The DOI matches its Crossref record; 21 of the 25 references it cites were found in a registry or an index.
Automated empirical optimizations of software and the ATLAS project
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Thus, building a static auto-tuning library in the spirit of ATLAS [25] or OSKI [24] to implement the many different stencil kernels is infeasible.”
From An auto-tuning framework for parallel multicore stencil computations (2010) · cites this paper as [25]
Citations
Cites
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
Self-citationA 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
Self-citationFFTW: 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
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
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
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
Self-citationAutomatically 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
Self-citationOptimizing 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
Self-citationOptimizing 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
Research works printed but not linked: 1
- Hanson, 1973, A proposal for standard linear algebra subprograms, ACM SIGNUM Newsl., 8
Cited by
Sorted by citations
Items 801–900 of 1,000
Dynamically transforming data structures
Österlund, E., & Löwe, W. (2013) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2013.6693099 · Cited by 4
Generate and offshore: type-safe and modular code generation for low-level optimization
Takashima, N., Sakamoto, H., & Kameyama, Y. (2015) · Functional High-Performance Computing · doi:10.1145/2808091.2808096 · Cited by 4
BlackjackBench: Portable Hardware Characterization with Automated Results' Analysis
Anthony Danalis, A., Łuszczek, P., Marin, G., Vetter, J. S., & Dongarra, J. J. (2013) · The Computer Journal · doi:10.1093/comjnl/bxt057 · Cited by 4
Self-citationThe Mimetic Methods Toolkit: An object-oriented API for Mimetic Finite Differences
Sanchez, E. J., Paolini, C. P., & Castillo, J. E. (2014) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2013.12.046 · Cited by 4
Matrix Powers Algorithms for Trust Evaluation in Public-Key Infrastructures
Dumas, J., & Hossayni, H. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38004-4_9 · Cited by 4
Computer Algebra Systems – New strategies and techniques
Zotos, K. (2007) · Applied Mathematics and Computation · doi:10.1016/j.amc.2007.08.028 · Cited by 4
Performance Evaluation of Linear Algebra Routines
Caron, E., Desprez, F., Quinson, M., & Suter, F. (2004) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342004046046 · Cited by 4
Software for Petascale Computing Systems
Gropp, W. (2009) · Computing in Science & Engineering · doi:10.1109/mcse.2009.148 · Cited by 4
Auto-tuning Similarity Search Algorithms on Multi-core Architectures
Gedik, B. (2013) · International Journal of Parallel Programming · doi:10.1007/s10766-013-0239-8 · Cited by 4
MPC related computational capabilities of ARMv7A processors
Frison, G., & Jørgensen, J. B. (2015) · European Control Conference (ECC) · doi:10.1109/ecc.2015.7331062 · Cited by 4
Saunders, R. T., Jeffery, C. L., & Jones, D. T. (2007) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2007.370646 · Cited by 4
GLAF: A Visual Programming and Auto-tuning Framework for Parallel Computing
Krommydas, K. F., Sasanka, R., & Feng, W.-C. (2015) · Proceedings of the International Conference on Parallel Processing · doi:10.1109/icpp.2015.95 · Cited by 4
Multi-objective optimization of CMT-bone on hybrid processors
Gadou, M., Banerjee, T., & Ranka, S. (2016) · International Green and Sustainable Computing Conference · doi:10.1109/igcc.2016.7892612 · Cited by 4
Martins, M. A. D., Elias, R. N., & Coutinho, Á. L. G. A. (2007) · Springer eBooks · doi:10.1007/978-3-540-71351-7_23 · Cited by 4
Integration and exploitation of intra-routine malleability in BLIS
Rodríguez‐Sánchez, R., Igual, F. D., & Quintana–Ort́ı, E. S. (2019) · The Journal of Supercomputing · doi:10.1007/s11227-019-03078-z · Cited by 4
Automatic and Transparent Optimizations of an Application’s MPI Communication
Natvig, T., & Elster, A. C. (2007) · Springer eBooks · doi:10.1007/978-3-540-75755-9_26 · Cited by 4
Efficient dot product over word-size finite fieldsPreprint
Dumas, J. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0404008 · Cited by 4
Design of Interactive Environment for Numerically Intensive Parallel Linear Algebra Calculations
Łuszczek, P., & Dongarra, J. J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-25944-2_35 · Cited by 4
Self-citationYau, S.-M., Damevski, K., Karamcheti, V., Parker, S. G., & Zorin, D. (2008) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2008.4536324 · Cited by 4
Resource management in a multicore operating system
Peter, S. (2012) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-007579246 · Cited by 4
Accelerating BLAS on Custom Architecture through Algorithm-Architecture Co-designPreprint
Merchant, F., Vatwani, T., Chattopadhyay, A., Raha, S., Nandy, S. K., & Narayan, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.06385 · Cited by 4
Introduction of shared-memory parallelism in a distributed-memory multifrontal solver
L’Excellent, J.-Y., & Lakhdar, M. W. S. (2013) · HAL · Cited by 4
A model-driven approach for a new generation of adaptive librariesPreprint
Cianfriglia, M., Vella, F., Nugteren, C., Lokhmotov, A., & Fursin, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07060 · Cited by 4
Developing a High Performance Software Library with MPI and CUDA for Matrix ComputationsPreprint
Oancea, B., & Andrei, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.07174 · Cited by 4
LU Factorization with Partial Pivoting for a Multi-CPU, Multi-GPU Shared Memory System
Lawrence Berkeley National Lab. (LBNL), B., Jakub Kurzak, A., USDOE Office of Science (SC), Luszczek, P., Mathieu Faverge, A., & Dongarra, J. J. (2012) · doi:10.2172/1173291 · Cited by 4
Self-citationSySTeC: A Symmetric Sparse Tensor Compiler
Patel, R., Ahrens, W., & Amarasinghe, S. P. (2025) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1145/3696443.3708919 · Cited by 3
Mastering Software Variant Explosion for GPU Accelerators
Membarth, R., Hannig, F., Teich, J., Körner, M., & Eckert, W. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36949-0_15 · Cited by 3
Fast matrix multiplication via compiler‐only layered data reorganization and intrinsic lowering
Kuzma, B., Korostelev, I., de Carvalho, J. P. L., Moreira, J. E., Barton, C., Araujo, G. C. S. D., & Amaral, J. N. (2023) · Software Practice and Experience · doi:10.1002/spe.3214 · Cited by 3
Empirical Performance-Model Driven Data Layout Optimization
Lu, Q., Gao, X., Krishnamoorthy, S., Baumgartner, G., Ramanujam, J., & Sadayappan, P. (2005) · Lecture notes in computer science · doi:10.1007/11532378_7 · Cited by 3
Selecting linear algebra kernel composition using response time prediction
Hurault, A., Baek, K., & Casanova, H. (2014) · Software Practice and Experience · doi:10.1002/spe.2307 · Cited by 3
A Comparative Study on Task Dependent Scheduling Algorithms for Grid Computing
Mamani-Aliaga, A., Goldman, A., & Ngoko, Y. (2012) · Symposium on Computer Systems (WSCAD-SSC) · doi:10.1109/wscad-ssc.2012.41 · Cited by 3
Auto-Tuning of Thread Assignment for Matrix-Vector Multiplication on GPUs
Wang, J., Ma, X., Zhu, Y., & Sun, J. (2013) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e96.d.2319 · Cited by 3
Making TifaMMy fit for tomorrow: Towards future shared memory systems and beyond
Heinecke, A., & Trinitis, C. (2011) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcsim.2011.5999869 · Cited by 3
Utilization and Expansion of ppOpen-AT for OpenACC
Ohshima, S., Katagiri, T., & Matsumoto, M. (2016) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2016.123 · Cited by 3
Kelefouras, V. I. (2017) · Computing · doi:10.1007/s00607-016-0535-4 · Cited by 3
A Mathematical Programming Language Extension for Multilinear Algebra
Friedrich, F., Gutknecht, J., Morozov, O., & Hunziker, P. (2011) · Cited by 3
Anderson, E. (2017) · ACM Transactions on Mathematical Software · doi:10.1145/3061665 · Cited by 3
Komatsu, K., Kishitani, T., Sato, M., & Kobayashi, H. (2019) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2019.00127 · Cited by 3
Basic Linear Algebra Operations on TensorCore GPU
Zhang, S., Karihaloo, V., & Wu, P. (2020) · Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA) · doi:10.1109/scala51936.2020.00011 · Cited by 3
On Distributed Multiplication of Large-Scale Matrices
Glushan, V. M., & Yu., L. A. (2021) · IEEE International Conference on Application of Information and Communication Technologies (AICT) · doi:10.1109/aict52784.2021.9620434 · Cited by 3
Optimization of Matrix-Matrix Multiplication Algorithm for Matrix-Panel Multiplication on Intel KNL
Rizwan, M., Jung, E., Park, Y., Choi, J., & Kim, Y. (2022) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa56895.2022.10017947 · Cited by 3
Extending a predictable machine learning framework with efficient gemm-based convolution routines
Silva, I. D. A., Carle, T., Gauffriau, A., & Pagetti, C. (2023) · Real-Time Systems · doi:10.1007/s11241-023-09407-z · Cited by 3
Rank-Polymorphism for Shape-Guided Blocking
Šinkarovs, A., Koopman, T., & Scholz, S. (2023) · doi:10.1145/3609024.3609410 · Cited by 3
Bylina, B., Bylina, J., & Piekarz, M. (2023) · Annals of Computer Science and Information Systems · doi:10.15439/2023f6213 · Cited by 3
Wang, Z., Song, J., & Broccardo, M. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110459 · Cited by 3
Runtime Adaptive Matrix Multiplication for the SW26010 Many-Core Processor
Wu, Z., Li, M., Chi, M., Xu, L., & An, H. (2020) · IEEE Access · doi:10.1109/access.2020.3019302 · Cited by 3
YaskSite: Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures
Alappat, C. L., Seiferth, J., Hager, G., Korch, M., Rauber, T., & Wellein, G. (2021) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo51591.2021.9370316 · Cited by 3
Lopez, M. G., & Horton, M. D. (2014) · Springer eBooks · doi:10.1007/978-3-319-06548-9_3 · Cited by 3
DFT Performance Prediction in FFTW
Gu, L., & Li, X. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13374-9_10 · Cited by 3
High-Level User Interfaces for the DOE ACTS Collection
Drummond, L. A., Galiano, V., Migallón, V., & Penadés, J. (2007) · Springer eBooks · doi:10.1007/978-3-540-75755-9_31 · Cited by 3
A scalable performance prediction heuristic for implementation planning on heterogeneous systems
Wernsing, J. R., & Stitt, G. (2010) · IEEE/ACM/IFIP Workshop on Embedded Systems for Real-Time Multimedia (ESTIMedia) · doi:10.1109/estmed.2010.5666986 · Cited by 3
Autotuned parallel I/O for highly scalable biosequence analysis
You, H., Rekapalli, B., Liu, Q., & Moore, S. (2011) · TeraGrid Conference · doi:10.1145/2016741.2016772 · Cited by 3
Kelefouras, V. I., Kritikakou, A. S., & Goutis, C. E. (2015) · Computer Languages · doi:10.1016/j.cl.2015.01.003 · Cited by 3
Energy Optimization for Scientific Programs Using Auto-tuning Language ppOpen-AT
Katagiri, T., Luo, C., Suda, R., Hirasawa, S., & Ohshima, S. (2013) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc.2013.14 · Cited by 3
Modelization and Performance Evaluation of the DIET Middleware
Caron, E., Depardon, B., & Desprez, F. (2010) · Proceedings of the International Conference on Parallel Processing · doi:10.1109/icpp.2010.45 · Cited by 3
Exposing parallelism and locality in a runtime parallel optimization framework
Penry, D. A., Richins, D., Harris, T. S., Greenland, D. A., & Rehme, K. D. (2010) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/1787275.1787311 · Cited by 3
2nd Introduction to the Matrix package
Maechler, M., Bates, D. M., R Core Team, & Development Team (2006) · Cited by 3
Katagiri, T., Sakurai, T., Igai, M., Ohshima, S., Kuroda, H., Naono, K., & Nakajima, K. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38718-0_24 · Cited by 3
Large-Scale Program Behavior Analysis for Adaptation and Parallelization
Shen, X. (2006) · Cited by 3
A compiler toolkit for array-based languages targeting CPU/GPU hybrid systems
Garg, R. D., & Hendren, L. J. (2012) · Cited by 3
A programming system for future proofing performance critical libraries
Chang, L., Hajj, I. E., Kim, H.-S., Gómez-Luna, J., Dakkak, A., & Hwu, W. (2016) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/2851141.2851178 · Cited by 3
Cache Oblivious Matrix Multiplication Algorithm using Sequential Access Processing
Korde, P. S., & Khanale, P. B. (2011) · Research Journal of Information Technology · doi:10.3923/rjit.2011.61.67 · Cited by 3
Optimizing Metaheuristics and Hyperheuristics through Multi-level Parallelism on a Many-Core System
Cutillas-Lozano, J.-M., Domingo Giménez, C., & García, L. P. (2016) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2016.9 · Cited by 3
Parallel SQL Query Auto-Tuning on Multicore
Pankratius, V., & Heneka, M. (2011) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/1000022211 · Cited by 3
Optimization Approach to Accelerator Codesign
Prajapati, N., Rajopadhye, S. V., Djidjev, H. N., Santhi, N., Grosser, T., & Andonov, R. (2019) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2019.2926489 · Cited by 3
General Matrix-Matrix Multiplication Using SIMD Features of the PIII
Aberdeen, D., & Baxter, J. (2000) · Cited by 3
An Analytical Model for Loop Tiling Transformation
Kelefouras, V. I., Djemame, K., Κεραμίδας, Γ., & Voros, N. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04580-6_7 · Cited by 3
A Methodology for Efficient Tile Size Selection for Affine Loop Kernels
Kelefouras, V. I., Djemame, K., Κεραμίδας, Γ., & Voros, N. S. (2022) · International Journal of Parallel Programming · doi:10.1007/s10766-022-00734-5 · Cited by 3
Execution- and Prediction-Based Auto-Tuning of Parallel Read and Write Parameters
Agarwal, M., Jain, P., Singhvi, D., & Malakar, P. (2021) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys53884.2021.00100 · Cited by 3
Pradelle, B. (2011) · Cited by 3
An operation stacking framework for large ensemble computations
Belgin, M., Ribbens, C. J., & Back, G. (2007) · International Conference on Supercomputing · doi:10.1145/1274971.1274986 · Cited by 3
Functional parallels of sequential imperatives (short paper)
Rompf, T., & Brown, K. J. (2016) · ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation (PEPM) · doi:10.1145/3018882.3018891 · Cited by 3
Kemmer, T., Rjasanow, S., & Hildebrandt, A. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.08.008 · Cited by 3
Bisseling, R. H. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198788348.003.0005 · Cited by 3
Seinstra, F. J., & Koelma, D. C. (2004) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2003.1213418 · Cited by 3
X-Ray : Automatic Measurement of Hardware Parameters
Yotov, K., Pingali, K. K., & Stodghill, P. V. (2004) · eCommons (Cornell University) · Cited by 3
Middleware for Dynamic Adaptation of Component Applications
Norris, B., Bhowmick, S., Kaushik, D., & McInnes, L. C. (2007) · Springer eBooks · doi:10.1007/978-0-387-73659-4_8 · Cited by 3
A Modular Iterative Solver Package in a Categorical Language
Ashby, T. J., Kennedy, A. D., & O’boyle, M. F. P. (2005) · Springer eBooks · doi:10.1007/3-540-28504-0_12 · Cited by 3
Performance Engineering: Understanding and Improving thePerformance of Large-Scale Codes
Bailey, D. H., Lucas, R. F., Hovland, P., Norris, B., Yelick, K., Gunter, D., de Supinski, B. R., Quinlan, D., Worley, P. H., Vetter, J., et al. (2007) · University of North Texas Digital Library (University of North Texas) · Cited by 3
Self-citationImproving the performance of the linear systems solvers using CUDAPreprint
Oancea, B., Andrei, T., & Drăgoescu, R. M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.07207 · Cited by 3
Formal proof for delayed finite field arithmetic using floating point operatorsPreprint
Boldo, S., Daumas, M., & Pascal Giorgi, A. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0703026 · Cited by 3
Performance Modeling and Prediction for Dense Linear AlgebraPreprint
Peise, E. (2017) · arXiv (Cornell University) · doi:10.18154/rwth-2018-223017 · Cited by 3
An Efficient Algorithm for the Discrete Gabor Transform using full length WindowsPreprint
Søndergaard, P. (2009) · Cited by 3
On the Efficacy and High-Performance Implementation of Quaternion Matrix MultiplicationPreprint
Williams‐Young, D. B., & Li, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.05575 · Cited by 3
From Cluster to Grid: A Case Study in Scaling-Up a Molecular Electronics Simulation Code
Ribbens, C. J., Bora, P. K., Ventra, M. D., Hauck, J., Prabhakar, S., & TAYLOR, C. E. (2002) · VTechWorks (Virginia Tech) · Cited by 3
Report of the Workshop on Program Synthesis for Scientific ComputingPreprint
Finkel, H., & Laguna, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01687 · Cited by 3
Automated memory analysis: improving the design and implementation of iterative algorithms
Dennis, J. M. (2005) · Cited by 3
Découverte automatique des caractéristiques et capacités d'une plate-forme de calcul distribué
Quinson, M. (2003) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 3
Using Machine Learning to Automate Compiler Optimisation
Thomson, J. D. (2009) · ERA · Cited by 3
Parents’ Involvement in Early Childhood Care and Education: A Case Study
Qayyum, A., Khalil, S., Arif, S., Rafiq, S., & Makhdum, F. N. (2025) · Research Journal for Social Affairs · doi:10.71317/rjsa.003.04.0285 · Cited by 2
Adaptation of Legacy Codes to Context-Aware Composition Using Aspect-Oriented Programming
Danylenko, A., & Löwe, W. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30564-1_5 · Cited by 2
Using Experimental Data to Improve the Performance Modelling of Parallel Linear Algebra Routines
García, L.-P., Cuenca, J., & Domingo Giménez, C. (2008) · Springer eBooks · doi:10.1007/978-3-540-68111-3_122 · Cited by 2
An Infrastructure for Tackling Input-Sensitivity of GPU Program Optimizations
Shen, X., Liu, Y., Zhang, E. Z., & Bhamidipati, P. (2012) · International Journal of Parallel Programming · doi:10.1007/s10766-012-0236-3 · Cited by 2
Non-uniform DFT implementation for channel simulations in GPU
Natarajan, K., & Chandrachoodan, N. (2015) · National Conference on Communications · doi:10.1109/ncc.2015.7084858 · Cited by 2
Optimizing irregular data accesses for cluster and multicore architectures
Yelick, K., & Su, J. L.-S. (2010) · Cited by 2
Prospectus for a Dense Linear Algebra Software Library
Hida, Y., Demmel, J. W., Julien Langou, F., Jakub Kurzak, A., Gu, M., Buttari, A., Tomov, S. Z., Łuszczek, P., Langou, J., Marques, O., et al. (2007) · Chapman & Hall/CRC computer and information science series · doi:10.1201/9781420011296.ch29 · Cited by 2
Self-citationAutomatic tuning to performance modelling of matrix polynomials on multicore and multi-GPU systems
Boratto, M. D. C., Alonso, P., Domingo Giménez, C., & Lastovetsky, A. (2016) · The Journal of Supercomputing · doi:10.1007/s11227-016-1694-y · Cited by 2
A Haskell compiler for signal transforms
Mainland, G., & Johnson, J. R. (2017) · ACM SIGPLAN Notices · doi:10.1145/3170492.3136056 · Cited by 2
Katagiri, T. (2018) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2018.00167 · Cited by 2
Metadata
- Authors
- Clint Whaley, R., Petitet, A., & Dongarra, J. J.
- Affiliations
- University of Tennessee at Knoxville (US)
- Corresponding author
- Dongarra, Jack J.
- ORCID
- Dongarra, Jack J.
- Venue
- Parallel Computing
- Publisher
- Elsevier BV
- Published
- 2001-01
- Volume
- 27
- Issue
- 1-2
- Pages
- 3-35
- ISSN
- 0167-8191 (ISSN-L)
- OpenAlex
- W1964031104
- Cited by
- 1,317
- References
- 25
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 9 · 2023: 25 · 2022: 19 · 2021: 25 · 2020: 39 · 2019: 22 · 2018: 37 · 2017: 37 · 2016: 57 · 2015: 40 · 2014: 78 · 2013: 88 · 2012: 101
- FWCI
- 66.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Parallel Computing (publishedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Matrix Theory and Algorithms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Numerical Methods and Algorithms
- Keywords
- ATLAS project, BLAS, linear algebra software, software maintenance, Moore's law
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review