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references.bib
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% Julia Language
@inproceedings{knopp2014experimental,
title={Experimental multi-threading support for the Julia programming language},
author={Knopp, Tobias},
booktitle={Proceedings of the 1st First Workshop for High Performance Technical Computing in Dynamic Languages},
pages={1--5},
year={2014},
organization={IEEE Press}
}
@article{bezanson2014julia,
title={Julia: A fresh approach to numerical computing},
author={Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B},
journal={arXiv preprint arXiv:1411.1607},
year={2014}
}
@article{bezanson2012julia,
title={Julia: A fast dynamic language for technical computing},
author={Bezanson, Jeff and Karpinski, Stefan and Shah, Viral B and Edelman, Alan},
journal={arXiv preprint arXiv:1209.5145},
year={2012}
}
@article{lubin2015computing,
title={Computing in operations research using Julia},
author={Lubin, Miles and Dunning, Iain},
journal={INFORMS Journal on Computing},
volume={27},
number={2},
pages={238--248},
year={2015},
publisher={INFORMS}
}
@article{dunning2015jump,
title={JuMP: A Modeling Language for Mathematical Optimization},
author={Dunning, Iain and Huchette, Joey and Lubin, Miles},
journal={arXiv preprint arXiv:1508.01982},
year={2015}
}
% Machine Learning / Tools
@inproceedings{holmes1994weka,
title={Weka: A machine learning workbench},
author={Holmes, Geoffrey and Donkin, Andrew and Witten, Ian H},
booktitle={Intelligent Information Systems, 1994. Proceedings of the 1994 Second Australian and New Zealand Conference on},
pages={357--361},
year={1994},
organization={IEEE}
}
% GPU Analytical Performance Modelling
@inproceedings{amaris2015simple,
title={A simple bsp-based model to predict execution time in gpu applications},
author={Amaris, Marcos and Cordeiro, Daniel and Goldman, Alfredo and de Camargo, Raphael Y},
booktitle={High Performance Computing (HiPC), 2015 IEEE 22nd International Conference on},
pages={285--294},
year={2015},
organization={IEEE}
}
@article{dao2015performance,
title={A performance model for gpus with caches},
author={Dao, Thanh Tuan and Kim, Jungwon and Seo, Sangmin and Egger, Bernhard and Lee, Jaejin},
journal={IEEE Transactions on Parallel and Distributed Systems},
volume={26},
number={7},
pages={1800--1813},
year={2015},
publisher={IEEE}
}
@inproceedings{picchi2015impact,
title={Impact of L2 cache locking on GPU performance},
author={Picchi, John and Zhang, Wei},
booktitle={SoutheastCon 2015},
pages={1--4},
year={2015},
organization={IEEE}
}
@article{sampaio2013divergence,
title={Divergence analysis},
author={Sampaio, Diogo and Souza, Rafael Martins de and Collange, Sylvain and Pereira, Fernando Magno Quint{\~a}o},
journal={ACM Transactions on Programming Languages and Systems (TOPLAS)},
volume={35},
number={4},
pages={13},
year={2013},
publisher={ACM}
}
@inproceedings{baghsorkhi2010adaptive,
title={An adaptive performance modeling tool for GPU architectures},
author={Baghsorkhi, Sara S and Delahaye, Matthieu and Patel, Sanjay J and Gropp, William D and Hwu, Wen-mei W},
booktitle={ACM Sigplan Notices},
volume={45},
number={5},
pages={105--114},
year={2010},
organization={ACM}
}
@inproceedings{zhang2011quantitative,
title={A quantitative performance analysis model for GPU architectures},
author={Zhang, Yao and Owens, John D},
booktitle={High Performance Computer Architecture (HPCA), 2011 IEEE 17th International Symposium on},
pages={382--393},
year={2011},
doi = "10.1109/HPCA.2011.5749745",
issn = "1530-0897",
organization={IEEE}
}
% Autotuning in GPUs
@article{alves2004bsp,
title={BSP/CGM algorithms for maximum subsequence and maximum subarray},
author={Alves, Carlos ER and C{\'a}ceres, Edson N and Song, Siang W},
journal={PVM/MPI},
volume={3241},
pages={139--146},
year={2004},
publisher={Springer}
}
@inproceedings{ferreira2014parallel,
title={A parallel maximum subarray algorithm on gpus},
author={Ferreira, Cleber S and Camargo, Raphael Y and Song, Siang Wun},
booktitle={Computer Architecture and High Performance Computing Workshop (SBAC-PADW), 2014 International Symposium on},
pages={12--17},
year={2014},
organization={IEEE}
}
@inproceedings{grauer2012auto,
title={Auto-tuning a high-level language targeted to GPU codes},
author={Grauer-Gray, Scott and Xu, Lifan and Searles, Robert and Ayalasomayajula, Sudhee and Cavazos, John},
booktitle={Innovative Parallel Computing (InPar), 2012},
pages={1--10},
year={2012},
organization={IEEE}
}
@incollection{li2009note,
title={A note on auto-tuning GEMM for GPUs},
author={Li, Yinan and Dongarra, Jack and Tomov, Stanimire},
booktitle={Computational Science--ICCS 2009},
pages={884--892},
year={2009},
publisher={Springer}
}
@inproceedings{guo2010auto,
title={Auto-tuning CUDA parameters for sparse matrix-vector multiplication on gpus},
author={Guo, Ping and Wang, Liqiang},
booktitle={Computational and Information Sciences (ICCIS), 2010 International Conference on},
pages={1154--1157},
year={2010},
organization={IEEE}
}
@inproceedings{chaparala2015autotuning,
title={Autotuning GPU-accelerated QAP Solvers for Power and Performance},
author={Chaparala, Abhilash and Novoa, Clara and Qasem, Apan},
booktitle={High Performance Computing and Communications (HPCC), 2015 IEEE 7th International Symposium on Cyberspace Safety and Security (CSS), 2015 IEEE 12th International Conferen on Embedded Software and Systems (ICESS), 2015 IEEE 17th International Conference on},
pages={78--83},
year={2015},
organization={IEEE}
}
@inproceedings{sedaghati2015automatic,
title={Automatic selection of sparse matrix representation on GPUs},
author={Sedaghati, Naser and Mu, Te and Pouchet, Louis-No{\"e}l and Parthasarathy, Srinivasan and Sadayappan, P},
booktitle={Proceedings of the 29th ACM on International Conference on Supercomputing},
pages={99--108},
year={2015},
organization={ACM}
}
@article{jia2015gpu,
title={GPU Performance and Power Tuning Using Regression Trees},
author={Jia, Wenhao and Garza, Elba and Shaw, Kelly A and Martonosi, Margaret},
journal={ACM Transactions on Architecture and Code Optimization (TACO)},
volume={12},
number={2},
pages={13},
year={2015},
publisher={ACM}
}
@inproceedings{falch2015machine,
title={Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability},
author={Falch, Thomas L and Elster, Anne C},
booktitle={Parallel and Distributed Processing Symposium Workshop (IPDPSW), 2015 IEEE International},
pages={1231--1240},
year={2015},
organization={IEEE}
}
@inproceedings{belviranli2015peerwave,
title={PeerWave: Exploiting Wavefront Parallelism on GPUs with Peer-SM Synchronization},
author={Belviranli, Mehmet E and Deng, Peng and Bhuyan, Laxmi N and Gupta, Rajiv and Zhu, Qi},
booktitle={Proceedings of the 29th ACM on International Conference on Supercomputing},
pages={25--35},
year={2015},
organization={ACM}
}
@inproceedings{wahib2015automated,
title={Automated GPU Kernel Transformations in Large-Scale Production Stencil Applications},
author={Wahib, Mohamed and Maruyama, Naoya},
booktitle={Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing},
pages={259--270},
year={2015},
organization={ACM}
}
% Parallel Computing
@inproceedings{virouleau2014evaluation,
title={Evaluation of OpenMP dependent tasks with the KASTORS benchmark suite},
author={Virouleau, Philippe and Brunet, Pierrick and Broquedis, Fran{\c{c}}ois and Furmento, Nathalie and Thibault, Samuel and Aumage, Olivier and Gautier, Thierry},
booktitle={International Workshop on OpenMP},
pages={16--29},
year={2014},
organization={Springer}
}
@inproceedings{schardl2017tapir,
title={Tapir: Embedding Fork-Join Parallelism into LLVM's Intermediate Representation},
author={Schardl, Tao B and Moses, William S and Leiserson, Charles E},
booktitle={Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming},
pages={249--265},
year={2017},
organization={ACM}
}
@article{asanovic2009view,
title={A view of the parallel computing landscape},
author={Asanovic, Krste and Bodik, Rastislav and Demmel, James and Keaveny, Tony and Keutzer, Kurt and Kubiatowicz, John and Morgan, Nelson and Patterson, David and Sen, Koushik and Wawrzynek, John and others},
journal={Communications of the ACM},
volume={52},
number={10},
pages={56--67},
year={2009},
publisher={ACM}
}
@techreport{asanovic2006landscape,
title={The landscape of parallel computing research: A view from berkeley},
author={Asanovic, Krste and Bodik, Ras and Catanzaro, Bryan Christopher and Gebis, Joseph James and Husbands, Parry and Keutzer, Kurt and Patterson, David A and Plishker, William Lester and Shalf, John and Williams, Samuel Webb and others},
year={2006},
institution={Technical Report UCB/EECS-2006-183, EECS Department, University of California, Berkeley}
}
@inproceedings{che2009rodinia,
title={Rodinia: A benchmark suite for heterogeneous computing},
author={Che, Shuai and Boyer, Michael and Meng, Jiayuan and Tarjan, David and Sheaffer, Jeremy W and Lee, Sang-Ha and Skadron, Kevin},
booktitle={Workload Characterization, 2009. IISWC 2009. IEEE International Symposium on},
pages={44--54},
year={2009},
organization={IEEE}
}
@article{dastgeer2015smart,
title={Smart containers and skeleton programming for GPU-based systems},
author={Dastgeer, Usman and Kessler, Christoph},
journal={International Journal of Parallel Programming},
pages={1--25},
year={2015},
publisher={Springer}
}
@article{markov2014limits,
title={Limits on fundamental limits to computation},
author={Markov, Igor L},
journal={Nature},
volume={512},
number={7513},
pages={147--154},
year={2014},
publisher={Nature Publishing Group}
}
% Bandit Techniques
@inproceedings{cohen2017learning,
title={Learning with bandit feedback in potential games},
author={Cohen, Johanne and H{\'e}liou, Am{\'e}lie and Mertikopoulos, Panayotis},
booktitle={Proceedings of the 31th International Conference on Neural Information Processing Systems},
year={2017}
}
% Autotuning (Umbrella)
% TODO: Create meaningful groups of papers.
@inproceedings{balaprakash2011can,
title={Can search algorithms save large-scale automatic performance tuning?},
author={Balaprakash, Prasanna and Wild, Stefan M and Hovland, Paul D},
booktitle={ICCS},
pages={2136--2145},
year={2011}
}
@inproceedings{balaprakash2012experimental,
title={An experimental study of global and local search algorithms in empirical performance tuning},
author={Balaprakash, Prasanna and Wild, Stefan M and Hovland, Paul D},
booktitle={International Conference on High Performance Computing for Computational Science},
pages={261--269},
year={2012},
organization={Springer}
}
@inproceedings{yi2007poet,
title={{POET}: Parameterized optimizations for empirical tuning},
author={Yi, Qing and Seymour, Keith and You, Haihang and Vuduc, Richard and Quinlan, Dan},
booktitle={Parallel and Distributed Processing Symposium, 2007. IPDPS 2007. IEEE International},
pages={1--8},
year={2007},
organization={IEEE}
}
@inproceedings{hartono2009annotation,
title={Annotation-based empirical performance tuning using {Orio}},
author={Hartono, Albert and Norris, Boyana and Sadayappan, Ponnuswamy},
booktitle={Parallel \& Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on},
pages={1--11},
year={2009},
organization={IEEE}
}
@inproceedings{zhang2018auto,
title={Auto-tuning streamed applications on Intel Xeon Phi},
author={Zhang, Peng and Fang, Jianbin and Tang, Tao and Yang, Canqun and Wang, Zheng},
booktitle={2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
year={2018},
organization={IEEE}
}
@inproceedings{zeng2018online,
title={Online Tuning of Parallelism Degree in Parallel Nesting Transactional Memory},
author={Zeng, Jingna and Romano, Paolo and Barreto, Joao and Rodrigues, Lu{\i}s and Haridi, Seif},
booktitle={2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
year={2018},
organization={IEEE}
}
@inproceedings{xu2018taming,
title={Taming the" Monster": Overcoming Program Optimization Challenges on SW26010 Through Precise Performance Modeling},
author={Xu, Shizhen and Xu, Yuanchao and Xue, Wei and Shen, Xipeng and Zheng, Fang and Huang, Xiaomeng and Yang, Guangwen},
booktitle={2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
pages={763--773},
year={2018},
organization={IEEE}
}
@article{ashouri2016cobayn,
title={Cobayn: Compiler autotuning framework using bayesian networks},
author={Ashouri, Amir Hossein and Mariani, Giovanni and Palermo, Gianluca and Park, Eunjung and Cavazos, John and Silvano, Cristina},
journal={ACM Transactions on Architecture and Code Optimization (TACO)},
volume={13},
number={2},
pages={21},
year={2016},
publisher={ACM}
}
@inproceedings{balaprakash2013active,
title={Active-learning-based surrogate models for empirical performance tuning},
author={Balaprakash, Prasanna and Gramacy, Robert B and Wild, Stefan M},
booktitle={Cluster Computing (CLUSTER), 2013 IEEE International Conference on},
pages={1--8},
year={2013},
organization={IEEE}
}
@book{suda2010software,
title = {Software Automatic Tuning: From Concepts to State-of-the-Art Results},
author = {Reiji Suda, Ken Naono, Keita Teranishi, John Cavazos (auth.), Ken Naono, Keita Teranishi, John Cavazos, Reiji Suda (eds.)},
publisher = {Springer-Verlag New York},
isbn = {1441969349,9781441969347},
year = {2010},
edition = {1}
}
@inproceedings{nelson2015generating,
title={Generating efficient tensor contractions for gpus},
author={Nelson, Thomas and Rivera, Axel and Balaprakash, Prasanna and Hall, Mary and Hovland, Paul D and Jessup, Elizabeth and Norris, Boyana},
booktitle={2015 44th International Conference on Parallel Processing (ICPP)},
pages={969--978},
year={2015},
organization={IEEE}
}
@inproceedings{seymour2008comparison,
title={A comparison of search heuristics for empirical code optimization.},
author={Seymour, Keith and You, Haihang and Dongarra, Jack},
booktitle={CLUSTER},
pages={421--429},
year={2008}
}
@article{knijnenburg2003combined,
title={Combined selection of tile sizes and unroll factors using iterative compilation},
author={Knijnenburg, Peter MW and Kisuki, Toru and O'Boyle, Michael FP},
journal={The Journal of Supercomputing},
volume={24},
number={1},
pages={43--67},
year={2003},
publisher={Springer}
}
@article{balaprakash2018autotuning,
title={Autotuning in High-Performance Computing Applications},
author={Balaprakash, Prasanna and Dongarra, Jack and Gamblin, Todd and Hall, Mary and Hollingsworth, Jeffrey K and Norris, Boyana and Vuduc, Richard},
journal={Proceedings of the IEEE},
number={99},
pages={1--16},
year={2018},
publisher={IEEE}
}
@article{vu2017surrogate,
title={Surrogate-based methods for black-box optimization},
author={Vu, Ky Khac and D'Ambrosio, Claudia and Hamadi, Youssef and Liberti, Leo},
journal={International Transactions in Operational Research},
volume={24},
number={3},
pages={393--424},
year={2017},
publisher={Wiley Online Library}
}
@article{castro2013adaptive,
title={Adaptive sampling for performance characterization of application kernels},
author={Castro, Pablo and Petit, Eric and Farjallah, Asma and Jalby, William},
journal={Concurrency and Computation: Practice and Experience},
volume={25},
number={17},
pages={2345--2362},
year={2013},
publisher={Wiley Online Library}
}
@article{videau2017boast,
title={{BOAST}: A metaprogramming framework to produce portable and efficient computing kernels for HPC applications},
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@mastersthesis{masnada2016semi,
TITLE = {{Semi-Automatic Performance Optimization of HPC Kernels}},
AUTHOR = {Masnada, Steven Quinito},
URL = {https://hal.inria.fr/hal-01579422},
SCHOOL = {{Universit{\'e} Grenoble Alpes}},
YEAR = {2016},
MONTH = Jun,
KEYWORDS = {Perfomance tuning ; Auto-tuning ; Linear regression ; Design of experiments},
HAL_ID = {hal-01579422},
HAL_VERSION = {v1},
}
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