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The field of machine programming (MP), the automation of the development of software, is making notable research advances.
A Probe Effect in Concurrent Programs
J. Gait · 1986
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Debugging Concurrent Programs
C. E. McDowell and D. P. Helmbold · 1989
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Network Constraints and Multi-objective Optimization for One-class Classification
M. M. Moya and D. R. Hush · 1996
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An Efficient k-Means Clustering Algorithm: Analysis and Implementation
T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu · 2002
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A Comparison of Counting and Sampling Modes of Using Performance Monitoring Hardware
S. V. Moore · 2002
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Correlating Instrumentation Data to System States: A Building Block for Automated Diagnosis and Control
I. Cohen, M. Goldszmidt, T. Kelly, J. Symons, and J. S. Chase · 2004
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https://bugs.mysql.com/bug.php?id=16504 , 2006
MySQL bug 16504 · 2006
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ftp://ftp.software.ibm.com/aix/tools/perftools/SystempTechUniv2006/UnixLasVegas2006-A09.pdf , 2006
Visual Performance Analyzer · 2006
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Evaluating MapReduce for Multi-core and Multiprocessor Systems
C. Ranger, R. Raghuraman, A. Penmetsa, G. Bradski, and C. Kozyrakis · 2007
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The PARSEC Benchmark Suite: Characterization and Architectural Implications
C. Bienia, S. Kumar, J. P. Singh, and K. Li · 2008
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Extracting and Composing Robust Features with Denoising Autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
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Online Anomaly Prediction for Robust Cluster Systems
X. Gu and H. Wang · 2009
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Massive Performance Regression From Switching to GCC 4.5
T. Glek · 2010
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Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression
L. Huang, J. Jia, B. Yu, B.-G. Chun, P. Maniatis, and M. Naik · 2010
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Demand-driven Software Race Detection Using Hardware Performance Counters
J. L. Greathouse, Z. Ma, M. I. Frank, R. Peri, and T. Austin · 2011
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SHERIFF: Precise Detection and Automatic Mitigation of False Sharing
T. Liu and E. D. Berger · 2011
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Profiling Directed NUMA Optimization on Linux Systems: A Case Study of the Gaussian Computational Chemistry Code
R. Yang, J. Antony, A. Rendell, D. Robson, and P. Strazdins · 2011
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Dynamic Cache Contention Detection in Multi-threaded Applications
Q. Zhao, D. Koh, S. Raza, D. Bruening, W.-F. Wong, and S. Amarasinghe · 2011
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X-ray: Automating Root-cause Diagnosis of Performance Anomalies in Production Software
M. Attariyan, M. Chow, and J. Flinn · 2012
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UBL: Unsupervised Behavior Learning for Predicting Performance Anomalies in Virtualized Cloud Systems
D. J. Dean, H. Nguyen, and X. Gu · 2012
Cited alongside, same era.
Visualizing Transactional Memory
J. E. Gottschlich, M. P. Herlihy, G. A. Pokam, and J. G. Siek · 2012
Cited alongside, same era.
Performance Debugging In The Large via Mining Millions of Stack Traces
S. Han, Y. Dang, S. Ge, D. Zhang, and T. Xie · 2012
Cited alongside, same era.
Understanding and Detecting Real-world Performance Bugs
G. Jin, L. Song, X. Shi, J. Scherpelz, and S. Lu · 2012
Cited alongside, same era.
Automated Detection of Performance Regressions Using Statistical Process Control Techniques
T. H. Nguyen, B. Adams, Z. M. Jiang, A. E. Hassan, M. Nasser, and P. Flora · 2012
Cited alongside, same era.
Enhancing Performance Optimization of Multicore Chips and Multichip Nodes with Data Structure Metrics
Remix: Online Detection and Repair of Cache Contention for the JVM
A. Eizenberg, S. Hu, G. Pokam, and J. Devietti · 2016
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Cheetah: Detecting False Sharing Efficiently and Effectively
T. Liu and X. Liu · 2016
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LASER: Light, Accurate Sharing dEtection and Repair
L. Luo, A. Sriraman, B. Fugate, S. Hu, G. Pokam, C. J. Newburn, and J. Devietti · 2016
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AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms
K. Becker and J. Gottschlich · 2017
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Hang Doctor: Runtime Detection and Diagnosis of Soft Hangs for Smartphone Apps
M. Brocanelli and X. Wang · 2018
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The Three Pillars of Machine Programming
J. Gottschlich, A. Solar-Lezama, N. Tatbul, M. Carbin, M. Rinard, R. Barzilay, S. Amarasinghe, J. B. Tenenbaum, and T. Mattson · 2018
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A. Rane and J. Browne · 2012
Cited alongside, same era.
PREPARE: Predictive Performance Anomaly Prevention for Virtualized Cloud Systems
Y. Tan, H. Nguyen, Z. Shen, X. Gu, C. Venkatramani, and D. Rajan · 2012
Cited alongside, same era.
Production-run Software Failure Diagnosis via Hardware Performance Counters
J. Arulraj, P.-C. Chang, G. Jin, and S. Lu · 2013
Cited alongside, same era.
Concurrent Predicates: A Debugging Technique for Every Parallel Programmer
J. E. Gottschlich, G. A. Pokam, C. L. Pereira, and Y. Wu · 2013
Cited alongside, same era.
Detection of False Sharing Using Machine Learning
S. Jayasena, S. Amarasinghe, A. Abeyweera, G. Amarasinghe, H. D. Silva, S. Rathnayake, X. Meng, and Y. Liu · 2013
Cited alongside, same era.
Whose Cache Line is It Anyway?: Operating System Support for Live Detection and Repair of False Sharing
M. Nanavati, M. Spear, N. Taylor, S. Rajagopalan, D. T. Meyer, W. Aiello, and A. Warfield · 2013
Cited alongside, same era.
Discovering, Reporting, and Fixing Performance Bugs
A. Nistor, T. Jiang, and L. Tan · 2013
Cited alongside, same era.
Closest in time.
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection
T. J. Lee, J. Gottschlich, N. Tatbul, E. Metcalf, and S. Zdonik · 2018
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PerfFuzz: Automatically Generating Pathological Inputs
C. Lemieux, R. Padhye, K. Sen, and D. Song · 2018
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Pcatch: Automatically Detecting Performance Cascading Bugs in Cloud Systems
J. Li, Y. Chen, H. Liu, S. Lu, Y. Zhang, H. S. Gunawi, X. Gu, X. Lu, and D. Li · 2018
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Generalized Data Structure Synthesis
C. Loncaric, M. D. Ernst, and E. Torlak · 2018
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Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
X. Zhang, A. Solar-Lezama, and R. Singh · 2018
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Learning to Optimize Halide with Tree Search and Random Programs
A. Adams, K. Ma, L. Anderson, R. Baghdadi, T.-M. Li, M. Gharbi, B. Steiner, S. Johnson, K. Fatahalian, F. Durand, and J. Ragan-Kelley · 2019
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Automatically Translating Image Processing Libraries to Halide
M. B. S. Ahmad, J. Ragan-Kelley, A. Cheung, and S. Kamil · 2019
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Aroma: Code Recommendation via Structural Code Search
S. Luan, D. Yang, C. Barnaby, K. Sen, and S. Chandra · 2019
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Learning Fitness Functions for Genetic Algorithms, 2019
S. Mandal, T. A. Anderson, J. Gottschlich, S. Zhou, and A. Muzahid · 2019
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Neo: A Learned Query Optimizer
R. Marcus, P. Negi, H. Mao, C. Zhang, M. Alizadeh, T. Kraska, O. Papaemmanouil, and N. Tatbul · 2019
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Scaling symbolic evaluation for automated verification of systems code with Serval
L. Nelson, J. Bornholt, R. Gu, A. Baumann, E. Torlak, and X. Wang · 2019
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Swizzle Inventor: Data Movement Synthesis for GPU Kernels
P. M. Phothilimthana, A. S. Elliott, A. Wang, A. Jangda, B. Hagedorn, H. Barthels, S. J. Kaufman, V. Grover, E. Torlak, and R. Bodik · 2019
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