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 4,493

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

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

Automatically Tuned Linear Algebra Software

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Optimizing supercompilers for supercomputers

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

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

WattProf: A Flexible Platform for Fine-Grained HPC Power Profiling

Rashti, M. J., Sabin, G., Vansickle, D., & Norris, B. (2015) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster.2015.121 · Cited by 18

Identification of the Reactive cis,mer Isomer of [Ir(CO)2I3Me]−: Relation to the Mechanism of Iridium-Catalyzed Methanol Carbonylation

Haynes, A., Meijer, A. J. H. M., Lyons, J. R., & Adams, H. (2008) · Inorganic Chemistry · doi:10.1021/ic8017858 · Cited by 18

Optimization of algorithms with OPAL

Audet, C., Dang, K.-C., & Orban, D. (2014) · Mathematical Programming Computation · doi:10.1007/s12532-014-0067-x · Cited by 18

On-line soft error correction in matrix–matrix multiplication

Wu, P., Ding, C., Chen, L., Davies, T., Karlsson, C., & Chen, Z. (2013) · Journal of Computational Science · doi:10.1016/j.jocs.2013.05.002 · Cited by 18

Automatic mapping of parallel applications on multicore architectures using the Servet benchmark suite

González‐Domínguez, J., Taboada, G. L., Fraguela, B. B., Martín, M. J., & Touriño, J. (2012) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2011.12.007 · Cited by 18

Autotuning GEMMs for Fermi

Jakub Kurzak, A., Tomov, S. Z., & Dongarra, J. J. (2011) · Cited by 18

Efficient hierarchical online-autotuning

Pfaffe, P., Grosser, T., & Tillmann, M. (2019) · International Conference on Supercomputing · doi:10.1145/3330345.3330377 · Cited by 18

A study of shared-memory parallelism in a multifrontal solver

L’Excellent, J.-Y., & Sid-Lakhdar, W. M. (2014) · Parallel Computing · doi:10.1016/j.parco.2014.02.003 · Cited by 18

Gas-phase study of new organozinc reagents by IRMPD-spectroscopy, computational modelling and tandem-MS

Massah, A. R., Dreiocker, F., Jackson, R. F. W., Pickup, B. T., Oomens, J., Meijer, A. J. H. M., & Schäfer, M. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp20747d · Cited by 18

Empirically tuning LAPACK’s blocking factor for increased performance

Whaley, R. C. (2008) · Proceedings of the International Multiconference on Computer Science and Information Technology · doi:10.1109/imcsit.2008.4747256 · Cited by 18

Direct INDO/SCI method for excited state calculations

Tomlinson, A. L., & Yaron, D. (2003) · Journal of Computational Chemistry · doi:10.1002/jcc.10336 · Cited by 18

Effective padding of multidimensional arrays to avoid cache conflict misses

Hong, C., Bao, W., Cohen, A., Krishnamoorthy, S., Pouchet, L.-N., Rastello, F., Ramanujam, J., & Sadayappan, P. (2016) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/2908080.2908123 · Cited by 17

Simple, Accurate, Analytical Time Modeling and Optimal Tile Size Selection for GPGPU Stencils

Prajapati, N., Ranasinghe, W., Rajopadhye, S. V., Andonov, R., Djidjev, H. N., & Grosser, T. (2017) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/3018743.3018744 · Cited by 17

A Comprehensive Survey of Benchmarks for Improvement of Software's Non-Functional Properties

Blot, A., & Petke, J. (2025) · ACM Computing Surveys · doi:10.1145/3711119 · Cited by 17

Empirical performance model-driven data layout optimization and library call selection for tensor contraction expressions

Lu, Q., Gao, X., Krishnamoorthy, S., Baumgartner, G., Ramanujam, J., & Sadayappan, P. (2011) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2011.09.006 · Cited by 17

A design methodology for domain-optimized power-efficient supercomputing

Mohiyuddin, M., Murphy, M., Oliker, L., Shalf, J. M., Wawrzynek, J., & Williams, S. (2009) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/1654059.1654072 · Cited by 17

The Operator Variant Selection Problem on Heterogeneous Hardware.

Rosenfeld, V., Heimel, M., Viebig, C., & Markl, V. (2015) · Very Large Data Bases · Cited by 17

PERI - auto-tuning memory-intensive kernels for multicore

Williams, S., Datta, K., Carter, J. T., Oliker, L., Shalf, J. M., Yelick, K., & Bailey, D. (2008) · Journal of Physics Conference Series · doi:10.1088/1742-6596/125/1/012038 · Cited by 17

Model-Driven SIMD Code Generation for a Multi-resolution Tensor Kernel

Stock, K., Henretty, T., Murugandi, I. T., Sadayappan, P., & Harrison, R. J. (2011) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2011.101 · Cited by 17

Achieving accurate and context‐sensitive timing for code optimization

Whaley, R. C., & Castaldo, A. M. (2008) · Software Practice and Experience · doi:10.1002/spe.884 · Cited by 17

Towards Automatic and Adaptive Optimizations of MPI Collective Operations

Pješivac–Grbović, J. (2007) · Cited by 17

Investigating Adaptive Compilation Using the Mipspro Compiler

Cooper, K. D., & Waterman, T. (2005) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342005056142 · Cited by 17

Computational Quality of Service for Scientific CCA Applications: Composition, Substitution, and Reconfiguration

McInnes, L. C., Ray, J., Armstrong, R., Dahlgren, T. L., Malony, A. D., Norris, B., Shende, S., Kenny, J. P., & Steensland, J. (2006) · Cited by 17

A Synergetic Approach to Throughput Computing on x86-Based Multicore Desktops

Luk, C.-K., Newton, R., Hasenplaugh, W., Hampton, M., & Lowney, G. N. (2010) · IEEE Software · doi:10.1109/ms.2011.2 · Cited by 17

Throughput-Distortion Computation of Generic Matrix Multiplication: Toward a Computation Channel for Digital Signal Processing Systems

Anastasia, D., & Andreopoulos, Y. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2011.2176337 · Cited by 16

An Efficient, Portable and Generic Library for Successive Cancellation Decoding of Polar Codes

Cassagne, A., Gal, B. L., Leroux, C., Aumage, O., & Barthou, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-29778-1_19 · Cited by 16

Auto-tuning of Sparse Matrix-Vector Multiplication on Graphics Processors

Abu-Sufah, W., & Karim, A. A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38750-0_12 · Cited by 16

Collapsing towers of interpreters

Amin, N., & Rompf, T. (2017) · Proceedings of the ACM on Programming Languages · doi:10.1145/3158140 · Cited by 16

GPU vs FPGA: A Comparative Analysis for Non-standard Precision

Minhas, U. I., Bayliss, S., & Constantinides, G. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-05960-0_32 · Cited by 16

Staged Static Techniques to Efficiently Implement Array Copy Semantics in a MATLAB JIT Compiler

Lameed, N. A., & Hendren, L. J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19861-8_3 · Cited by 16

Delivery Massager: A tool for propagating seismic inversion information into reservoir models

Gunning, J. S., Glinsky, M. E., & White, C. (2007) · Computers & Geosciences · doi:10.1016/j.cageo.2006.09.004 · Cited by 16

Visual simulation of shockwaves

Sewall, J. D., Galoppo, N., Tsankov, G., & Lin, M. C. (2009) · Graphical Models · doi:10.1016/j.gmod.2009.03.002 · Cited by 16

DESOLA: An active linear algebra library using delayed evaluation and runtime code generation

Russell, F. P., Mellor, M. R., Kelly, P. H. J., & Beckmann, O. (2008) · Science of Computer Programming · doi:10.1016/j.scico.2008.06.002 · Cited by 16

OpenBliSSART: Design and evaluation of a research toolkit for Blind Source Separation in Audio Recognition Tasks

Weninger, F. J., Lehmann, A., & Schuller, B. W. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946809 · Cited by 16

Automated transformation for performance-critical kernels

Yi, Q., & Whaley, R. C. (2007) · doi:10.1145/1512762.1512773 · Cited by 16

Automatic measurement of memory hierarchy parameters

Yotov, K., Pingali, K. K., & Stodghill, P. V. (2005) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/1071690.1064233 · Cited by 16

A Fully Empirical Autotuned Dense QR Factorization for Multicore Architectures

Agullo, E., Dongarra, J. J., Nath, R., & Tomov, S. Z. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23397-5_19 · Cited by 16

Adaptive and Hybrid Algorithms: classification and illustration on triangular system solving

Cung, V., Danjean, V., Dumas, J., Gautier, T., Huard, G., Raffin, B., Rapine, C., Roch, J.-L., Cung, V., Vincent, C., et al. (2006) · Cited by 16

Configuring Large High-Performance Clusters at Lightspeed: A Case Study

Papadopoulos, P. M., Papadopoulos, C. A., Katz, M. J., Link, W. J., & Bruno, G. (2004) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342004046056 · Cited by 16

Scheduling and memory optimizations for sparse direct solver on multi-core/multi-gpu duster systems

Lacoste, X. (2015) · doi:10.70675/cd52b8c9z2e01z4a44zbf46z2a50f95115bd · Cited by 16

Exploring the Optimization Space of Dense Linear Algebra Kernels

Yi, Q., & Qasem, A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89740-8_24 · Cited by 15

TuningGenie: Auto-Tuning Framework Based on Rewriting Rules

Ivanenko, P. А., Doroshenko, А. Y., & Zhereb, K. (2014) · Communications in computer and information science · doi:10.1007/978-3-319-13206-8_7 · Cited by 15

Elastic computing: A portable optimization framework for hybrid computers

Wernsing, J. R., & Stitt, G. (2012) · Parallel Computing · doi:10.1016/j.parco.2012.05.001 · Cited by 15

Optimizing Sorting with Machine Learning Algorithms

Li, X., Garzarán, M. J., & Padua, D. (2007) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2007.370499 · Cited by 15

Fracture safety of double-porous hydroxyapatite biomaterials

Dejaco, A., Комлев, В. С., Jaroszewicz, J., Święszkowski, W., & Hellmich, C. (2016) · Bioinspired Biomimetic and Nanobiomaterials · doi:10.1680/jbibn.15.00021 · Cited by 15

Continuous shape shifting: Enabling loop co-optimization via near-free dynamic code rewriting

Jain, A., Laurenzano, M. A., Tang, L., & Mars, J. (2016) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro.2016.7783726 · Cited by 15

Towards many-core implementation of LU decomposition using Peano Curves

Heinecke, A., & Bader, M. (2009) · doi:10.1145/1531666.1531672 · Cited by 15

Dynamic Distribution of Workload between CPU and GPU for a Parallel Conjugate Gradient Method in an Adaptive FEM

Lang, J., & Rünger, G. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.05.193 · Cited by 15

A Fast Semidirect Least Squares Algorithm for Hierarchically Block Separable Matrices

Ho, K. L., & Greengard, L. (2014) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/120902677 · Cited by 15

Multiparadigm Space Processing for Hyperspectral Imaging

Jacobs, A., Conger, C., & George, A. D. (2008) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2008.4526468 · Cited by 15

Extending Resolution of Fault Slip With Geodetic Networks Through Optimal Network Design

Sathiakumar, S., Barbot, S., & Agram, P. S. (2017) · Journal of Geophysical Research Solid Earth · doi:10.1002/2017jb014326 · Cited by 15

Autotuning Numerical Dense Linear Algebra for Batched Computation With GPU Hardware Accelerators

Dongarra, J. J., Gates, M., Jakub Kurzak, A., Łuszczek, P., & Tsai, Y. M. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2868961 · Cited by 15

A new singular value decomposition algorithm suited to parallelization and preliminary results

Konda, T., Takata, M., Iwasaki, M., & Nakamura, Y. (2006) · Cited by 15

Evaluating a High-Level Parallel Language (GpH) for Computational GRIDs

Zain, A. D. A., Trinder, P. W., Michaelson, G. J., & Loidl, H. (2008) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2007.70728 · Cited by 15

Performance Modeling for Dynamic Algorithm Selection

McCracken, M. O., Snavely, A. E., & Malony, A. D. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44864-0_77 · Cited by 15

Dependence-Based Code Generation for a CELL Processor

Zhao, Y. F., & Kennedy, K. (2007) · Springer eBooks · doi:10.1007/978-3-540-72521-3_6 · Cited by 15

Reducing 3D Fast Wavelet Transform Execution Time Using Blocking and the Streaming SIMD Extensions

Bernabé, G., García, J. M., & González, J. (2005) · The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology · doi:10.1007/s11265-005-6651-6 · Cited by 15

Sparse Bayesian classification of predicate arguments

Johansson, R., & Nugues, P. (2005) · Conference on Computational Natural Language Learning · doi:10.3115/1706543.1706575 · Cited by 15

Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication

Yılmaz, B., Aktemur, B., Garzarán, M. J., Kamin, S., & Kıraç, F. (2016) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/2851500 · Cited by 14

Scaling LAPACK panel operations using parallel cache assignment

Castaldo, A. M., Whaley, R. C., & Samuel, S. (2013) · ACM Transactions on Mathematical Software · doi:10.1145/2491491.2491492 · Cited by 14

Reactivity of Ir(iii) carbonyl complexes with water: alternative by-product formation pathways in catalytic methanol carbonylation

Elliott, P. I. P., Haak, S., Meijer, A. J. H. M., Sunley, G. J., & Haynes, A. (2013) · Dalton Transactions · doi:10.1039/c3dt52092g · Cited by 14

A Portable and High-Performance General Matrix-Multiply (GEMM) Library for GPUs and Single-Chip CPU/GPU Systems

Garg, R., & Hendren, L. J. (2014) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/pdp.2014.40 · Cited by 14

The Essence of Multi-stage Evaluation in LMS

Rompf, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30936-1_17 · Cited by 14

Loop transformations leveraging hardware prefetching

Sioutas, S., Stuijk, S., Corporaal, H., Basten, T., & Somers, L. (2018) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1145/3168823 · Cited by 14

On the impact of data input sets on statistical compiler tuning

Haneda, M., Knijnenburg, P. M. W., & Wijshoff, H. A. G. (2006) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2006.1639724 · Cited by 14

Extendable pattern-oriented optimization directives

Cui, H., Xue, J., Wang, L., Yang, Y., Feng, X., & Fan, D. (2011) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo.2011.5764679 · Cited by 14

Automated empirical optimization of high performance floating point kernels

Whaley, R. C., & Whalley, D. B. (2004) · DigiNole (Florida State University) · Cited by 14

Install-Time System for Automatic Generation of Optimized Parallel Sorting Algorithms.

Olszewski, M., & Voss, M. (2004) · Cited by 14

Optimization Techniques for 3D-FWT on Systems with Manycore GPUs and Multicore CPUs

Bernabé, G., Cuenca, J., & Domingo Giménez, C. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.05.195 · Cited by 14

Evaluation of a performance portable lattice Boltzmann code using OpenCL

McIntosh‐Smith, S., & Curran, D. (2014) · doi:10.1145/2664666.2664668 · Cited by 14

Auto-tuning of Computation Kernels from an FDM Code with ppOpen-AT

Katagiri, T., Ohshima, S., & Matsumoto, M. (2014) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc.2014.22 · Cited by 14

A framework for auto-tuning HDF5 applications

Behzad, B., Huchette, J., Luu, H. V. T., Aydt, R. A., Byna, S., Yao, Y., Koziol, Q., & Prabhat (2013) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/2462902.2462931 · Cited by 14

PERI auto-tuning

Bailey, D. H., Chame, J., Chen, C., Dongarra, J. J., Hall, M. W., Hollingsworth, J. K., Hovland, P., Moore, S., Seymour, K., Shin, J., et al. (2008) · Journal of Physics Conference Series · doi:10.1088/1742-6596/125/1/012089 · Cited by 14

Integrating MPI-based numerical software into an advanced parallel computing environment

D’Ambra, P., Danelutto, M., di Serafino, D., & Lapegna, M. (2003) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/empdp.2003.1183601 · Cited by 14

Performance-Based Numerical Solver Selection in the Lighthouse Framework

Jessup, E. R., Motter, P., Norris, B., & Sood, K. (2016) · SIAM Journal on Scientific Computing · doi:10.1137/15m1028406 · Cited by 16

Benchmarking in Data Envelopment Analysis: An Approach Based on Genetic Algorithms and Parallel Programming

Aparicio, J., López-Espín, J. J., Martinez-Moreno, R., & Pastor, J. T. (2014) · Advances in Operations Research · doi:10.1155/2014/431749 · Cited by 14

An Adaptive Strategy for Inline Substitution

Cooper, K. D., Harvey, T. J., & Waterman, T. (2008) · Springer eBooks · doi:10.1007/978-3-540-78791-4_5 · Cited by 14

On implicit-factorization constraint preconditioners

Dollar, H. S., Gould, N. I. M., & Wathen, A. (2006) · Nonconvex optimization and its applications · doi:10.1007/0-387-30065-1_5 · Cited by 14

Performance Study of LU Decomposition on the Programmable GPU

Ino, F., Matsui, M., Goda, K., & Kenichi Hagihara, A. (2005) · Lecture notes in computer science · doi:10.1007/11602569_13 · Cited by 14

Delayed Evaluation, Self-optimising Software Components as a Programming Model

Liniker, P., Beckmann, O., & Kelly, P. H. J. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45706-2_92 · Cited by 14

Towards the design of an automatically tuned linear algebra library

Cuenca, J., Domingo Giménez, C., & Gonzalez, J. (2003) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/empdp.2002.994270 · Cited by 14

pMapper: Automatic Mapping of Parallel Matlab Programs

Bliss, N. T., Hoffmann, H., Bond, R., Chan, H. K., Kepner, J., & Wong, E. R. (2006) · DoD High Performance Computing Modernization Program Users Group Conference (HPCMP-UGC) · doi:10.1109/dodugc.2005.53 · Cited by 14

BLISlab: A Sandbox for Optimizing GEMMPreprint

Huang, J., & Geijn, R. A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.00076 · Cited by 14

Scheduling Tasks over Multicore machines enhanced with Accelerators : a Runtime System’s Perspective

Augonnet, C. (2011) · doi:10.70675/ebf6c770zbd52z4fa0z9b27z202c46301d00 · Cited by 14

Generation of Multigrid-based Numerical Solvers for FPGA Accelerators

Schmitt, C., Schmid, M., Hannig, F., Teich, J., Kuckuk, S., & Köstler, H. (2015) · doi:10.13140/2.1.1680.9760 · Cited by 13

A Generalized Approach to Linear Transform Approximations with Applications to the Discrete Cosine Transform

Chen, Y., & Sundaram, H. (2008) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2008/736460 · Cited by 13

Engineering parallel applications with tunable architectures

Schaefer, C. A., Pankratius, V., & Tichy, W. F. (2010) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/1806799.1806859 · Cited by 13

Examination of the Coordination Sphere of AlIII in Trifluoromethyl‐Heteroarylalkenolato Complex Ions by Gas‐Phase IRMPD Spectroscopy and Computational Modelling

Brückmann, L., Tyrra, W., Mathur, S., Berden, G., Oomens, J., Meijer, A. J. H. M., & Schäfer, M. (2012) · ChemPhysChem · doi:10.1002/cphc.201200132 · Cited by 13

A genetic algorithm based autotuning approach for performance and energy optimization

Banerjee, T., & Ranka, S. (2015) · International Green and Sustainable Computing Conference · doi:10.1109/igcc.2015.7393708 · Cited by 13

TurboTiling

Mehta, S., Garg, R., Trivedi, N., & Yew, P.-C. (2016) · International Conference on Supercomputing · doi:10.1145/2925426.2926288 · Cited by 13

Reflections on LMS: exploring front-end alternatives

Rompf, T. (2016) · Symposium on Scala · doi:10.1145/2998392.2998399 · Cited by 13

Optimizing tensor contractions for embedded devices with racetrack memory scratch-pads

Khan, A. A., Rink, N. A., Hameed, F., & Castrillón, J. (2019) · ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES) · doi:10.1145/3316482.3326351 · Cited by 13

Non-smooth Bayesian optimization in tuning scientific applications

Luo, H., Cho, Y., Demmel, J. W., Kozachenko, I., Li, X. S., & Liu, Y. (2024) · The International Journal of High Performance Computing Applications · doi:10.1177/10943420241278981 · Cited by 13

Dynamically Controlling Node-Level Parallelism in Hadoop

Kc, K., & Freeh, V. W. (2015) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2015.49 · Cited by 13

P-Ray: A Software Suite for Multi-core Architecture Characterization

Duchâteau, A., Sidelnik, A., Garzarán, M. J., & Padua, D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89740-8_13 · Cited by 13

High-Order Discontinuous Galerkin Methods by GPU Metaprogramming

Klöckner, A., Warburton, T., & Hesthaven, J. S. (2013) · Lecture notes in earth system sciences · doi:10.1007/978-3-642-16405-7_23 · Cited by 13

Processes Distribution of Homogeneous Parallel Linear Algebra Routines on Heterogeneous Clusters

Cuenca, J., García, L. P., Domingo Giménez, C., & Dongarra, J. J. (2005) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/clustr.2005.347021 · Cited by 13

Early Experiences for Adaptation of Auto-tuning by ppOpen-AT to an Explicit Method

Katagiri, T., Ito, S., & Ohshima, S. (2013) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc.2013.15 · Cited by 13

Experiences of sparse direct symmetric solvers

Scott, J. A., & Hu, Y. (2007) · ACM Transactions on Mathematical Software · doi:10.1145/1268769.1268772 · Cited by 13

Tuning basic Linear Algebra Routines for Hybrid CPU+GPU Platforms

Bernabé, G., Cuenca, J., García, L.-P., & Domingo Giménez, C. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.05.003 · Cited by 13

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

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