partial Compilation of Variational Algorithms for Noisy Intermediate-Scale Quantum Machines
P. Gokhale, Y. Ding, T. Propson, C. Winkler, N. Leung, Y. Shi, D. I. Schuster, H. Hoffmann, and F. T. Chong. Partial compilation of variational algorithms for noisy intermediate-scale quantum machines. In 52nd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO, 2019.
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Patterns and Statistical Analysis for Understanding Reduced Resource Computing
M. C. Rinard, H. Hoffmann, S. Misailovic, and S. Sidiroglou. Patterns and statistical analysis for understanding reduced resource computing. In Proceedings of the 25th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications, OOPSLA, 2010.
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PCP: A Generalized Approach to Optimizing Performance Under Power Constraints Through Resource Management (2014)
H. Hoffmann and M. Maggio. PCP: A generalized approach to optimizing performance under power constraints through resource management. In 11th International Conference on Autonomic Computing, ICAC, 2014.
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Penelope: Peer-to-peer Power Management
T. Srivastava, H. Zhang, and H. Hoffmann. Penelope: Peer-to-peer power management. In the 51st International Conference on Parallel Processing ICPP, 2022.
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Performance & Energy Tradeoffs for Dependent Distributed Applications Under System-Wide Power Caps
H. Zhang and H. Hoffmann. Performance & energy tradeoffs for dependent distributed applications under system-wide power caps. In Proceedings of the International Conference on Parallel Processing, ICPP, 2018.
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PoDD: Power-Capping Dependent Distributed Applications
H. Zhang and H. Hoffmann. PoDD: Power-capping dependent distributed applications. In The International Conference for High Performance Computing, Networking, Storage, and Analysis, Supercomputing, 2019.
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POET: A Portable Approach to Minimizing Energy Under Soft Real-time Constraints (2015)
C. Imes, D. H. K. Kim, M. Maggio, and H. Hoffmann. POET: A Portable Approach to Minimizing Energy Under Soft Real-time Constraints. In 21st IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, 2015.
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PoLiMEr: An Energy Monitoring and Power Limiting Interface for HPC Applications (2017)
I. Marincic, V. Vishwanath, and H. Hoffmann. Polimer: An energy monitoring and power limiting interface for HPC applications. In Proceedings of the 5th International Workshop on Energy Efficient Supercomputing, E2SC@SC, 2017.
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Prediction Privacy in Distributed Multi-Exit Neural Networks: Vulnerabilities and Solutions (2023)
T. Kannan, N. Feamster, and H. Hoffmann. Prediction Privacy in Distributed Multi-Exit Neural Networks: Vulnerabilities and Solutions. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS), 2023.
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Protecting Adaptive Sampling From Information Leakage on Low-Power Sensors
T. Kannan and H. Hoffmann. Protecting adaptive sampling from information leakage on low-power sensors. In 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Lausanne, ASPLOS, 2022.
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Proteus: Language and Runtime Support for Self-Adaptive Software Development
S. Barati, F. A. Bartha, S. Biswas, R. Cartwright, A. Duracz, D. S. Fussell, H. Hoffmann, C. Imes, J. E. Miller, N. Mishra, Arvind, D. Nguyen, K. V. Palem, Y. Pei, K. Pingali, R. Sai, A. Wright, Y. Yang, and S. Zhang. Proteus: Language and runtime support for self-adaptive software development. IEEE Software, 36(2):73–82, 2019.
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Providing Fairness in Heterogeneous Multicores with a Predictive, Adaptive Scheduler
I. Marincic and H. H. Venkat Vishwanath. Providing Fairness in Heterogeneous Multicores with a Predictive, Adaptive Scheduler. In 34th IEEE International Parallel and Distributed Processing Symposium, IPDPS, 2020.
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Proxima: Accelerating the Integration of Machine Learning in Atomistic Simulations (2021)
Y. Zamora, L. Ward, G. Sivaraman, I. Foster, and H. Hoffmann. Proxima: Accelerating the Integration of Machine Learning in Atomistic Simulations. In International Conference on Supercomputing ICS, 2021.
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Quality of Service Profiling
S. Misailovic, S. Sidiroglou, H. Hoffmann, and M. C. Rinard. Quality of service profiling. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering, ICSE, 2010.
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Racing and Pacing to Idle: An Evaluation of Heuristics for Energy-aware Resource Allocation
H. Hoffmann. Racing and pacing to idle: an evaluation of heuristics for energy-aware resource allocation. In Proceedings of the Workshop on Power-Aware Computing and Systems, HotPower, 2013.
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Racing and Pacing to Idle: Theoretical and Empirical Analysis of Energy Optimization Heuristics
D. H. K. Kim, C. Imes, and H. Hoffmann. Racing and pacing to idle: Theoretical and empirical analysis of energy optimization heuristics. In 2015 IEEE 3rd International Conference on Cyber-Physical Systems, Networks, and Applications, CPSNA, 2015.
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Remote Store Programming: Mechanisms and Performance
H. Hoffmann, D. Wentzlaff, and A. Agarwal. Remote store programming. In High Performance Embedded Architectures and Compilers, 5th International Conference, HiPEAC, 2010.
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Retrospective: Evaluation of the RAW Micro-Processor: An Exposed-Wire-Delay Architecture for ILP and Streams
M. B. Taylor, W. Lee, J. E. Miller, D. Wentzlaff, I. Bratt, B. Greenwald, H. Hoffmann, P. Johnson, J. S. Kim, J. Psota, A. Saraf, N. Shnidman, V. Strumpen, M. Frank, S. P. Amarasinghe, and A. Agarwal. Evaluation of the raw micro-processor: An exposed-wire-delay architecture for ILP and streams. In 31st International Symposium on Computer…
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Self-adaptive Video Encoder: Comparison of Multiple adaptation Strategies Made Simple (2017)
M. Maggio, A. V. Papadopoulos, A. Filieri, and H. Hoffmann. Self-adaptive video encoder: Comparison of multiple adaptation strategies made simple. In 12th IEEE/ACM International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS, 2017.
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Self-aware Computing in the Angstrom Processor
H. Hoffmann, J. Holt, G. Kurian, E. Lau, M. Maggio, J. E. Miller, S. M. Neuman, M. E. Sinangil, Y. Sinangil, A. Agar-wal, A. P. Chandrakasan, and S. Devadas. Self-aware Computing in the Angstrom Processor. In The 49th Annual Design Automation Conference 2012, DAC, 2012.
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Self-Aware Systems for the Internet of Things (2016)
¨M. Mostl, J. Schlatow, R. Ernst, H. Hoffmann, A. Merchant, and A. Shraer. Self-aware systems for the internet-of-things. In Proceedings of the Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, CODES, 2016.
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Server-Driven Video Streaming for Deep Learning Inference
K. Du, A. Pervaiz, X. Yuan, A. Chowdhery, Q. Zhang, H. Hoffmann, and J. Jiang. Server-driven video streaming for deep learning inference. In H. Schulzrinne and V. Misra, editors, Proceedings of the ACM Special Interest Group on Data Communication on the applications SIGCOMM, 2020.
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Software Engineering Meets Control Theory (2015)
A. Filieri et al. Software Engineering Meets Control Theory. In Proceedings of the 10th IEEE/ACM International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 2015, pp. 71–82.
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Statically Inferring Performance Properties of Software Configurations
C. Li, S. Wang, H. Hoffmann, and S. Lu. Statically inferring performance properties of software configurations. In Fifteenth EuroSys Conference, Eurosys, 2020.
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Stream Algorithms and Architecture
V. Strumpen, H. Hoffmann, and A. Agarwal. Stream algorithms and architecture. J. Instruction-Level Parallelism, 6, 2004.
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StrongBox: Confidentiality, Integrity, and Performance using Stream Ciphers for Full Drive Encryption
B. D. III, A. J. Feldman, H. Gunawi, and H. Hoffmann. StrongBox: Confidentiality, Integrity, and Performance using Stream Ciphers for Full Drive Encryption. In Proceedings of the Twenty-third International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS, 2018.
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Systemwide Power Management with Argo
D. A. Ellsworth, T. Patki, S. Perarnau, S. Seo, A. Amer, J. A. Zounmevo, R. Gupta, K. Yoshii, H. Hoffmann, A. D. Malony, M. Schulz, and P. H. Beckman. Systemwide Power Management with Argo. In International Parallel and Distributed Processing Symposium Workshops IPDPS Workshops, 2016.
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The RAW Microprocessor: A Computational Fabric for Software Circuits and General-Purpose Programs
aylor, J. S. Kim, J. E. Miller, D. Wentzlaff, F. Ghodrat, B. Greenwald, H. Hoffmann, P. Johnson, J. W. Lee, W. Lee, A. Ma, A. Saraf, M. Seneski, N. Shnidman, V. Strumpen, M. Frank, S. P. Amarasinghe, and A. Agarwal. The RAW microprocessor: A computational fabric for software circuits and general-purpose programs. IEEE Micro, 22(2),…
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Understanding and Auto-Adjusting Performance-Sensitive Configurations (2018)
S. Wang, C. Li, W. Sentosa, H. Hoffmann, and S. Lu. Understanding and Auto-Adjusting Performance-Sensitive Con-figurations. In Proceedings of the Twenty-third International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS, 2018.
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Using Code Perforation to Improve Performance, Reduce Energy Consumption, and Respond to Failures
H. Hoffmann, S. Misailovic, S. Sidiroglou, A. Agarwal, and M. Rinard. Using Code Perforation to Improve Performance, Reduce Energy Consumption, and Respond to Failures. Technical Report MIT-CSAIL-TR-2009-042, MIT, September 2009.
