Automated empirical optimizations of software and the ATLAS project

LAPACK Users' Guide

Anderson, E., Bai, Z., Bischof, C., Blackford, L. S., Demmel, J., Dongarra, J. J., Croz, J. D., Greenbaum, A., Hammarling, S. J., McKenney, A., et al. (1999) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898719604 · Cited by 4,437 · see citing works

A set of level 3 basic linear algebra subprograms

Dongarra, J. J., Croz, J. D., Hammarling, S. J., & Duff, I. (1990) · ACM Transactions on Mathematical Software · doi:10.1145/77626.79170 · Cited by 1,822 · see citing works

FFTW: an adaptive software architecture for the FFT

Frigo, M., & Johnson, S. G. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.681704 · Cited by 1,803 · see citing works

Basic Linear Algebra Subprograms for Fortran Usage

Lawson, C. L., Hanson, R. J., Kincaid, D. R., & Krogh, F. T. (1979) · ACM Transactions on Mathematical Software · doi:10.1145/355841.355847 · Cited by 1,626 · see citing works

High-Performance Compilers for Parallel Computing

Wolfe, M., Shanklin, C., & Ortega, L. (1995) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,288 · see citing works

An extended set of FORTRAN basic linear algebra subprograms

Dongarra, J. J., Croz, J. D., Hammarling, S. J., & Hanson, R. J. (1988) · ACM Transactions on Mathematical Software · doi:10.1145/42288.42291 · Cited by 882 · see citing works

Automatically Tuned Linear Algebra Software

Whaley, R. C., & Dongarra, J. J. (1998) · Conference on High Performance Computing (Supercomputing) · doi:10.5555/509058.509096 · Cited by 867 · see citing works

Optimizing supercompilers for supercomputers

Wolfe, M. (2020) · ACM International Conference on Supercomputing (ICS) · doi:10.1145/3392717.3400034 · Cited by 839 · see citing works

Automatically Tuned Linear Algebra Software

Whaley, R., & Dongarra, J. J. (1998) · doi:10.1109/sc.1998.10004 · Cited by 531 · see citing works

Optimizing matrix multiply using PHiPAC

Bilmes, J., Asanović, K., Chin, C., & Demmel, J. (2014) · International Conference on Supercomputing · doi:10.1145/2591635.2667174 · Cited by 454 · see citing works

A fast Fourier transform compiler

Frigo, M. (1999) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/301618.301661 · Cited by 445 · see citing works

Optimizing matrix multiply using PHiPAC

Bilmes, J., Asanović, K., Chin, C., & Demmel, J. (1997) · International Conference on Supercomputing · doi:10.1145/263580.263662 · Cited by 310 · see citing works

The Fastest Fourier Transform in the West

Frigo, M., & Johnson, S. G. (1997) · doi:10.21236/ada479065 · Cited by 249 · see citing works

GEMM-based level 3 BLAS

Kågström, B., Ling, P., & Loan, C. F. V. (1998) · ACM Transactions on Mathematical Software · doi:10.1145/292395.292412 · Cited by 204 · see citing works

Algorithm 656: an extended set of basic linear algebra subprograms: model implementation and test programs

Dongarra, J. J., Croz, J. D., Hammarling, S. J., & Hanson, R. J. (1988) · ACM Transactions on Mathematical Software · doi:10.1145/42288.42292 · Cited by 185 · see citing works

Recursive blocked data formats and BLAS’s for dense linear algebra algorithms

Gustavson, F. G., Henriksson, A., Jonsson, I., Kågström, B., & Ling, P. (1998) · Lecture notes in computer science · doi:10.1007/bfb0095337 · Cited by 68 · see citing works

The Spectral Decomposition of Nonsymmetric Matrices on Distributed Memory Parallel Computers

Bai, Z., Demmel, J. W., Dongarra, J. J., Petitet, A. P., Robinson, H., & Stanley, K. S. (1997) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827595281368 · Cited by 39 · see citing works

Telescoping languages: a compiler strategy for implementation of high-level domain-specific programming systems

Kennedy, K. (2002) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2000.845999 · Cited by 28 · see citing works

Superscalar GEMM-based level 3 BLAS—The on-going evolution of a portable and high-performance library

Gustavson, F. G., Henriksson, A., Jonsson, I., Kågström, B., & Ling, P. (1998) · Lecture notes in computer science · doi:10.1007/bfb0095338 · Cited by 24 · see citing works

A parallel block implementation of Level-3 BLAS for MIMD vector processors

Daydé, M. J., Duff, I., & Petitet, A. P. (1994) · ACM Transactions on Mathematical Software · doi:10.1145/178365.174413 · Cited by 20 · see citing works

The IBM RISC System/6000 and linear algebra operations

Dongarra, J. J., & Mays, P. (1991) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 13 · see citing works

Scaling distributed machine learning with the parameter server

Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A. A., Josifovski, V., Long, J. E., Shekita, E. J., & Su, B.-Y. (2014) · Operating Systems Design and Implementation · doi:10.5555/2685048.2685095 · Cited by 1,178 · see citing works

Brook for GPUs

Buck, I., Foley, T., Horn, D. R., Sugerman, J., Fatahalian, K., Houston, M., & Hanrahan, P. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015800 · Cited by 1,163 · see citing works

Scaling Distributed Machine Learning with the Parameter Server

Li, M. (2014) · International Conference on Big Data Science and Computing (BigDataScience) · doi:10.1145/2640087.2644155 · Cited by 1,000 · see citing works

A practical automatic polyhedral parallelizer and locality optimizer

Bondhugula, U. K. R., Hartono, A., Ramanujam, J., & Sadayappan, P. (2008) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/1375581.1375595 · Cited by 881 · see citing works

SPIRAL: Code Generation for DSP Transforms

Puschel, M., Moura, J. M. F., Johnson, J. R., Padua, D., Veloso, M. M., Singer, B. W., Xiong, J., Franchetti, F., Gacic, A., Voronenko, Y., et al. (2005) · Proceedings of the IEEE · doi:10.1109/jproc.2004.840306 · Cited by 826 · see citing works

The LINPACK Benchmark: past, present and future

Dongarra, J. J., Luszczek, P., & Petitet, A. (2003) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.728 · Cited by 813 · see citing works

Anatomy of high-performance matrix multiplication

Goto, K., & Geijn, R. A. (2008) · ACM Transactions on Mathematical Software · doi:10.1145/1356052.1356053 · Cited by 751 · see citing works

Large-scale deep unsupervised learning using graphics processors

Raina, R., Madhavan, A., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553486 · Cited by 742 · see citing works

PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation

Klöckner, A., Pinto, N., Lee, Y., Catanzaro, B., Ivanov, P., & Fasih, A. R. (2011) · Parallel Computing · doi:10.1016/j.parco.2011.09.001 · Cited by 571 · see citing works

A class of parallel tiled linear algebra algorithms for multicore architectures

Buttari, A., Julien Langou, F., Jakub Kurzak, A., & Dongarra, J. J. (2008) · Parallel Computing · doi:10.1016/j.parco.2008.10.002 · Cited by 524 · see citing works

SPINEVOLUTION: A powerful tool for the simulation of solid and liquid state NMR experiments

Veshtort, M., & Griffin, R. G. (2005) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2005.07.018 · Cited by 504 · see citing works

Qilin

Luk, C.-K., Hong, S., & Kim, H. (2009) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/1669112.1669121 · Cited by 501 · see citing works

OSKI: A library of automatically tuned sparse matrix kernels

Vuduc, R., Demmel, J. W., & Yelick, K. A. (2005) · Journal of Physics: Conference Series · doi:10.1088/1742-6596/16/1/071 · Cited by 490 · see citing works

Auto-tuning a high-level language targeted to GPU codes

Grauer-Gray, S., Xu, L., Searles, R., Ayalasomayajula, S., & Cavazos, J. (2012) · Innovative Parallel Computing (InPar) · doi:10.1109/inpar.2012.6339595 · Cited by 476 · see citing works

Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures

Datta, K., Murphy, M., Volkov, V. M., Williams, S., Carter, J. T., Oliker, L., Patterson, D. A., Shalf, J. M., & Yelick, K. (2008) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.5555/1413370.1413375 · Cited by 463 · see citing works

Memory---Sequoia

Fatahalian, K., Dally, W. J., Hanrahan, P., Horn, D. R., Knight, T. J., Leem, L., Houston, M., Park, J. Y., Erez, M., Ren, M., et al. (2006) · ACM/IEEE Conference on Supercomputing (SC) · doi:10.1145/1188455.1188543 · Cited by 457 · see citing works

Generating Efficient Quantum Chemistry Codes for Novel Architectures

Titov, A. V., Ufimtsev, I. S., Luehr, N., & Martı́nez, T. J. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300321a · Cited by 406 · see citing works

Ernest: efficient performance prediction for large-scale advanced analytics

Venkataraman, S., Yang, Z., Franklin, M. J. M., Recht, B., & Stoica, I. G. (2016) · Networked Systems Design and Implementation · Cited by 373 · see citing works

Evaluation of the Raw Microprocessor

Taylor, M. B., Lee, W., Miller, J. E., Wentzlaff, D., Bratt, I., Greenwald, B., Hoffmann, H., Johnson, P., Kim, J. Z., Psota, J., et al. (2004) · ACM SIGARCH Computer Architecture News · doi:10.1145/1028176.1006733 · Cited by 353 · see citing works

Sparsity: Optimization Framework for Sparse Matrix Kernels

Im, E.-J., Yelick, K., & Vuduc, R. W. (2004) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342004041296 · Cited by 337 · see citing works

Nodal discontinuous Galerkin methods on graphics processors

Klöckner, A., Warburton, T., Bridge, J., & Hesthaven, J. S. (2009) · Journal of Computational Physics · doi:10.1016/j.jcp.2009.06.041 · Cited by 321 · see citing works

Triton: an intermediate language and compiler for tiled neural network computations

Tillet, P., Kung, H. T., & Cox, D. (2019) · Proceedings of the 3rd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages · doi:10.1145/3315508.3329973 · Cited by 318 · see citing works

From CUDA to OpenCL: Towards a performance-portable solution for multi-platform GPU programming

Du, P., Weber, R., Łuszczek, P., Tomov, S. Z., Peterson, G. D., & Dongarra, J. J. (2011) · Parallel Computing · doi:10.1016/j.parco.2011.10.002 · Cited by 312 · see citing works

Understanding the efficiency of GPU algorithms for matrix-matrix multiplication

Fatahalian, K., Sugerman, J., & Hanrahan, P. (2004) · Eurographics Conference on Graphics Hardware · doi:10.1145/1058129.1058148 · Cited by 307 · see citing works

A catalog of stream processing optimizations

Hirzel, M., Soulé, R., Schneider, S., Gedik, B., & Grimm, R. (2014) · ACM Computing Surveys · doi:10.1145/2528412 · Cited by 299 · see citing works

Mapping chemical and bonding information using multivariate analysis of electron energy-loss spectrum images

Bosman, M., Watanabe, M., Alexander, D. T. L., & Keast, V. J. (2006) · Ultramicroscopy · doi:10.1016/j.ultramic.2006.04.016 · Cited by 294 · see citing works

Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures

Datta, K., Murphy, M., Volkov, V. M., Williams, S., Carter, J. T., Oliker, L., Patterson, D. A., Shalf, J. M., & Yelick, K. (2008) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2008.5222004 · Cited by 280 · see citing works

Lightweight modular staging

Rompf, T., & Odersky, M. (2010) · International Conference on Generative Programming and Component Engineering (GPCE) · doi:10.1145/1868294.1868314 · Cited by 277 · see citing works

Minimizing development and maintenance costs in supporting persistently optimized BLAS

Whaley, R. C., & Petitet, A. P. (2004) · Software Practice and Experience · doi:10.1002/spe.626 · Cited by 250 · see citing works

Fast SVM training algorithm with decomposition on very large data sets

Dong, J.-X., Krzyzak, A., & Suen, C. Y. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.77 · Cited by 230 · see citing works

Automatic performance tuning of sparse matrix kernels

Vuduc, R. W., & Demmel, J. W. (2003) · Cited by 226 · see citing works

Programming by optimization

Hoos, H. H. (2012) · Communications of the ACM · doi:10.1145/2076450.2076469 · Cited by 221 · see citing works

Sequoia: Programming the Memory Hierarchy

Fatahalian, K., Knight, T. J., Houston, M., Erez, M., Horn, D. R., Leem, L., Park, J., Ren, M., Aiken, A., Dally, W. J., et al. (2006) · ACM/IEEE Conference on Supercomputing (SC) · doi:10.1109/sc.2006.55 · Cited by 221 · see citing works

An auto-tuning framework for parallel multicore stencil computations

Kamil, S., Chan, C., Oliker, L., Shalf, J., & Williams, S. (2010) · 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS) · doi:10.1109/ipdps.2010.5470421 · Cited by 209 · see citing works

Self-Adapting Linear Algebra Algorithms and Software

Demmel, J., Dongarra, J., Eijkhout, V., Fuentes, E., Petitet, A., Vuduc, R., Whaley, R. C., & Yelick, K. (2005) · Proceedings of the IEEE · doi:10.1109/jproc.2004.840848 · Cited by 204 · see citing works

AUGEM

Wang, Q., Zhang, X., Zhang, Y., & Yi, Q. (2013) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/2503210.2503219 · Cited by 204 · see citing works

LU-GPU: Efficient Algorithms for Solving Dense Linear Systems on Graphics Hardware

Galoppo, N., Govindaraju, N. K., Henson, M., & Manocha, D. (2005) · ACM/IEEE Conference on Supercomputing (SC) · doi:10.1109/sc.2005.42 · Cited by 200 · see citing works

A Fast Direct Solver for Structured Linear Systems by Recursive Skeletonization

Ho, K. L., & Greengard, L. (2012) · SIAM Journal on Scientific Computing · doi:10.1137/120866683 · Cited by 190 · see citing works

Automatic online tuning for fast Gaussian summation

Morariu, V. I., Srinivasan, B. V., Raykar, V. C., Ramani Duraiswami, B., & Davis, L. S. (2008) · Cited by 189 · see citing works

A Note on Auto-tuning GEMM for GPUs

Li, Y., Dongarra, J. J., & Tomov, S. Z. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01970-8_89 · Cited by 188 · see citing works

A Heterogeneous Parallel Framework for Domain-Specific Languages

Brown, K. J., Sujeeth, A. K., Lee, H. J., Rompf, T., Chafi, H., Odersky, M., & Olukotun, K. (2011) · International Conference on Parallel Architectures and Compilation Techniques (PACT) · doi:10.1109/pact.2011.15 · Cited by 188 · see citing works

An Improved Magma Gemm For Fermi Graphics Processing Units

Nath, R., Tomov, S. Z., & Dongarra, J. J. (2010) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342010385729 · Cited by 185 · see citing works

Model-driven Level 3 BLAS Performance Optimization on Loongson 3A Processor

Xianyi, Z., Wang, Q., & Yunquan, Z. (2012) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2012.97 · Cited by 182 · see citing works

Parallel tiled QR factorization for multicore architectures

Buttari, A., Julien Langou, F., Jakub Kurzak, A., & Dongarra, J. J. (2008) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.1301 · Cited by 178 · see citing works

SEEDS: a software engineer's energy-optimization decision support framework

Manotas, I., Pollock, L., & Clause, J. (2014) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2568225.2568297 · Cited by 170 · see citing works

Finding effective compilation sequences

Almagor, L., Cooper, K. D., Grosul, A., Harvey, T. J., Reeves, S. W., Subramanian, D., Torczon, L., & Waterman, T. (2004) · ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES) · doi:10.1145/997163.997196 · Cited by 169 · see citing works

Recursive Blocked Algorithms and Hybrid Data Structures for Dense Matrix Library Software

Elmroth, E., Gustavson, F. G., Jonsson, I., & Kågström, B. (2004) · SIAM Review · doi:10.1137/s0036144503428693 · Cited by 159 · see citing works

Implications of a metric for performance portability

Pennycook, S. J., Sewall, J. D., & Lee, V. W. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.08.007 · Cited by 158 · see citing works

FreeContact: fast and free software for protein contact prediction from residue co-evolution

Kaján, L., Hopf, T. A., Kalaš, M., Marks, D. S., & Rost, B. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-85 · Cited by 152 · see citing works

Lightweight modular staging

Rompf, T., & Odersky, M. (2012) · Communications of the ACM · doi:10.1145/2184319.2184345 · Cited by 152 · see citing works

Closing the Gap: CPU and FPGA Trends in Sustainable Floating-Point BLAS Performance

Underwood, K. D., & Hemmert, K. S. (2004) · IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM) · doi:10.1109/fccm.2004.21 · Cited by 150 · see citing works

A survey of algorithmic skeleton frameworks: high‐level structured parallel programming enablers

González–Vélez, H., & Leyton, M. (2010) · Software Practice and Experience · doi:10.1002/spe.1026 · Cited by 149 · see citing works

Is Search Really Necessary to Generate High-Performance BLAS?

Yotov, K., Li, X., Ren, G., Garzarán, M. J., Padua, D., Pingali, K. K., & Stodghill, P. V. (2005) · Proceedings of the IEEE · doi:10.1109/jproc.2004.840444 · Cited by 148 · see citing works

Analytical Modeling Is Enough for High-Performance BLIS

Low, T. M., Igual, F. D., Smith, T., & Quintana–Ort́ı, E. S. (2016) · ACM Transactions on Mathematical Software · doi:10.1145/2925987 · Cited by 148 · see citing works

/spl epsi/-SSVR: a smooth support vector machine for /spl epsi/-insensitive regression

Lee, Y., Hsieh, W.-F., & Huang, C. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.77 · Cited by 145 · see citing works

On the accuracy of finite-difference solutions for nonlinear water waves

Bingham, H. B., & Zhang, H. (2006) · Journal of Engineering Mathematics · doi:10.1007/s10665-006-9108-4 · Cited by 139 · see citing works

The hardware lottery

Hooker, S. (2021) · Communications of the ACM · doi:10.1145/3467017 · Cited by 211 · see citing works

Computing Globally Optimal Solutions for Single-Row Layout Problems Using Semidefinite Programming and Cutting Planes

Anjos, M. F., & Vannelli, A. (2008) · INFORMS journal on computing · doi:10.1287/ijoc.1080.0270 · Cited by 136 · see citing works

Auto-tuning 3-D FFT library for CUDA GPUs

Nukada, A., & Matsuoka, S. (2009) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/1654059.1654090 · Cited by 136 · see citing works

A cross-input adaptive framework for GPU program optimizations

Liu, Y., Zhang, E. Z., & Shen, X. (2009) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2009.5160988 · Cited by 133 · see citing works

Parallel MATLAB: Doing it Right

Choy, R., & Edelman, A. S. (2005) · Proceedings of the IEEE · doi:10.1109/jproc.2004.840490 · Cited by 133 · see citing works

Annotation-based empirical performance tuning using Orio

Hartono, A., Norris, B., & Sadayappan, P. (2009) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2009.5161004 · Cited by 132 · see citing works

Predictive Runtime Code Scheduling for Heterogeneous Architectures

Jiménez, V., Vilanova, L., Gelado, I., Gil, M., Fursin, G., & Navarro, N. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-92990-1_4 · Cited by 132 · see citing works

A framework for adaptive algorithm selection in STAPL

Thomas, N. L., Tanase, G., Tkachyshyn, O., Perdue, J., Amato, N. M., & Rauchwerger, L. (2005) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/1065944.1065981 · Cited by 130 · see citing works

Programming for parallelism and locality with hierarchically tiled arrays

Bikshandi, G., Guo, J., Hoeflinger, D. E., Almási, G., Fraguela, B. B., Garzarán, M. J., Padua, D., & von Praun, C. (2006) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/1122971.1122981 · Cited by 128 · see citing works

Numerical simulation of weakly ionized hypersonic flow over reentry capsules.

Boyd, I. D., & Scalabrin, L. (2007) · Deep Blue (University of Michigan) · Cited by 128 · see citing works

Elastic scaling of data parallel operators in stream processing

Schneider, S., Andrade, H. C. M., Gedik, B., Biem, A., & Wu, K. (2009) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2009.5161036 · Cited by 126 · see citing works

Evaluation of the raw microprocessor: an exposed-wire-delay architecture for ILP and streams

Taylor, M. B., Lee, W., Miller, J. B., Wentzlaff, D., Bratt, I., Greenwald, B., Hoffmann, H., Johnson, P., Kim, J., Psota, J., et al. (2004) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca.2004.1310759 · Cited by 125 · see citing works

Autotuning GEMM Kernels for the Fermi GPU

Jakub Kurzak, A., Tomov, S. Z., & Dongarra, J. J. (2012) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2011.311 · Cited by 123 · see citing works

Fast searches for effective optimization phase sequences

Kulkarni, P. A., Hines, S. R., Hiser, J. D., Whalley, D. B., Davidson, J. W., & Jones, D. L. (2004) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/996841.996863 · Cited by 121 · see citing works

Auto-generation and auto-tuning of 3D stencil codes on GPU clusters

Zhang, Y., & Mueller, F. (2012) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1145/2259016.2259037 · Cited by 119 · see citing works

Taming parallel I/O complexity with auto-tuning

Behzad, B., Luu, H. V. T., Huchette, J., Byna, S., Prabhat, Aydt, R. A., Koziol, Q., & Snir, M. (2013) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/2503210.2503278 · Cited by 119 · see citing works

NPACI Rocks: tools and techniques for easily deploying manageable Linux clusters

Papadopoulos, P. M., Katz, M. J., & Bruno, G. (2003) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.722 · Cited by 118 · see citing works

Untitled work

(2002) · doi:10.5555/762761.762822 · Cited by 115 · see citing works

Combining Models and Guided Empirical Search to Optimize for Multiple Levels of the Memory Hierarchy

Chen, C., Chame, J., & Hall, M. W. (2005) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo.2005.10 · Cited by 114 · see citing works

Automating the Finite Element Method

Logg, A. (2007) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-007-9003-9 · Cited by 113 · see citing works

Loop transformations

Pouchet, L.-N., Bondhugula, U. K. R., Bastoul, C., Cohen, A., Ramanujam, J., Sadayappan, P., & Vasilache, N. (2011) · ACM SIGPLAN Notices · doi:10.1145/1925844.1926449 · Cited by 111 · see citing works

Provably near-optimal solutions for very large single-row facility layout problems

Anjos, M. F., & Yen, G. (2009) · Optimization methods & software · doi:10.1080/10556780902917735 · Cited by 110 · see citing works

POET: Parameterized Optimizations for Empirical Tuning

Yi, Q., Seymour, K., You, H., Vuduc, R. W., & Quinlan, D. (2007) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2007.370637 · Cited by 110 · see citing works

Quantum Monte Carlo on graphical processing units

Anderson, A. G., Goddard, W. A., & Schröder, P. (2007) · Computer Physics Communications · doi:10.1016/j.cpc.2007.03.004 · Cited by 109 · see citing works

The Cornish–Fisher expansion in the context of Delta–Gamma-normal approximations

Jaschke, S. R. (2002) · The Journal of Risk · doi:10.21314/jor.2002.068 · Cited by 107 · see citing works

Applying the roofline model

Ofenbeck, G., Steinmann, R., Cabezas, V. C., Spampinato, D. G., & Püschel, M. (2014) · IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass.2014.6844463 · Cited by 106 · see citing works

PLuTo: A practical and fully automatic polyhedral program optimization system

Bondhugula, U. K. R., & Ramanujam, J. (2008) · Cited by 106 · see citing works

Magnetic Levitation Over Large Translation and Rotation Ranges in All Directions

Berkelman, P. J., & Dzadovsky, M. (2011) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2011.2161614 · Cited by 105 · see citing works

QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators

Agullo, E., Augonnet, C., Dongarra, J. J., Mathieu Faverge, A., Ltaief, H., Thibault, S., & Tomov, S. Z. (2011) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2011.90 · Cited by 103 · see citing works

Error bounds from extra-precise iterative refinement

Demmel, J. W., Hida, Y., Kahan, W., Li, X. S., Mukherjee, S., & Riedy, J. (2006) · ACM Transactions on Mathematical Software · doi:10.1145/1141885.1141894 · Cited by 103 · see citing works

Loop Transformation Recipes for Code Generation and Auto-Tuning

Hall, M. W., Chame, J., Chen, C., Shin, J., Rudy, G., & Khan, M. M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13374-9_4 · Cited by 102 · see citing works

Finding effective optimization phase sequences

Kulkarni, P. A., Zhao, W., Moon, H., Cho, K., Whalley, D. B., Davidson, J. W., Bailey, M. W., Paek, Y., & Gallivan, K. A. (2003) · ACM SIGPLAN Notices · doi:10.1145/780731.780735 · Cited by 101 · see citing works

Mitigating the compiler optimization phase-ordering problem using machine learning

Kulkarni, S. G., & Cavazos, J. (2012) · Conference on Object-Oriented Programming Systems, Languages, and Applications · doi:10.1145/2384616.2384628 · Cited by 101 · see citing works

SPIRAL: Extreme Performance Portability

Franchetti, F., Low, T. M., Popovici, D. T., Veras, R., Spampinato, D. G., Johnson, J. R., Püschel, M., Hoe, J. C., & Moura, J. M. F. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2873289 · Cited by 100 · see citing works

Statistical Models for Empirical Search-Based Performance Tuning

Vuduc, R. W., Demmel, J. W., & Bilmes, J. (2004) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342004041293 · Cited by 100 · see citing works

Nonlinear Optimization

Apuzzo, M. D., Marino, M., Migdalas, A., Pardalos, P. M., & Toraldo, G. (2005) · Statistics, a series of textbooks and monographs · doi:10.1201/9781420028683.ch8 · Cited by 99 · see citing works

Lattice Boltzmann simulation optimization on leading multicore platforms

Williams, S., Carter, J., Oliker, L., Shalf, J., & Yelick, K. (2008) · 2008 IEEE International Symposium on Parallel and Distributed Processing · doi:10.1109/ipdps.2008.4536295 · Cited by 98 · see citing works

Terra

DeVito, Z., Hegarty, J., Aiken, A., Hanrahan, P., & Vítek, J. (2013) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/2491956.2462166 · Cited by 98 · see citing works

Efficient computation of sum-products on GPUs through software-managed cache

Silberstein, M., Schuster, A., Geiger, D., Patney, A., & Owens, J. D. (2008) · International Conference on Supercomputing · doi:10.1145/1375527.1375572 · Cited by 98 · see citing works

Language and compiler support for stream programs

Amarasinghe, S. P., & Thies, W. (2009) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 97 · see citing works

Language virtualization for heterogeneous parallel computing

Chafi, H., DeVito, Z., Moors, A., Rompf, T., Sujeeth, A. K., Hanrahan, P., Odersky, M., & Olukotun, K. (2010) · Conference on Object-Oriented Programming Systems, Languages, and Applications · doi:10.1145/1869459.1869527 · Cited by 96 · see citing works

A divide-and-conquer/cellular-decomposition framework for million-to-billion atom simulations of chemical reactions

Nakano, A., Kalia, R. K., Nomura, K.-I., Sharma, A. K., Vashishta, P. D., Shimojo, F., van Duin, A. C. T., Goddard, W. A., Biswas, R., & Srivastava, D. (2006) · Computational Materials Science · doi:10.1016/j.commatsci.2006.04.012 · Cited by 96 · see citing works

Using GPUs to improve multigrid solver performance on a cluster

Dominik Göddeke, A., Strzodka, R., Mohd-Yusof, J., McCormick, P. S., Wobker, H., Becker, C., & Turek, S. (2008) · International Journal of Computational Science and Engineering · doi:10.1504/ijcse.2008.021111 · Cited by 95 · see citing works

Minimizing execution time in MPI programs on an energy-constrained, power-scalable cluster

Springer, R., Lowenthal, D. K., Rountree, B., & Freeh, V. W. (2006) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/1122971.1123006 · Cited by 93 · see citing works

Automated Empirical Optimization of Software and the ATLAS Project

Whaley, R. C., Petitet, A. P., & Dongarra, J. J. (2000)

ATLAS Version 3.9: Overview and Status

Whaley, R. C. (2010) · Springer eBooks · doi:10.1007/978-1-4419-6935-4_2 · Cited by 1 · see citing works

ATLAS (Automatically Tuned Linear Algebra Software)

Karmani, R. K., Agha, G., Squillante, M. S., Seiferas, J., Brezina, M., Hu, J., Tuminaro, R. S., Sanders, P., Träffe, J. L., Geijn, R. A., et al. (2011) · Springer eBooks · doi:10.1007/978-0-387-09766-4_85 · Cited by 24 · see citing works

Automated Empirical Optimizations of Software and the ATLAS Project (LAPACK Working Note 147)

Whaley, C., Petitet, A. P., & Dongarra, J. J. (2000) · Cited by 107 · see citing works

An Effective Empirical Search Method for Automatic Software Tuning

You, H., Seymour, K., & Dongarra, J. J. (2005) · Cited by 11 · see citing works