Automated empirical optimizations of software and the ATLAS project

LAPACK Users' Guide

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A set of level 3 basic linear algebra subprograms

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FFTW: an adaptive software architecture for the FFT

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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

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

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

Learning from the Success of MPI

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A framework for high‐performance matrix multiplication based on hierarchical abstractions, algorithms and optimized low‐level kernels

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Self‐Assembled, Kinetically Locked, RuII‐Based Metallomacrocycles: Physical, Structural, and Modeling Studies

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A High Performance and Memory Efficient LU Decomposer on FPGAs

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A parallel Fortran framework for neural networks and deep learning

Curcic, M. (2019) · ACM SIGPLAN Fortran Forum · doi:10.1145/3323057.3323059 · Cited by 48

Computer Generation of General Size Linear Transform Libraries

Voronenko, Y., de Mesmay, F., & Püschel, M. (2009) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo.2009.33 · Cited by 43

Enhancing locality for recursive traversals of recursive structures

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Representation-transparent matrix algorithms with scalable performance

Gottschling, P., Wise, D. S., & Adams, M. D. (2007) · International Conference on Supercomputing · doi:10.1145/1274971.1274989 · Cited by 42

Computer Architecture: Challenges and Opportunities for the Next Decade

Agerwala, T., & Chatterjee, S. (2005) · IEEE Micro · doi:10.1109/mm.2005.45 · Cited by 42

High-performance linear algebra algorithms using new generalized data structures for matrices

Gustavson, F. G. (2003) · IBM Journal of Research and Development · doi:10.1147/rd.471.0031 · Cited by 42

Using SIMD registers and instructions to enable instruction-level parallelism in sorting algorithms

Furtak, T., Amaral, J. N., & Niewiadomski, R. (2007) · ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) · doi:10.1145/1248377.1248436 · Cited by 42

Fractional Steps methods for transient problems on commodity computer architectures

Krotkiewski, M., Dąbrowski, M., & Podladchikov, Y. (2008) · Physics of The Earth and Planetary Interiors · doi:10.1016/j.pepi.2008.08.008 · Cited by 41

Performance Portability in the Physical Parameterizations of the Community Atmospheric Model

Worley, P. H., & Drake, J. B. (2005) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342005056095 · Cited by 41

Measurement-based modeling of the cache replacement policy

Abel, A., & Reineke, J. (2013) · IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) · doi:10.1109/rtas.2013.6531080 · Cited by 40

The impact of paravirtualized memory hierarchy on linear algebra computational kernels and software

Youseff, L., Seymour, K., You, H., Dongarra, J. J., & Wolski, R. (2008) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/1383422.1383440 · Cited by 40

Effective automatic parallelization and locality optimization using the polyhedral model

Sadayappan, P., & Bondhugula, U. K. R. (2008) · Cited by 40

Numerical algorithms for high-performance computational science

Dongarra, J. J., Grigori, L., & Higham, N. J. (2020) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2019.0066 · Cited by 40

Dinitrogen Release from Arylpentazole: A Picosecond Time-Resolved Infrared, Spectroelectrochemical, and DFT Computational Study

Portius, P., Davis, M. F., Campbell, R., Hartl, F., Zeng, Q., Meijer, A. J. H. M., & Towrie, M. (2013) · The Journal of Physical Chemistry A · doi:10.1021/jp4077454 · Cited by 40

Auto-Tuning the 27-point Stencil for Multicore

Williams, S., Datta, A. K., Volkov, Carter, J. T., Oliker, L., Shalf, J. M., & Yelick, K. (2009) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 39

A Case for Richer Cross-Layer Abstractions: Bridging the Semantic Gap with Expressive Memory

Vijaykumar, N., Jain, A., Majumdar, D., Hsieh, K., Pekhimenko, G. G., Ebrahimi, E., Hajinazar, N., Gibbons, P. B., & Mutlu, O. (2018) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca.2018.00027 · Cited by 39

The survey on ARM processors for HPC

Yokoyama, D., Schulze, B. R., Borges, F., & Evoy, G. M. (2019) · The Journal of Supercomputing · doi:10.1007/s11227-019-02911-9 · Cited by 39

Mitigating the compiler optimization phase-ordering problem using machine learning

Kulkarni, S. G., & Cavazos, J. (2012) · ACM SIGPLAN Notices · doi:10.1145/2398857.2384628 · Cited by 39

Tuning High Performance Kernels through Empirical Compilation

Whaley, R., & Whalley, D. B. (2005) · Proceedings of the International Conference on Parallel Processing · doi:10.1109/icpp.2005.77 · Cited by 39

Measurement‐Based Interactive Simulation of Viscoelastic Solids

Schoner, J. L., Lang, J., & Seidel, H. (2004) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2004.00786.x · Cited by 39

Linear analysis and optimization of stream programs

Lamb, A. A., Thies, W., & Amarasinghe, S. P. (2003) · ACM SIGPLAN Notices · doi:10.1145/780822.781134 · Cited by 39

Optimized composition of performance‐aware parallel components

Keßler, C. W., & Löwe, W. (2011) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.1844 · Cited by 38

Operational retrieval algorithms for JEM/SMILES level 2 data processing system

Takahashi, C., Ochiai, S., & Suzuki, M. (2009) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2009.06.005 · Cited by 38

Radial basis functions for inter-grid interpolation and mesh motion in FSI problems

Lombardi, M., Parolini, N., & Quarteroni, A. M. (2013) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2012.12.019 · Cited by 38

Extracting ultra-scale Lattice Boltzmann performance via hierarchical and distributed auto-tuning

Williams, S., Oliker, L., Carter, J. T., & Shalf, J. M. (2011) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/2063384.2063458 · Cited by 38

ExaSlang: A Domain-Specific Language for Highly Scalable Multigrid Solvers

Schmitt, C., Kuckuk, S., Hannig, F., Köstler, H., & Teich, J. (2014) · International Workshop on Domain-Specific Languages and High-Level Frameworks for High Performance Computing (WOLFHPC) · doi:10.1109/wolfhpc.2014.11 · Cited by 38

Optimization techniques for small matrix multiplication

Drevet, C. É., Islam, M. N., & Schost, É. (2010) · Theoretical Computer Science · doi:10.1016/j.tcs.2010.12.012 · Cited by 43

Simulation of Fusion Plasmas: Current Status and Future Direction

Batchelor, D. A., Beck, M., Bécoulet, A., Budny, R. V., Chang, C. S., Diamond, P. H., Dong, J., Fu, G. Y., Fukuyama, A., Hahm, T. S., et al. (2007) · Plasma Science and Technology · doi:10.1088/1009-0630/9/3/13 · Cited by 38

Programming with tiles

Guo, J., Bikshandi, G., Fraguela, B. B., Garzarán, M. J., & Padua, D. (2008) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/1345206.1345225 · Cited by 39

Parallelized Bayesian inversion for three-dimensional dental X-ray imaging

Kolehmainen, V., Vanne, A., Siltanen, S., Järvenpää, S., Kaipio, J. P., Lassas, M. J., & Kalke, M. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.862662 · Cited by 38

Self-adapting numerical software (SANS) effort

Dongarra, J. J., George Bosilca, A., Chen, Z., Eijkhout, V. L., Fagg, G. E., Fuentes, E., Julien Langou, F., Łuszczek, P., Pjesivac-Grbovic, J., Seymour, K., et al. (2006) · IBM Journal of Research and Development · doi:10.1147/rd.502.0223 · Cited by 38

Performance Optimization and Modeling of Blocked Sparse Kernels

Buttari, A., Eijkhout, V. L., Julien Langou, F., & Filippone, S. (2007) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342007083801 · Cited by 38

Fault tolerant matrix-matrix multiplication

Wu, P., Ding, C., Chen, L., Gao, F. Q., Davies, T., Karlsson, C., & Chen, Z. (2011) · doi:10.1145/2133173.2133185 · Cited by 37

Statistical Models for Automatic Performance Tuning

Vuduc, R. W., Demmel, J. W., & Bilmes, J. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45545-0_21 · Cited by 37

Two-dimensional cache-oblivious sparse matrix–vector multiplication

Yzelman, A.-J. N., & Bisseling, R. H. (2011) · Parallel Computing · doi:10.1016/j.parco.2011.08.004 · Cited by 37

On the decidability of phase ordering problem in optimizing compilation

Touati, S. A. A., & Barthou, D. (2006) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/1128022.1128042 · Cited by 37

Nonlinear optimization and parallel computing

Migdalas, A., Toraldo, G., & Kumar, V. (2003) · Parallel Computing · doi:10.1016/s0167-8191(03)00013-9 · Cited by 37

Optimizing locality and scalability of embedded Runge–Kutta solvers using block-based pipelining

Korch, M., & Rauber, T. (2005) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2005.09.003 · Cited by 37

Parallel implementation of the accelerated BEM approach for EMSI of the human brain

Ataseven, Y., Akalin-Acar, Z., Acar, C. E., & Gençer, N. G. (2008) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-008-0316-0 · Cited by 36

Architecture of an automatically tuned linear algebra library

Cuenca, J., Domingo Giménez, C., & González, J. (2004) · Parallel Computing · doi:10.1016/j.parco.2003.11.002 · Cited by 36

With Extreme Computing, the Rules Have Changed

Dongarra, J. J., Tomov, S. Z., Łuszczek, P., Jakub Kurzak, A., Gates, M., Yamazaki, I., Anzt, H., Haidar, A., & Abdelfattah, A. (2017) · Computing in Science & Engineering · doi:10.1109/mcse.2017.48 · Cited by 36

A Characterization of the Raman Modes in a J-Aggregate-Forming Dye: A Comparison between Theory and Experiment

Coles, D. M., Meijer, A. J. H. M., Tsoi, W. C., Charlton, M. D. B., Kim, J., & Lidzey, D. G. (2010) · The Journal of Physical Chemistry A · doi:10.1021/jp107646p · Cited by 36

Iowa radon leukaemia study: a hierarchical population risk model for spatially correlated exposure measured with error

Smith, B. J., Zhang, L., & Field, R. W. (2007) · Statistics in Medicine · doi:10.1002/sim.2884 · Cited by 36

LU Factorization with Partial Pivoting for a Multicore System with Accelerators

Jakub Kurzak, A., Łuszczek, P., Mathieu Faverge, A., & Dongarra, J. J. (2012) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2012.242 · Cited by 36

FFT Program Generation for Shared Memory: SMP and Multicore

Franchetti, F., Voronenko, Y., & Püschel, M. (2006) · ACM/IEEE Conference on Supercomputing (SC) · doi:10.1109/sc.2006.31 · Cited by 36

Tile size selection revisited

Mehta, S., Beeraka, G., & Yew, P.-C. (2013) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/2541228.2555292 · Cited by 39

Scaling LAPACK panel operations using parallel cache assignment

Castaldo, A. M., & Whaley, R. C. (2010) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/1693453.1693484 · Cited by 35

From Intercalation to Groove Binding: Switching the DNA‐Binding Mode of Isostructural Transition‐Metal Complexes

Ahmad, H., Wragg, A. B., Cullen, W., Wombwell, C., Meijer, A. J. H. M., & Thomas, J. A. (2014) · Chemistry - A European Journal · doi:10.1002/chem.201304053 · Cited by 35

ABCLibScript: a directive to support specification of an auto-tuning facility for numerical software

Katagiri, T., Kise, K., Honda, H., & Yuba, T. (2005) · Parallel Computing · doi:10.1016/j.parco.2005.09.005 · Cited by 35

Granger Causality Network Reconstruction of Conductance-Based Integrate-and-Fire Neuronal Systems

Zhou, D., Xiao, Y., Zhang, Y., Xu, Z. J., & Cai, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087636 · Cited by 37

Performance without pain = productivity

Nishtala, R., Almasi, G. S., & Caşcaval, C. (2008) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/1345206.1345224 · Cited by 35

Think globally, search locally

Yotov, K., Pingali, K. K., & Stodghill, P. V. (2005) · International Conference on Supercomputing · doi:10.1145/1088149.1088168 · Cited by 35

Automated empirical tuning of scientific codes for performance and power consumption

Rahman, S. M. F., Guo, J., & Yi, Q. (2011) · High Performance Embedded Architectures and Compilers · doi:10.1145/1944862.1944880 · Cited by 34

Kernel Eigenspace-Based MLLR Adaptation

Mak, B. K.-W., & Hsiao, R. W.-H. (2007) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2006.885941 · Cited by 34

An object‐oriented bulk synchronous parallel library for multicore programming

Yzelman, A.-J. N., & Bisseling, R. H. (2011) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.1843 · Cited by 34

Multi-stage Programming with Functors and Monads: Eliminating Abstraction Overhead from Generic Code

Carette, J., & Kiselyov, O. (2005) · Lecture notes in computer science · doi:10.1007/11561347_18 · Cited by 34

An Adaptive Algorithm Selection Framework for Reduction Parallelization

Yu, H., & Rauchwerger, L. (2006) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2006.131 · Cited by 34

Hydrogen Tunneling above Room Temperature Evidenced by Infrared Ion Spectroscopy

Schäfer, M., Peckelsen, K., Paul, M., Martens, J. K., Oomens, J., Berden, G., Berkessel, A., & Meijer, A. J. H. M. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.6b10348 · Cited by 34

The quest for petascale computing

Dongarra, J. J., & WALKER, D. W. (2001) · Computing in Science & Engineering · doi:10.1109/5992.919263 · Cited by 34

MPI tools and performance studies---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:10.1145/1188455.1188583 · Cited by 34

Design principles for end-to-end multicore schedulers

Peter, S., Schüpbach, A., Barham, P., Baumann, A., Isaacs, R., Harris, T., & Roscoe, T. (2010) · Cited by 33

MATE-CG: A Map Reduce-Like Framework for Accelerating Data-Intensive Computations on Heterogeneous Clusters

Jiang, W., & Agrawal, G. (2012) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2012.65 · Cited by 33

Multi-stage programming with functors and monads: Eliminating abstraction overhead from generic code

Carette, J., & Kiselyov, O. (2008) · Science of Computer Programming · doi:10.1016/j.scico.2008.09.008 · Cited by 33

Accurate Cache and TLB Characterization Using Hardware Counters

Dongarra, J. J., Moore, S., Mucci, P. J., Seymour, K., & You, H. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24688-6_57 · Cited by 33

Online performance auditing

Lau, J., Arnold, M., Hind, M., & Calder, B. (2006) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/1133981.1134010 · Cited by 33

Pattern-driven parallel I/O tuning

Behzad, B., Byna, S., Prabhat, & Snir, M. (2015) · IEEE/ACM International Parallel Data Systems Workshop (PDSW) · doi:10.1145/2834976.2834977 · Cited by 32

Optimizing stream programs using linear state space analysis

Agrawal, S., Thies, W., & Amarasinghe, S. P. (2005) · International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES) · doi:10.1145/1086297.1086315 · Cited by 32

Untitled work

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Parallel matrix multiplication based on space-filling curves on shared memory multicore platforms

Heinecke, A., & Bader, M. (2008) · doi:10.1145/1366219.1366223 · Cited by 31

Autogeneration and Autotuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Zhang, Y., & Mueller, F. (2012) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2012.160 · Cited by 31

A dynamic scheduling framework for emerging heterogeneous systems

Ravi, V. T., & Agrawal, G. (2011) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.1109/hipc.2011.6152724 · Cited by 31

A Novel Parallel QR Algorithm for Hybrid Distributed Memory HPC Systems

Granat, R. A., Kågström, B., & Kreßner, D. (2010) · SIAM Journal on Scientific Computing · doi:10.1137/090756934 · Cited by 31

An implementation of matrix–matrix multiplication on the Intel KNL processor with AVX-512

Lim, R., Lee, Y., Kim, R., & Choi, J. (2018) · Cluster Computing · doi:10.1007/s10586-018-2810-y · Cited by 31

Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication

Moon, G. E., Kwon, H., Jeong, G., Chatarasi, P., Rajamanickam, S., & Krishna, T. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3104240 · Cited by 31

Autoscheduling for sparse tensor algebra with an asymptotic cost model

Ahrens, W., Kjølstad, F., & Amarasinghe, S. P. (2022) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/3519939.3523442 · Cited by 31

Autotuning algorithmic choice for input sensitivity

Ding, Y., Ansel, J., Veeramachaneni, K. K., Shen, X., O’Reilly, U.-M., & Amarasinghe, S. P. (2015) · ACM SIGPLAN Notices · doi:10.1145/2813885.2737969 · Cited by 31

Unified Geostatistical Modeling for Data Fusion and Spatial Heteroskedasticity withRPackageramps

Smith, B. J., Yan, J., & Cowles, M. K. (2008) · Journal of Statistical Software · doi:10.18637/jss.v025.i10 · Cited by 31

Hardware-Oriented Implementation of Cache Oblivious Matrix Operations Based on Space-Filling Curves

Bader, M., Franz, R., Günther, S., & Heinecke, A. (2008) · Springer eBooks · doi:10.1007/978-3-540-68111-3_66 · Cited by 30

Floating-point arithmetic

Boldo, S., Jeannerod, C.-P., Melquiond, G., & Muller, J. (2023) · Acta Numerica · doi:10.1017/s0962492922000101 · Cited by 30

ExaSlang: a domain-specific language for highly scalable multigrid solvers

Schmitt, C., Kuckuk, S., Hannig, F., Köstler, H., & Teich, J. (2014) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.5555/2691166.2691171 · Cited by 30

Performance-aware component composition for GPU-based systems

Dastgeer, U. (2014) · Linköping University Electronic Press eBooks · doi:10.3384/diss.diva-104310 · Cited by 30

Performance Optimization of Tensor Contraction Expressions for Many-Body Methods in Quantum Chemistry

Hartono, A., Lu, Q., Henretty, T. S., Krishnamoorthy, S., Zhang, H., Baumgartner, G., Bernholdt, D. E., Nooijen, M., Pitzer, R. M., Ramanujam, J., et al. (2009) · The Journal of Physical Chemistry A · doi:10.1021/jp9051215 · Cited by 30

The cache-oblivious gaussian elimination paradigm

Chowdhury, R., & Ramachandran, V. (2007) · ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) · doi:10.1145/1248377.1248392 · Cited by 30

GOTHIC: Gravitational oct-tree code accelerated by hierarchical time step controlling

Miki, Y., & Umemura, M. (2016) · New Astronomy · doi:10.1016/j.newast.2016.10.007 · Cited by 30

Run-Time Automatic Performance Tuning for Multicore Applications

Karcher, T., & Pankratius, V. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23400-2_2 · Cited by 30

Parallel Processing and Applied Mathematics

MV, A., D’Ambrosio, D., V, L., Rongo, R., & S., S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14403-5 · Cited by 30

A re-investigation of [Fe(l-cysteinate)2(CO)2]2−: an example of non-heme CO coordination of possible relevance to CO binding to ion channel receptors

Hewison, L., Johnson, T. R., Mann, B. E., Meijer, A. J. H. M., Sawle, P., & Motterlini, R. (2011) · Dalton Transactions · doi:10.1039/c1dt10338e · Cited by 29

Optimization and Parallelization of Monaural Source Separation Algorithms in the openBliSSART Toolkit

Weninger, F. J., & Schuller, B. W. (2012) · Journal of Signal Processing Systems · doi:10.1007/s11265-012-0673-7 · Cited by 29

GiMMiK—Generating bespoke matrix multiplication kernels for accelerators: Application to high-order Computational Fluid Dynamics

Wozniak, B. D., Witherden, F., Russell, F. P., Vincent, P., & Kelly, P. H. J. (2016) · Computer Physics Communications · doi:10.1016/j.cpc.2015.12.012 · Cited by 29

A new numerical method for nonlocal electrostatics in biomolecular simulations

Weggler, S., Rutka, V., & Hildebrandt, A. (2010) · Journal of Computational Physics · doi:10.1016/j.jcp.2010.01.040 · Cited by 29

Performance Tuning of Matrix Multiplication in OpenCL on Different GPUs and CPUs

Matsumoto, K., Nakasato, N., & Sedukhin, S. G. (2012) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.companion.2012.59 · Cited by 29

On the effect of linear algebra implementations in real-time multibody system dynamics

González, M., González, F., Dopico, D., & Luaces, A. (2007) · Computational Mechanics · doi:10.1007/s00466-007-0218-2 · Cited by 29

Automatic Library Generation for BLAS3 on GPUs

Cui, H., Wang, L., Xue, J., Yang, Y., & Feng, X. (2011) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2011.33 · Cited by 29

Predicting and optimizing system utilization and performance via statistical machine learning

Patterson, D. A., & Ganapathi, A. (2009) · Cited by 29

Computation of azimuthal waves and their stability in thermal convection in rotating spherical shells with application to the study of a double-Hopf bifurcation

Sánchez, J., Garcia, F., & Net, M. (2013) · Physical Review E · doi:10.1103/physreve.87.033014 · Cited by 28

Dynamic Adaptation for Grid Computing

Buisson, J., André, F., & Pazat, J.-L. (2005) · Lecture notes in computer science · doi:10.1007/11508380_55 · Cited by 28

Automated Empirical Optimization of Software and the ATLAS Project

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ATLAS Version 3.9: Overview and Status

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