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Basic Linear Algebra Subprograms for Fortran Usage

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High-Performance Compilers for Parallel Computing

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An extended set of FORTRAN basic linear algebra subprograms

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Automatically Tuned Linear Algebra Software

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Optimizing supercompilers for supercomputers

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Automatically Tuned Linear Algebra Software

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Optimizing matrix multiply using PHiPAC

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Parallel Gaussian elimination for Gröbner bases computations in finite fields

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X-ray: a tool for automatic measurement of hardware parameters

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Energy-Efficient Computational Chemistry: Comparison of x86 and ARM Systems

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Apollo: Reusable Models for Fast, Dynamic Tuning of Input-Dependent Code

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High-Performance Generalized Tensor Operations

Gareev, R. A., Grosser, T., & Kruse, M. (2018) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/3235029 · Cited by 28

Design Tradeoffs for BLAS Operations on Reconfigurable Hardware

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A Case Study Using Automatic Performance Tuning for Large-Scale Scientific Programs

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Automatic Performance Tuning and Analysis of Sparse Triangular Solve

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A Cache-Oblivious Sparse Matrix–Vector Multiplication Scheme Based on the Hilbert Curve

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Emmerald: a fast matrix–matrix multiply using Intel's SSE instructions

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An integer programming framework for optimizing shared memory use on GPUs

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Servet: A benchmark suite for autotuning on multicore clusters

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Autotuning Sparse Matrix-Vector Multiplication for Multicore

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Sensitivity Generation in an Adaptive BDF-Method

Albersmeyer, J., & Bock, H. G. (2008) · Springer eBooks · doi:10.1007/978-3-540-79409-7_2 · Cited by 27

Input-aware auto-tuning for directive-based GPU programming

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Parallel schedule synthesis for attribute grammars

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Parallel Simulation of Massively Coupled Interconnect Networks

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Gaussian Elimination: A case study in efficient genericity with MetaOCaml

Carette, J. (2006) · Science of Computer Programming · doi:10.1016/j.scico.2005.10.012 · Cited by 27

Efficient and portable acceleration of quantum chemical many-body methods in mixed floating point precision using OpenACC compiler directives

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How to Write Fast Numerical Code: A Small Introduction

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A sparse algorithm for the evaluation of the local energy in quantum Monte Carlo

Aspuru‐Guzik, A., Salomón–Ferrer, R., Austin, B. M., & Lester, W. A. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20205 · Cited by 27

Fast Automatic Generation of DSP Algorithms

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ABCLib_DRSSED: A parallel eigensolver with an auto-tuning facility

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Breslow Intermediates (Amino Enols) and Their Keto Tautomers: First Gas‐Phase Characterization by IR Ion Spectroscopy

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Simulation and Flight Test of Autonomous Aircraft Estimation, Planning, and Control Algorithms

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Algorithmic Parameter Optimization of the DFO Method with the OPAL Framework

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Zori 1.0: A parallel quantum Monte Carlo electronic structure package

Aspuru‐Guzik, A., Salomón–Ferrer, R., Austin, B. M., Perusquía–Flores, R., Griffin, M. A., Oliva, R. A., Skinner, D. E., Domin, D., & Lester, W. A. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20215 · Cited by 26

Self-aware Computing Systems: Related Concepts and Research Areas

Cámara, J., Bellman, K. L., Kephart, J. O., Autili, M., Bencomo, N., Diaconescu, A., Giese, H., Götz, S., Inverardi, P., Kounev, S., et al. (2017) · Springer eBooks · doi:10.1007/978-3-319-47474-8_2 · Cited by 25

Force2Vec: Parallel Force-Directed Graph Embedding

Rahman, M. K., Sujon, M. H., & Azad, A. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00053 · Cited by 25

Teaching parallel computing to science faculty

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A high-performance matrix–matrix multiplication methodology for CPU and GPU architectures

Kelefouras, V. I., Kritikakou, A. S., Mporas, I., & Kolonias, V. (2016) · The Journal of Supercomputing · doi:10.1007/s11227-015-1613-7 · Cited by 26

MPI Performance Analysis Tools on Blue Gene/L

Chung, I., Walkup, R. E., Wen, H.-F., & Yu, H. (2006) · ACM/IEEE Conference on Supercomputing (SC) · doi:10.1109/sc.2006.43 · Cited by 25

Algebraic analysis of high-pass quantization

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Predicting memory-access cost based on data-access patterns

Byna, S., Sun, X., Gropp, W., & Thakur, R. (2005) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/clustr.2004.1392630 · Cited by 25

Linear analysis and optimization of stream programs

Lamb, A. A., Thies, W., & Amarasinghe, S. P. (2003) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/781131.781134 · Cited by 25

Solvent-dependent modulation of metal–metal electronic interactions in a dinuclear cyanoruthenate complex: a detailed electrochemical, spectroscopic and computational study

Wragg, A. B., Derossi, S., Easun, T. L., George, M. W., Sun, X., Hartl, F., Shelton, A. H., Meijer, A. J. H. M., & Ward, M. D. (2012) · Dalton Transactions · doi:10.1039/c2dt31001e · Cited by 24

Automatic Tuning of Discrete Fourier Transforms Driven by Analytical Modeling

Fraguela, B. B., Voronenko, Y., & Püschel, M. (2009) · International Conference on Parallel Architectures and Compilation Techniques (PACT) · doi:10.1109/pact.2009.11 · Cited by 24

Perflint: A Context Sensitive Performance Advisor for C++ Programs

Liu, L., & Rus, S. (2009) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo.2009.36 · Cited by 24

Iterative Algorithms to Approximate Canonical Gabor Windows: Computational Aspects

Janssen, A. J. E. M., & Søndergaard, P. L. (2007) · Journal of Fourier Analysis and Applications · doi:10.1007/s00041-006-6069-y · Cited by 24

Automatic Vectorization of Interleaved Data Revisited

Anderson, A., Malik, A., & Gregg, D. (2015) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/2838735 · Cited by 24

Anatomy of a globally recursive embedded LINPACK benchmark

Dongarra, J. J., & Łuszczek, P. (2012) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec.2012.6408679 · Cited by 24

Matrix product on heterogeneous master-worker platforms

Dongarra, J. J., Pineau, J., Robert, Y. L., & Vivien, F. (2008) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/1345206.1345217 · Cited by 24

Insights into homonuclear decoupling from efficient numerical simulation: Techniques and examples

Zorin, V., Ernst, M., Brown, S. P., & Hodgkinson, P. (2008) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2008.02.012 · Cited by 24

Vectorization techniques for the Blue Gene/L double FPU

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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

Gas-phase complexes of Ni2+ and Ca2+ with deprotonated histidylhistidine (HisHis): A model case for polyhistidyl-metal binding motifs

Peckelsen, K., Martens, J. K., Berden, G., Oomens, J., Dunbar, R. C., Meijer, A. J. H. M., & Schäfer, M. (2016) · Journal of Molecular Spectroscopy · doi:10.1016/j.jms.2016.10.008 · Cited by 23

Dynamic selection of tile sizes

Tavarageri, S., Pouchet, L.-N., Ramanujam, J., Rountev, A., & Sadayappan, P. (2011) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.1109/hipc.2011.6152742 · Cited by 23

Reducing Floating Point Error in Dot Product Using the Superblock Family of Algorithms

Castaldo, A. M., Whaley, R. C., & Chronopoulos, A. T. (2008) · SIAM Journal on Scientific Computing · doi:10.1137/070679946 · Cited by 23

Paraiso: an automated tuning framework for explicit solvers of partial differential equations

Muranushi, T. (2012) · Computational Science & Discovery · doi:10.1088/1749-4699/5/1/015003 · Cited by 23

An Adaptive Algorithm Selection Framework

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Programming for Locality and Parallelism with Hierarchically Tiled Arrays

Almási, G., Rose, L. A. D., Fraguela, B. B., Moreira, J., & Padua, D. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24644-2_11 · Cited by 23

Finite state machine-based optimization of data parallel regular domain problems applied in low-level image processing

Seinstra, F. J., Koelma, D. C., & Bagdanov, A. D. (2004) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2004.55 · Cited by 23

Partial Evaluation of MATLAB

Elphick, D., Leuschel, M., & Cox, S. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39815-8_21 · Cited by 23

Avoiding Communication in Dense Linear Algebra

Ballard, G. (2013) · eScholarship (California Digital Library) · Cited by 23

Is Cache-Oblivious DGEMM Viable?

Gunnels, J. A., Gustavson, F. G., Pingali, K. K., & Yotov, K. (2007) · Springer eBooks · doi:10.1007/978-3-540-75755-9_109 · Cited by 22

A step towards transparent integration of input-consciousness into dynamic program optimizations

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Building the functional performance model of a processor

Lastovetsky, A., Manumachu, R. R., & Higgins, R. (2006) · ACM Symposium on Applied Computing · doi:10.1145/1141277.1141450 · Cited by 22

Combining analytical and empirical approaches in tuning matrix transposition

Lu, Q., Krishnamoorthy, S., & Sadayappan, P. (2006) · International Conference on Parallel Architectures and Compilation Techniques (PACT) · doi:10.1145/1152154.1152190 · Cited by 22

A Matrix–Matrix Multiplication methodology for single/multi-core architectures using SIMD

Kelefouras, V. I., Kritikakou, A. S., & Goutis, C. E. (2014) · The Journal of Supercomputing · doi:10.1007/s11227-014-1098-9 · Cited by 22

Adaptive Off-Line Tuning for Optimized Composition of Components for Heterogeneous Many-Core Systems

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Tessellating stencils

Yuan, L., Zhang, Y., Guo, P. W., & Huang, S. (2017) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3126908.3126920 · Cited by 22

Ultrafast photoinduced charge transport in Pt( ii ) donor–acceptor assembly bearing naphthalimide electron acceptor and phenothiazine electron donor

Sazanovich, I. V., Best, J., Scattergood, P. A., Towrie, M., Тихомиров, С. А., Bouganov, O. V., Meijer, A. J. H. M., & Weinstein, J. A. (2014) · Physical Chemistry Chemical Physics · doi:10.1039/c4cp03995e · Cited by 22

A dynamically tuned sorting library

Li, X., Garzarán, M. J., & Padua, D. (2004) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo.2004.1281668 · Cited by 22

Optimized Stencil Computation Using In-Place Calculation on Modern Multicore Systems

Augustin, W., Heuveline, V., & Weiß, J.-P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03869-3_72 · Cited by 22

Programming Abstractions and Optimization Techniques for GPU-based Heterogeneous Systems

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CUDA Memory Optimizations for Large Data-Structures in the Gravit Simulator

Siegel, J., Ributzka, J., & Li, X. (2009) · International Conference on Parallel Processing Workshops (ICPPW) · doi:10.1109/icppw.2009.78 · Cited by 21

Optimizing parallel GEMM routines using auto-tuning with Intel AVX-512

Kim, R., Choi, J., & Lee, M. (2019) · International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia) · doi:10.1145/3293320.3293334 · Cited by 21

Synthesis of chiral β-amino acid derivatives by asymmetric hydrosilylation with an imidazole derived organocatalyst

Jones, S., & Li, X. (2012) · Tetrahedron · doi:10.1016/j.tet.2012.04.084 · Cited by 21

Active Learning-based Automatic Tuning and Prediction of Parallel I/O Performance

Agarwal, M., Singhvi, D., Malakar, P., & Byna, S. (2019) · IEEE/ACM International Parallel Data Systems Workshop (PDSW) · doi:10.1109/pdsw49588.2019.00007 · Cited by 21

Automated programmable control and parameterization of compiler optimizations

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Minimal Data Copy for Dense Linear Algebra Factorization

Gustavson, F. G., Gunnels, J. A., & Sexton, J. C. (2007) · Springer eBooks · doi:10.1007/978-3-540-75755-9_66 · Cited by 21

An Empirically Optimized Radix Sort for GPU

Huang, B., Gao, J., & Li, X. (2009) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa.2009.89 · Cited by 21

Library generation for linear transforms

Voronenko, Y. (2008) · Cited by 21

A Novel KLT Algorithm Optimized for Small Signal Sets

Gianfelici, F., Biagetti, G., Crippa, P., & Turchetti, C. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2005.1415136 · Cited by 21

Block algorithms for reordering standard and generalized Schur forms

Kreßner, D. (2006) · ACM Transactions on Mathematical Software · doi:10.1145/1186785.1186787 · Cited by 21

Implementation of the Mixed-Precision High Performance LINPACK Benchmark on the CELL Processor

Jakub Kurzak, A., & Dongarra, J. J. (2006) · Cited by 21

Self-tuning resource aware specialisation for prolog

Craig, S.-J., & Leuschel, M. (2005) · International Symposium on Principles and Practice of Declarative Programming (PPDP) · doi:10.1145/1069774.1069777 · Cited by 21

A Survey of Methods for Collective Communication Optimization and TuningPreprint

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Architecture-Adaptive Code Variant Tuning

Muralidharan, S., Roy, A., Hall, M. W., Garland, M., & Rai, P. (2016) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/2872362.2872411 · Cited by 20

Autotuning of MPI Applications Using PTF

Sikora, A., César, E., Ureña, I. A. C., & Gerndt, M. (2016) · doi:10.1145/2916026.2916028 · Cited by 20

clMAGMA

Cao, C., Dongarra, J. J., Du, P., Gates, M., Łuszczek, P., & Tomov, S. Z. (2014) · doi:10.1145/2664666.2664667 · Cited by 20

Implementing a Code Generator for Fast Matrix Multiplication in OpenCL on the GPU

Matsumoto, K., Nakasato, N., & Sedukhin, S. G. (2012) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc.2012.30 · Cited by 20

Productive high performance parallel programming with auto-tuned domain-specific embedded languages

Yelick, K., Fox, A., & Kamil, S. A. (2012) · Cited by 20

Vyasa: A High-Performance Vectorizing Compiler for Tensor Convolutions on the Xilinx AI Engine

Chatarasi, P., Neuendorffer, S., Bayliss, S., Vissers, K., & Sarkar, V. (2020) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec43674.2020.9286183 · Cited by 20

An Implementation of the Tile QR Factorization for a GPU and Multiple CPUs

Jakub Kurzak, A., Nath, R., Du, P., & Dongarra, J. J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28145-7_25 · Cited by 20

Two-stage least squares and indirect least squares algorithms for simultaneous equations models

López-Espín, J. J., Vidal, A. M., & Domingo Giménez, C. (2011) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2011.07.005 · Cited by 20

Automatic optimisation of parallel linear algebra routines in systems with variable load

Cuenca, J., Domingo Giménez, C., Gonzalez, J., Dongarra, J. J., & Roche, K. J. (2003) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/empdp.2003.1183618 · Cited by 20

Bell inequalities stronger than the Clauser-Horne-Shimony-Holt inequality for three-level isotropic states

Ito, T., Imai, H., & Avis, D. (2006) · Physical Review A · doi:10.1103/physreva.73.042109 · Cited by 20

92 ¢ /MFlops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster

Aberdeen, D., Baxter, J., & Edwards, R. (2000) · ACM/IEEE Conference on Supercomputing (SC) · doi:10.1109/sc.2000.10031 · Cited by 20

Ergothioneine and related histidine derivatives in the gas phase: tautomer structures determined by IRMPD spectroscopy and theory

Peckelsen, K., Martens, J. K., Czympiel, L., Oomens, J., Berden, G., Gründemann, D., Meijer, A. J. H. M., & Schäfer, M. (2017) · Physical Chemistry Chemical Physics · doi:10.1039/c7cp03843g · Cited by 19

Parallel Tiled QR Factorization for Multicore Architectures

Buttari, A., Julien Langou, F., Jakub Kurzak, A., & Dongarra, J. J. (2008) · Springer eBooks · doi:10.1007/978-3-540-68111-3_67 · Cited by 19

Effect of auto-tuning with user's knowledge for numerical software

Katagiri, T., Kise, K., Honda, H., & Yuba, T. (2004) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/977091.977095 · Cited by 19

Bringing Parallel Performance to Python with Domain-Specific Selective Embedded Just-in-Time Specialization

Kamil, S. A., Coetzee, D., & Fox, A. (2011) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-ebaa42b7-00f · Cited by 19

Decomposition and parallelization of strongly coupled fluid–structure interaction linear subsystems based on the Q1/P0 discretization

Toma, M., Oshima, M., & Takagi, S. (2016) · Computers & Structures · doi:10.1016/j.compstruc.2016.06.001 · Cited by 19

Prospectus for the Next LAPACK and ScaLAPACK Libraries

Demmel, J. W., Dongarra, J. J., Parlett, B. Ν., Kahan, W., Gu, M., Bindel, D., Hida, Y., Li, X., Marques, O., Riedy, J., et al. (2007) · Springer eBooks · doi:10.1007/978-3-540-75755-9_2 · Cited by 19

Incremental and Decremental Proximal Support Vector Classification using Decay Coefficients

Tveit, A., Hetland, M. L., & Engum, H. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45228-7_42 · Cited by 19

Energy-Efficient Channel Estimation in MIMO Systems

Yatawatta, S., Petropulu, A. P., & Graff, C. (2006) · EURASIP Journal on Wireless Communications and Networking · doi:10.1155/wcn/2006/27694 · Cited by 19

Analytic Models and Empirical Search: A Hybrid Approach to Code Optimization

Epshteyn, A., Garzarán, M. J., DEJONG, G. F., Padua, D., Ren, G., Li, X., Yotov, K., & Pingali, K. K. (2006) · Lecture notes in computer science · doi:10.1007/978-3-540-69330-7_18 · Cited by 19

A Computational Framework for Handling Motion.

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