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In the last decade, machine learning based compilation has moved from an an obscure research niche to a mainstream activity.
J. Chipps, M. Koschmann, S. Orgel, A. Perlis, and J. Smith, “A mathematical language compiler,” in Proceedings of the 1956 11th ACM national meeting . ACM, 1956, pp. 114–117
1956
Earlier work this paper cites.
P. B. Sheridan, “The arithmetic translator-compiler of the ibm fortran automatic coding system,” Communications of the ACM , vol. 2, no. 2, pp. 9–21, 1959
1959
Earlier work this paper cites.
M. D. McIlroy, “Macro instruction extensions of compiler languages,” Communications of the ACM , vol. 3, no. 4, pp. 214–220, 1960
1960
Earlier work this paper cites.
J. MacQueen et al. , “Some methods for classification and analysis of multivariate observations,” 1967
1967
Earlier work this paper cites.
M. E. Lesk and E. Schmidt, “Lex: A lexical analyzer generator,” 1975
1975
Earlier work this paper cites.
S. C. Johnson, Yacc: Yet another compiler-compiler . Bell Laboratories Murray Hill, NJ, 1975, vol. 32
1975
Earlier work this paper cites.
H. Massalin, “Superoptimizer: a look at the smallest program,” in ACM SIGPLAN Notices , vol. 22, no. 10, 1987, pp. 122–126
1987
Earlier work this paper cites.
T. A. Wagner, V. Maverick, S. L. Graham, and M. A. Harrison, “Accurate static estimators for program optimization,” in Proceedings of the ACM SIGPLAN 1994 Conference on Programming Language Design and Implementation , ser. PLDI ’94, 1994, pp. 85–96
1994
Earlier work this paper cites.
V. Tiwari, S. Malik, and A. Wolfe, “Power analysis of embedded software: A first step towards software power minimization,” in IEEE/ACM International Conference on Computer-Aided Design , 1994, pp. 384–390
1994
Earlier work this paper cites.
E. A. Brewer, “High-level optimization via automated statistical modeling,” in Proceedings of the Fifth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming , ser. PPOPP ’95, 1995, pp. 80–91
1995
Earlier work this paper cites.
T. K. Ho, “Random decision forests,” in Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1 , ser. ICDAR ’95, 1995
1995
Earlier work this paper cites.
C. K. Williams and C. E. Rasmussen, “Gaussian processes for regression,” in Advances in neural information processing systems , 1996, pp. 514–520
1996
Earlier work this paper cites.
F. Bodin, T. Kisuki, P. Knijnenburg, M. O’Boyle, and E. Rohou, “Iterative compilation in a non-linear optimisation space,” in Workshop on Profile and Feedback-Directed Compilation , 1998
1998
Earlier work this paper cites.
R. Leupers and P. Marwedel, “Function inlining under code size constraints for embedded processors,” in Computer-Aided Design, 1999. Digest of Technical Papers. 1999 IEEE/ACM International Conference on . IEEE, 1999, pp. 253–256
1999
Earlier work this paper cites.
K. D. Cooper, P. J. Schielke, and D. Subramanian, “Optimizing for reduced code space using genetic algorithms,” in Proceedings of the ACM SIGPLAN 1999 Workshop on Languages, Compilers, and Tools for Embedded Systems , ser. LCTES ’99, 1999, pp. 1–9
1999
Earlier work this paper cites.
K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, “When is “nearest neighbor” meaningful?” in International conference on database theory . Springer, 1999, pp. 217–235
1999
Earlier work this paper cites.
H. Yu and L. Rauchwerger, “Adaptive reduction parallelization techniques,” in Proceedings of the 14th International Conference on Supercomputing , ser. ICS ’00, 2000, pp. 66–77
2000
Earlier work this paper cites.
T. G. Dietterich, “Ensemble methods in machine learning,” in Proceedings of the First International Workshop on Multiple Classifier Systems , ser. MCS ’00, 2000, pp. 1–15
2000
Earlier work this paper cites.
M. G. Lagoudakis and M. L. Littman, “Algorithm selection using reinforcement learning,” in Proceedings of the Seventeenth International Conference on Machine Learning , ser. ICML ’00, 2000, pp. 511–518
2000
Earlier work this paper cites.
B. Singer and M. M. Veloso, “Learning to predict performance from formula modeling and training data,” in Proceedings of the Seventeenth International Conference on Machine Learning , ser. ICML ’00, 2000, pp. 887–894
2000
Earlier work this paper cites.
S. Browne, J. Dongarra, N. Garner, G. Ho, and P. Mucci, “A portable programming interface for performance evaluation on modern processors,” The international journal of high performance computing applications , vol. 14, no. 3, pp. 189–204, 2000
2000
Earlier work this paper cites.
M. J. Voss and R. Eigemann, “High-level adaptive program optimization with adapt,” in Proceedings of the Eighth ACM SIGPLAN Symposium on Principles and Practices of Parallel Programming , ser. PPoPP ’01, 2001, pp. 93–102
2001
Earlier work this paper cites.
A. Monsifrot, F. Bodin, and R. Quiniou, “A machine learning approach to automatic production of compiler heuristics,” in International Conference on Artificial Intelligence: Methodology, Systems, and Applications , 2002, pp. 41–50
2002
Earlier work this paper cites.
T. Sherwood, E. Perelman, G. Hamerly, and B. Calder, “Automatically characterizing large scale program behavior,” in Proceedings of the 10th International Conference on Architectural Support for Programming Languages and Operating Systems , ser. ASPLOS X, 2002, pp. 45–57
2002
Earlier work this paper cites.
L. Eeckhout, H. Vandierendonck, and K. D. Bosschere, “Workload design: selecting representative program-input pairs,” in Proceedings.International Conference on Parallel Architectures and Compilation Techniques , 2002, pp. 83–94
2002
Earlier work this paper cites.
B. Singer and M. Veloso, “Learning to construct fast signal processing implementations,” Journal of Machine Learning Research , vol. 3, pp. 887–919, 2002
2002
Earlier work this paper cites.
I. Fodor, “A survey of dimension reduction techniques,” Lawrence Livermore National Laboratory, Tech. Rep., 2002
2002
Earlier work this paper cites.
K. D. Cooper, D. Subramanian, and L. Torczon, “Adaptive optimizing compilers for the 21st century,” The Journal of Supercomputing , vol. 23, no. 1, pp. 7–22, 2002
2002
Earlier work this paper cites.
P. M. Knijnenburg, T. Kisuki, and M. F. O’Boyle, “Combined selection of tile sizes and unroll factors using iterative compilation,” The Journal of Supercomputing , vol. 24, no. 1, pp. 43–67, 2003
2003
Earlier work this paper cites.
M. Stephenson, S. Amarasinghe, M. Martin, and U.-M. O’Reilly, “Meta optimization: Improving compiler heuristics with machine learning,” in Proceedings of the ACM SIGPLAN 2003 Conference on Programming Language Design and Implementation , ser. PLDI ’03, 2003, pp. 77–90
2003
Earlier work this paper cites.
E. Perelman, G. Hamerly, M. Van Biesbrouck, T. Sherwood, and B. Calder, “Using simpoint for accurate and efficient simulation,” in Proceedings of the 2003 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems , ser. SIGMETRICS ’03, 2003, pp. 318–319
2003
Earlier work this paper cites.
C. Lattner and V. Adve, “LLVM: A compilation framework for lifelong program analysis & transformation,” in Proceedings of the International Symposium on Code Generation and Optimization: Feedback-directed and Runtime Optimization , ser. CGO ’04, 2004
2004
Earlier work this paper cites.
P. Zhao and J. Amaral, “To inline or not to inline? enhanced inlining decisions,” Languages and Compilers for Parallel Computing , pp. 405–419, 2004
2004
Earlier work this paper cites.
M. Kim, T. Hiroyasu, M. Miki, and S. Watanabe, SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2 , 2004, pp. 742–751
2004
Earlier work this paper cites.
L. Almagor, K. D. Cooper, A. Grosul, T. J. Harvey, S. W. Reeves, D. Subramanian, L. Torczon, and T. Waterman, “Finding effective compilation sequences,” in Proceedings of the 2004 ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems , ser. LCTES ’04, 2004, pp. 231–239
2004
Earlier work this paper cites.
M. Frigo and S. G. Johnson, “The design and implementation of FFTW3,” Proceedings of the IEEE , vol. 93, no. 2, pp. 216–231, 2005, special issue on “Program Generation, Optimization, and Platform Adaptation”
2005
Earlier work this paper cites.
J. Cavazos and M. F. P. O’Boyle, “Automatic tuning of inlining heuristics,” in Proceedings of the 2005 ACM/IEEE Conference on Supercomputing , ser. SC ’05, 2005
2005
Earlier work this paper cites.
M. Stephenson and S. Amarasinghe, “Predicting unroll factors using supervised classification,” in Proceedings of the International Symposium on Code Generation and Optimization , ser. CGO ’05, 2005, pp. 123–134
2005
Earlier work this paper cites.
X. Li, M. J. Garzaran, and D. Padua, “Optimizing sorting with genetic algorithms,” in Proceedings of the International Symposium on Code Generation and Optimization , ser. CGO ’05, 2005, pp. 99–110
2005
Earlier work this paper cites.
K. D. Cooper, A. Grosul, T. J. Harvey, S. Reeves, D. Subramanian, L. Torczon, and T. Waterman, “ACME: Adaptive compilation made efficient,” in Proceedings of the 2005 ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems , ser. LCTES ’05, 2005, pp. 69–77
2005
Earlier work this paper cites.
Y. Zhang, M. Voss, and E. S. Rogers, “Runtime empirical selection of loop schedulers on hyperthreaded smps,” in 19th IEEE International Parallel and Distributed Processing Symposium , ser. IPDPS ’05, 2005
2005
Earlier work this paper cites.
F. Agakov, E. Bonilla, J. Cavazos, B. Franke, G. Fursin, M. F. P. O’Boyle, J. Thomson, M. Toussaint, and C. K. I. Williams, “Using machine learning to focus iterative optimization,” in Proceedings of the International Symposium on Code Generation and Optimization , ser. CGO ’06, 2006, pp. 295–305
2006
Earlier work this paper cites.
B. C. Lee and D. M. Brooks, “Accurate and efficient regression modeling for microarchitectural performance and power prediction,” in Proceedings of the 12th International Conference on Architectural Support for Programming Languages and Operating Systems , ser. ASPLOS XII, 2006, pp. 185–194
2006
Earlier work this paper cites.
J. Cavazos and M. F. P. O’Boyle, “Method-specific dynamic compilation using logistic regression,” in Proceedings of the 21st Annual ACM SIGPLAN Conference on Object-oriented Programming Systems, Languages, and Applications , ser. OOPSLA ’06, 2006, pp. 229–240
2006
Earlier work this paper cites.
M. Curtis-Maury, J. Dzierwa, C. D. Antonopoulos, and D. S. Nikolopoulos, “Online power-performance adaptation of multithreaded programs using hardware event-based prediction,” in Proceedings of the 20th Annual International Conference on Supercomputing , ser. ICS ’06, 2006, pp. 157–166
2006
Earlier work this paper cites.
P. J. Joseph, K. Vaswani, and M. J. Thazhuthaveetil, “A predictive performance model for superscalar processors,” in Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture , ser. MICRO 39, 2006, pp. 161–170
2006
Earlier work this paper cites.
C. E. Rasmussen and C. K. Williams, Gaussian processes for machine learning . MIT press Cambridge, 2006, vol. 1
2006
Earlier work this paper cites.
J. Cavazos, C. Dubach, F. Agakov, E. Bonilla, M. F. P. O’Boyle, G. Fursin, and O. Temam, “Automatic performance model construction for the fast software exploration of new hardware designs,” in Proceedings of the 2006 International Conference on Compilers, Architecture and Synthesis for Embedded Systems , ser. CASES ’06, 2006, pp. 24–34
2006
Earlier work this paper cites.
C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) . Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006
2006
Earlier work this paper cites.
K. Hoste, A. Phansalkar, L. Eeckhout, A. Georges, L. K. John, and K. De Bosschere, “Performance prediction based on inherent program similarity,” in Parallel Architectures and Compilation Techniques (PACT), 2006 International Conference on . IEEE, 2006, pp. 114–122
2006
Earlier work this paper cites.
K. Asanovic, R. Bodik, B. C. Catanzaro, J. J. Gebis, P. Husbands, K. Keutzer, D. A. Patterson, W. L. Plishker, J. Shalf, S. W. Williams et al. , “The landscape of parallel computing research: A view from berkeley,” Technical Report UCB/EECS-2006-183, University of California, Berkeley, Tech. Rep., 2006
2006
Earlier work this paper cites.
K. Vaswani, M. J. Thazhuthaveetil, Y. N. Srikant, and P. J. Joseph, “Microarchitecture sensitive empirical models for compiler optimizations,” in International Symposium on Code Generation and Optimization (CGO’07) , 2007, pp. 131–143
2007
Earlier work this paper cites.
M. Curtis-Maury, K. Singh, S. A. McKee, F. Blagojevic, D. S. Nikolopoulos, B. R. de Supinski, and M. Schulz, “Identifying energy-efficient concurrency levels using machine learning,” in 2007 IEEE International Conference on Cluster Computing , 2007, pp. 488–495
2007
Earlier work this paper cites.
J. Cavazos, G. Fursin, F. Agakov, E. Bonilla, M. F. P. O’Boyle, and O. Temam, “Rapidly selecting good compiler optimizations using performance counters,” in Proceedings of the International Symposium on Code Generation and Optimization , ser. CGO ’07, 2007
2007
Earlier work this paper cites.
C. Dubach, J. Cavazos, B. Franke, G. Fursin, M. F. O’Boyle, and O. Temam, “Fast compiler optimisation evaluation using code-feature based performance prediction,” in Proceedings of the 4th International Conference on Computing Frontiers , ser. CF ’07, 2007, pp. 131–142
2007
Earlier work this paper cites.
B. C. Lee, D. M. Brooks, B. R. de Supinski, M. Schulz, K. Singh, and S. A. McKee, “Methods of inference and learning for performance modeling of parallel applications,” in Proceedings of the 12th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming , ser. PPoPP ’07, 2007, pp. 249–258
2007
Earlier work this paper cites.
A. Phansalkar, A. Joshi, and L. K. John, “Analysis of redundancy and application balance in the spec cpu2006 benchmark suite,” in Proceedings of the 34th Annual International Symposium on Computer Architecture , ser. ISCA ’07, 2007, pp. 412–423
2007
Earlier work this paper cites.
S. Khan, P. Xekalakis, J. Cavazos, and M. Cintra, “Using predictivemodeling for cross-program design space exploration in multicore systems,” in Proceedings of the 16th International Conference on Parallel Architecture and Compilation Techniques . IEEE Computer Society, 2007, pp. 327–338
2007
Earlier work this paper cites.
K. Datta, M. Murphy, V. Volkov, S. Williams, J. Carter, L. Oliker, D. Patterson, J. Shalf, and K. Yelick, “Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures,” in Proceedings of the 2008 ACM/IEEE conference on Supercomputing , 2008, p. 4
2008
Earlier work this paper cites.
V. Volkov and J. W. Demmel, “Benchmarking GPUs to tune dense linear algebra,” in Proceedings of the 2008 ACM/IEEE Conference on Supercomputing , ser. SC ’08, 2008, pp. 31:1–31:11
2008
Earlier work this paper cites.
K. D. Cooper, T. J. Harvey, and T. Waterman, “An adaptive strategy for inline substitution,” in Proceedings of the Joint European Conferences on Theory and Practice of Software 17th International Conference on Compiler Construction , ser. CC’08/ETAPS’08, 2008, pp. 69–84
2008
Earlier work this paper cites.
K. Hoste and L. Eeckhout, “Cole: Compiler optimization level exploration,” in Proceedings of the 6th Annual IEEE/ACM International Symposium on Code Generation and Optimization , ser. CGO ’08, 2008, pp. 165–174
2008
Earlier work this paper cites.
M. Curtis-Maury, A. Shah, F. Blagojevic, D. S. Nikolopoulos, B. R. de Supinski, and M. Schulz, “Prediction models for multi-dimensional power-performance optimization on many cores,” in Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques , ser. PACT ’08, 2008, pp. 250–259
2008
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proceedings of the 25th International Conference on Machine Learning , ser. ICML ’08, 2008, pp. 1096–1103
2008
Cited alongside, same era.
E. Ipek, O. Mutlu, J. F. Martínez, and R. Caruana, “Self-optimizing memory controllers: A reinforcement learning approach,” in Computer Architecture, 2008. ISCA’08. 35th International Symposium on . IEEE, 2008, pp. 39–50
2008
Cited alongside, same era.
L.-N. Pouchet, C. Bastoul, A. Cohen, and J. Cavazos, “Iterative optimization in the polyhedral model: Part ii, multidimensional time,” in Proceedings of the 29th ACM SIGPLAN Conference on Programming Language Design and Implementation , ser. PLDI ’08, 2008, pp. 90–100
2008
Cited alongside, same era.
Slashdot. (2009) IBM releases open source machine learning compiler. [Online]. Available: https://tech.slashdot.org/story/09/07/03/0143233/ibm-releases-open-source-machine-learning-compiler
D. Grewe, Z. Wang, and M. F. O’Boyle, “OpenCL task partitioning in the presence of GPU contention,” in International Workshop on Languages and Compilers for Parallel Computing . Springer, 2013, pp. 87–101
2013
Later among the works it cites.
L. Tang, J. Mars, W. Wang, T. Dey, and M. L. Soffa, “Reqos: Reactive static/dynamic compilation for qos in warehouse scale computers,” in Proceedings of the Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems , ser. ASPLOS ’13, 2013, pp. 89–100
2013
Later among the works it cites.
P. Balaprakash, R. B. Gramacy, and S. M. Wild, “Active-learning-based surrogate models for empirical performance tuning,” in Cluster Computing (CLUSTER), 2013 IEEE International Conference on . IEEE, 2013, pp. 1–8
2013
Later among the works it cites.
M. Zuluaga, G. Sergent, A. Krause, and M. Püschel, “Active learning for multi-objective optimization,” in International Conference on Machine Learning , 2013, pp. 462–470
2013
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2009
Cited alongside, same era.
C.-K. Luk, S. Hong, and H. Kim, “Qilin: Exploiting parallelism on heterogeneous multiprocessors with adaptive mapping,” in Proceedings of the 42Nd Annual IEEE/ACM International Symposium on Microarchitecture , ser. MICRO 42, 2009, pp. 45–55
2009
Cited alongside, same era.
Z. Wang and M. F. O’Boyle, “Mapping parallelism to multi-cores: A machine learning based approach,” in Proceedings of the 14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming , ser. PPoPP ’09, 2009, pp. 75–84
2009
Cited alongside, same era.
Y. Liu, E. Z. Zhang, and X. Shen, “A cross-input adaptive framework for GPU program optimizations,” in 2009 IEEE International Symposium on Parallel Distributed Processing , 2009, pp. 1–10
2009
Cited alongside, same era.
H. Leather, E. Bonilla, and M. O’Boyle, “Automatic feature generation for machine learning based optimizing compilation,” in Proceedings of the 7th Annual IEEE/ACM International Symposium on Code Generation and Optimization , ser. CGO ’09, 2009, pp. 81–91
2009
Cited alongside, same era.
P. Lokuciejewski, F. Gedikli, P. Marwedel, and K. Morik, “Automatic WCET reduction by machine learning based heuristics for function inlining,” in 3rd Workshop on Statistical and Machine Learning Approaches to Architectures and Compilation (SMART) , 2009, pp. 1–15
2009
Cited alongside, same era.
A. Ganapathi, K. Datta, A. Fox, and D. Patterson, “A case for machine learning to optimize multicore performance,” in Proceedings of the First USENIX Conference on Hot Topics in Parallelism , ser. HotPar’09, 2009
2009
Cited alongside, same era.
H. Lee, Y. Largman, P. Pham, and A. Y. Ng, “Unsupervised feature learning for audio classification using convolutional deep belief networks,” in Proceedings of the 22Nd International Conference on Neural Information Processing Systems , ser. NIPS, 2009, pp. 1096–1104
2009
Cited alongside, same era.
J. Ansel, C. Chan, Y. L. Wong, M. Olszewski, Q. Zhao, A. Edelman, and S. Amarasinghe, “Petabricks: A language and compiler for algorithmic choice,” in ACM SIGPLAN Conference on Programming Language Design and Implementation , ser. PLDI ’09, 2009
2009
Cited alongside, same era.
Later among the works it cites.
A. Magni, C. Dubach, and M. O’Boyle, “Automatic optimization of thread-coarsening for graphics processors,” in Proceedings of the 23rd International Conference on Parallel Architectures and Compilation , ser. PACT ’14, 2014, pp. 455–466
2014
Later among the works it cites.
Y. Wen, Z. Wang, and M. O’Boyle, “Smart multi-task scheduling for OpenCL programs on CPU/GPU heterogeneous platforms,” in 21st Annual IEEE International Conference on High Performance Computing (HiPC 2014) . IEEE, 2014
2014
Later among the works it cites.
R. W. Moore and B. R. Childers, “Building and using application utility models to dynamically choose thread counts,” The Journal of Supercomputing , vol. 68, no. 3, pp. 1184–1213, 2014
2014
Later among the works it cites.
P. E. Bailey, D. K. Lowenthal, V. Ravi, B. Rountree, M. Schulz, and B. R. d. Supinski, “Adaptive configuration selection for power-constrained heterogeneous systems,” in 2014 43rd International Conference on Parallel Processing , 2014, pp. 371–380
2014
Later among the works it cites.
Z. Wang, D. Grewe, and M. F. P. O’boyle, “Automatic and portable mapping of data parallel programs to opencl for GPU-Based heterogeneous systems,” ACM Trans. Archit. Code Optim. , vol. 11, no. 4, pp. 42:1–42:26, 2014
2014
Later among the works it cites.
Z. Wang, G. Tournavitis, B. Franke, and M. F. O’boyle, “Integrating profile-driven parallelism detection and machine-learning-based mapping,” ACM Transactions on Architecture and Code Optimization (TACO) , vol. 11, no. 1, p. 2, 2014
2014
Later among the works it cites.
L. G. Martins, R. Nobre, A. C. Delbem, E. Marques, and J. a. M. Cardoso, “Exploration of compiler optimization sequences using clustering-based selection,” in Proceedings of the 2014 SIGPLAN/SIGBED Conference on Languages, Compilers and Tools for Embedded Systems , ser. LCTES ’14, 2014, pp. 63–72
2014
Later among the works it cites.
A. H. Ashouri, G. Mariani, G. Palermo, and C. Silvano, “A bayesian network approach for compiler auto-tuning for embedded processors,” in Embedded Systems for Real-time Multimedia (ESTIMedia), 2014 IEEE 12th Symposium on . IEEE, 2014, pp. 90–97
2014
Later among the works it cites.
M. K. Emani and M. O’Boyle, “Change detection based parallelism mapping: Exploiting offline models and online adaptation,” in Languages and Compilers for Parallel Computing: 27th International Workshop (LCPC 2014) , 2014, pp. 208–223
2014
Later among the works it cites.
E. Park, C. Kartsaklis, and J. Cavazos, “Hercules: Strong patterns towards more intelligent predictive modeling,” in 43rd International Conference on Parallel Processing , 2014, pp. 172–181
2014
Later among the works it cites.
C. Delimitrou and C. Kozyrakis, “Quasar: Resource-efficient and qos-aware cluster management,” in Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems , ser. ASPLOS ’14, 2014, pp. 127–144
2014
Later among the works it cites.
Y. Zhang, M. A. Laurenzano, J. Mars, and L. Tang, “Smite: Precise qos prediction on real-system smt processors to improve utilization in warehouse scale computers,” in Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture , ser. MICRO-47, 2014, pp. 406–418
2014
Later among the works it cites.
Y. David and E. Yahav, “Tracelet-based code search in executables,” in Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation , ser. PLDI ’14, 2014, pp. 349–360
2014
Later among the works it cites.
W. F. Ogilvie, P. Petoumenos, Z. Wang, and H. Leather, “Fast automatic heuristic construction using active learning,” in International Workshop on Languages and Compilers for Parallel Computing , 2014, pp. 146–160
2014
Later among the works it cites.
Y. Ding, J. Ansel, K. Veeramachaneni, X. Shen, U.-M. O’Reilly, and S. Amarasinghe, “Autotuning algorithmic choice for input sensitivity,” in Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation , ser. PLDI ’15, 2015, pp. 379–390
2015
Later among the works it cites.
S. Benedict, R. S. Rejitha, P. Gschwandtner, R. Prodan, and T. Fahringer, “Energy prediction of openmp applications using random forest modeling approach,” in 2015 IEEE International Parallel and Distributed Processing Symposium Workshop , 2015, pp. 1251–1260
2015
Later among the works it cites.
Y. LeCun, Y. Bengio, and G. Hinton, Deep Learning , 2015
2015
Later among the works it cites.
M. K. Emani and M. O’Boyle, “Celebrating diversity: A mixture of experts approach for runtime mapping in dynamic environments,” in Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation , ser. PLDI ’15, 2015, pp. 499–508
2015
Later among the works it cites.
Y. Luo, G. Tan, Z. Mo, and N. Sun, “Fast: A fast stencil autotuning framework based on an optimal-solution space model,” in Proceedings of the 29th ACM on International Conference on Supercomputing , 2015, pp. 187–196
2015
Later among the works it cites.
A. Bhattacharyya, G. Kwasniewski, and T. Hoefler, “Using compiler techniques to improve automatic performance modeling,” in 2015 International Conference on Parallel Architecture and Compilation (PACT) , 2015, pp. 468–479
2015
Later among the works it cites.
V. Petrucci, M. A. Laurenzano, J. Doherty, Y. Zhang, D. Mosse, J. Mars, and L. Tang, “Octopus-man: Qos-driven task management for heterogeneous multicores in warehouse-scale computers,” in 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA) . IEEE, 2015, pp. 246–258
2015
Later among the works it cites.
E. Wong, T. Liu, and L. Tan, “Clocom: Mining existing source code for automatic comment generation,” in Software Analysis, Evolution and Reengineering (SANER), 2015 IEEE 22nd International Conference on , 2015, pp. 380–389
2015
Later among the works it cites.
J. Kurzak, H. Anzt, M. Gates, and J. Dongarra, “Implementation and tuning of batched cholesky factorization and solve for nvidia gpus,” IEEE Transactions on Parallel and Distributed Systems , vol. 27, no. 7, 2016
2016
Later among the works it cites.
Y. M. Tsai, P. Luszczek, J. Kurzak, and J. Dongarra, “Performance-portable autotuning of opencl kernels for convolutional layers of deep neural networks,” in Workshop on Machine Learning in HPC Environments (MLHPC) , 2016, pp. 9–18
2016
Later among the works it cites.
R. Nobre, L. G. A. Martins, and J. a. M. P. Cardoso, “A graph-based iterative compiler pass selection and phase ordering approach,” in Proceedings of the 17th ACM SIGPLAN/SIGBED Conference on Languages, Compilers, Tools, and Theory for Embedded Systems , ser. LCTES 2016, 2016, pp. 21–30
2016
Later among the works it cites.
S. Schürmans, G. Onnebrink, R. Leupers, G. Ascheid, and X. Chen, “Frequency-aware esl power estimation for arm cortex-a9 using a black box processor model,” ACM Trans. Embed. Comput. Syst. , vol. 16, no. 1, pp. 26:1–26:26, 2016
2016
Later among the works it cites.
Y. Zhang, D. Meisner, J. Mars, and L. Tang, “Treadmill: Attributing the source of tail latency through precise load testing and statistical inference,” in Proceedings of the 43rd International Symposium on Computer Architecture , ser. ISCA ’16, 2016, pp. 456–468
2016
Later among the works it cites.
P.-J. Micolet, A. Smith, and C. Dubach, “A machine learning approach to mapping streaming workloads to dynamic multicore processors,” in ACM SIGPLAN Notices , vol. 51, no. 5, 2016, pp. 113–122
2016
Later among the works it cites.
E. Deniz and A. Sen, “Using machine learning techniques to detect parallel patterns of multi-threaded applications,” International Journal of Parallel Programming , vol. 44, no. 4, pp. 867–900, 2016
2016
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2016
2016
Later among the works it cites.
U. Garciarena and R. Santana, “Evolutionary optimization of compiler flag selection by learning and exploiting flags interactions,” in Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion , ser. GECCO ’16 Companion, 2016, pp. 1159–1166
2016
Later among the works it cites.
B. Porter, M. Grieves, R. Rodrigues Filho, and D. Leslie, “Rex: A development platform and online learning approach for runtime emergent software systems,” in Symposium on Operating Systems Design and Implementation . USENIX, November 2016, pp. 333–348
2016
Later among the works it cites.
2016
Later among the works it cites.
M. White, M. Tufano, C. Vendome, and D. Poshyvanyk, “Deep Learning Code Fragments for Code Clone Detection,” in ASE ’16 (31st IEEE/ACM International Conference on Automated Software Engineering) , 2016, pp. 87–98
2016
Later among the works it cites.
S. Venkataraman, Z. Yang, M. J. Franklin, B. Recht, and I. Stoica, “Ernest: Efficient performance prediction for large-scale advanced analytics.” in NSDI , 2016, pp. 363–378
2016
Later among the works it cites.
S. Sankaran, “Predictive modeling based power estimation for embedded multicore systems,” in Proceedings of the ACM International Conference on Computing Frontiers , ser. CF ’16, 2016, pp. 370–375
2016
Later among the works it cites.
N. J. Yadwadkar, B. Hariharan, J. E. Gonzalez, and R. Katz, “Multi-task learning for straggler avoiding predictive job scheduling,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 3692–3728, 2016
2016
Later among the works it cites.
Y. David, N. Partush, and E. Yahav, “Statistical similarity of binaries,” in Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation , ser. PLDI ’16, 2016, pp. 266–280
2016
Later among the works it cites.
J. Fowkes and C. Sutton, “Parameter-free probabilistic api mining across github,” in Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering , ser. FSE 2016, 2016, pp. 254–265
2016
Later among the works it cites.
A. T. Nguyen, M. Hilton, M. Codoban, H. A. Nguyen, L. Mast, E. Rademacher, T. N. Nguyen, and D. Dig, “Api code recommendation using statistical learning from fine-grained changes,” in Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering , ser. FSE 2016, 2016, pp. 511–522
2016
Later among the works it cites.
V. Raychev, P. Bielik, and M. Vechev, “Probabilistic model for code with decision trees,” in Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications , ser. OOPSLA 2016, 2016, pp. 731–747
2016
Later among the works it cites.
B. Bichsel, V. Raychev, P. Tsankov, and M. Vechev, “Statistical deobfuscation of android applications,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’16, 2016, pp. 343–355
2016
Later among the works it cites.
Y. Kang, J. Hauswald, C. Gao, A. Rovinski, T. Mudge, J. Mars, and L. Tang, “Neurosurgeon: Collaborative intelligence between the cloud and mobile edge,” in Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems , ser. ASPLOS ’17, 2017, pp. 615–629
2017
Later among the works it cites.
R. Rejitha, S. Benedict, S. A. Alex, and S. Infanto, “Energy prediction of cuda application instances using dynamic regression models,” Computing , pp. 1–26, 2017
2017
Later among the works it cites.
C. Cummins, P. Petoumenos, Z. Wang, and H. Leather, “End-to-end deep learning of optimization heuristics,” in The 26th International Conference on Parallel Architectures and Compilation Techniques (PACT) , ser. PACT ’17, 2017
2017
Later among the works it cites.
B. Taylor, V. S. Marco, and Z. Wang, “Adaptive optimization for OpenCL programs on embedded heterogeneous systems,” in The 18th Annual ACM SIGPLAN / SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems , ser. LCETS ’17, 2017
2017
Later among the works it cites.
M. Allamanis and C. Sutton, “A Survey of Machine Learning for Big Code and Naturalness,” 2017
2017
Later among the works it cites.
Y. Li, “Deep reinforcement learning: An overview,” CoRR , vol. abs/1701.07274, 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
V. S. Marco, B. Taylor, B. Porter, and Z. Wang, “Improving spark application throughput via memory aware task co-location: A mixture of experts approach,” in ACM/IFIP/USENIX Middleware conference , 2017
2017
Later among the works it cites.
C. Cummins, P. Petoumenos, Z. Wang, and H. Leather, “Synthesizing benchmarks for predictive modeling,” in Proceedings of the 2017 International Symposium on Code Generation and Optimization , ser. CGO ’17, 2017, pp. 86–99
2017
Later among the works it cites.
M. White, M. Tufano, M. Martínez, M. Monperrus, and D. Poshyvanyk, “Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities,” 2017
2017
Later among the works it cites.
A. H. Ashouri, A. Bignoli, G. Palermo, C. Silvano, S. Kulkarni, and J. Cavazos, “MiCOMP: Mitigating the compiler phase-ordering problem using optimization sub-sequences and machine learning,” ACM Trans. Archit. Code Optim. , vol. 14, no. 3, pp. 29:1–29:28, 2017
2017
Later among the works it cites.
J. Ren, L. Gao, H. Wang, and Z. Wang, “Optimise web browsing on heterogeneous mobile platforms: a machine learning based approach,” in IEEE International Conference on Computer Communications (INFOCOM), 2017 , ser. INFOCOM 2017, 2017
2017
Later among the works it cites.
W. F. Ogilvie, P. Petoumenos, Z. Wang, and H. Leather, “Minimizing the cost of iterative compilation with active learning,” in Proceedings of the 2017 International Symposium on Code Generation and Optimization , ser. CGO ’17, 2017, pp. 245–256
2017
Later among the works it cites.
P. Zhang, J. Fang, T. Tang, C. Yang, and Z. Wang, “Auto-tuning streamed applications on Intel Xeon Phi,” in 32nd IEEE International Parallel & Distributed Processing Symposium , ser. IPDPS, 2018
2018
Closest in time.
S. Chen, J. Fang, D. Chen, C. Xu, and Z. Wang, “Adaptive optimization of sparse matrix-vector multiplication on emerging many-core architectures,” in The 20th IEEE International Conference on High Performance Computing and Communications (HPCC) , 2018
2018
Closest in time.
N. Mishra and C. Imes, “CALOREE: Learning control for predictable latency and low energy,” in Proceedings of the 23th International Conference on Architectural Support for Programming Languages and Operating Systems , ser. ASPLOS, 2018
2018
Closest in time.
J. Kukunas, R. D. Cupper, and G. M. Kapfhammer, “A genetic algorithm to improve linux kernel performance on resource-constrained devices,” in Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation , ser. GECCO ’10, 2010, pp. 2095–2096
2096
Closest in time.