Relevant bibliographies by topics / Mixed precision computation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Mixed precision computation'

Author: Grafiati
Published: 25 January 2025
Last updated: 31 July 2025

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Journal articles on the topic "Mixed precision computation"

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Van Zee, Field G., Devangi N. Parikh, and Robert A. Van De Geijn. "Supporting Mixed-domain Mixed-precision Matrix Multiplication within the BLIS Framework." ACM Transactions on Mathematical Software 47, no. 2 (2021): 1–26. http://dx.doi.org/10.1145/3402225.

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We approach the problem of implementing mixed-datatype support within the general matrix multiplication ( gemm ) operation of the BLAS-like Library Instantiation Software framework, whereby each matrix operand A , B , and C may be stored as single- or double-precision real or complex values. Another factor of complexity, whereby the matrix product and accumulation are allowed to take place in a precision different from the storage precisions of either A or B , is also discussed. We first break the problem into orthogonal dimensions, considering the mixing of domains separately from mixing prec
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Al-Marakeby, A. "PRECISION ON DEMAND: A NOVEL LOSSLES MIXED-PRECISION COMPUTATION TECHNIQUE." Journal of Al-Azhar University Engineering Sector 15, no. 57 (2020): 1046–56. http://dx.doi.org/10.21608/auej.2020.120378.

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Wang, Shengquan, Chao Wang, Yong Cai, and Guangyao Li. "A novel parallel finite element procedure for nonlinear dynamic problems using GPU and mixed-precision algorithm." Engineering Computations 37, no. 6 (2020): 2193–211. http://dx.doi.org/10.1108/ec-07-2019-0328.

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Purpose The purpose of this paper is to improve the computational speed of solving nonlinear dynamics by using parallel methods and mixed-precision algorithm on graphic processing units (GPUs). The computational efficiency of traditional central processing units (CPUs)-based computer aided engineering software has been difficult to satisfy the needs of scientific research and practical engineering, especially for nonlinear dynamic problems. Besides, when calculations are performed on GPUs, double-precision operations are slower than single-precision operations. So this paper implemented mixed
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Liu, Xingchao, Mao Ye, Dengyong Zhou, and Qiang Liu. "Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8697–705. http://dx.doi.org/10.1609/aaai.v35i10.17054.

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We consider the post-training quantization problem, which discretizes the weights of pre-trained deep neural networks without re-training the model. We propose multipoint quantization, a quantization method that approximates a full-precision weight vector using a linear combination of multiple vectors of low-bit numbers; this is in contrast to typical quantization methods that approximate each weight using a single low precision number. Computationally, we construct the multipoint quantization with an efficient greedy selection procedure, and adaptively decides the number of low precision poin
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Zhang, Jianfei, and Lei Zhang. "Efficient CUDA Polynomial Preconditioned Conjugate Gradient Solver for Finite Element Computation of Elasticity Problems." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/398438.

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Graphics processing unit (GPU) has obtained great success in scientific computations for its tremendous computational horsepower and very high memory bandwidth. This paper discusses the efficient way to implement polynomial preconditioned conjugate gradient solver for the finite element computation of elasticity on NVIDIA GPUs using compute unified device architecture (CUDA). Sliced block ELLPACK (SBELL) format is introduced to store sparse matrix arising from finite element discretization of elasticity with fewer padding zeros than traditional ELLPACK-based formats. Polynomial preconditioning
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C., T. Kelly. "Newton's Method in Mixed-Precision." SIAM Review 64, no. 1 (2022): 191–211. https://doi.org/10.5281/zenodo.6835437.

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<strong>Abstract:&nbsp;</strong> We investigate the use of reduced precision arithmetic to solve the linear equation for the Newton step. If one neglects the backward error in the linear solve, then well-known convergence theory implies that using single precision in the linear solve has very little negative effect on the nonlinear convergence rate. However, if one considers the effects of backward error, then the usual textbook estimates are very pessimistic and even the state-of-the-art estimates using probabilistic rounding analysis do not fully conform to experiments. We report on experime
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Tian, Luo, and Peng Wang. "An Effective Mixed-Precision Quantization Method for Joint Image Deblurring and Edge Detection." Electronics 14, no. 9 (2025): 1767. https://doi.org/10.3390/electronics14091767.

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Deploying deep neural networks (DNNs) for joint image deblurring and edge detection often faces challenges due to large model size, which restricts practical applicability. Although quantization has emerged as an effective solution to this issue, conventional quantization methods frequently struggle to optimize for the unique characteristics of the targeted model. This paper introduces a mixed-precision quantization method that dynamically adjusts quantization precision based on the edge regions of the input image. High-precision quantization is applied to edge neighborhoods to preserve critic
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Molina, Roméo, Vincent Lafage, David Chamont, and Fabienne Jézéquel. "Investigating mixed-precision for AGATA pulse-shape analysis." EPJ Web of Conferences 295 (2024): 03020. http://dx.doi.org/10.1051/epjconf/202429503020.

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The AGATA project aims at building a 4π gamma-ray spectrometer consisting of 180 germanium crystals, each crystal being divided into 36 segments. Each gamma ray produces an electrical signal within several neighbouring segments, which is compared with a data base of reference signals, enabling to locate the interaction. This step is called Pulse-Shape Analysis (PSA). In the execution chain leading to the PSA, we observe successive data conversions: the original 14-bit integers given by the electronics are finally converted to 32-bit floats. This made us wonder about the real numerical accuracy
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Yang, Linjie, and Qing Jin. "FracBits: Mixed Precision Quantization via Fractional Bit-Widths." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 10612–20. http://dx.doi.org/10.1609/aaai.v35i12.17269.

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Model quantization helps to reduce model size and latency of deep neural networks. Mixed precision quantization is favorable with customized hardwares supporting arithmetic operations at multiple bit-widths to achieve maximum efficiency. We propose a novel learning-based algorithm to derive mixed precision models end-to-end under target computation constraints and model sizes. During the optimization, the bit-width of each layer / kernel in the model is at a fractional status of two consecutive bit-widths which can be adjusted gradually. With a differentiable regularization term, the resource
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Stupishin, Leonid U., and Konstantin E. Nikitin. "Mixed Finite Element of Geometrically Nonlinear Shallow Shells of Revolution." Applied Mechanics and Materials 501-504 (January 2014): 514–17. http://dx.doi.org/10.4028/www.scientific.net/amm.501-504.514.

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The computation method for shallow shell of revolution in mixed finite-element formulation is developed. Final equations are constructed by the Galerkin method. Results of solution of test task are represented. Precision and convergence of results is analyzed.
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Dissertations / Theses on the topic "Mixed precision computation"

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Steffy, Daniel E. "Topics in exact precision mathematical programming." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39639.

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The focus of this dissertation is the advancement of theory and computation related to exact precision mathematical programming. Optimization software based on floating-point arithmetic can return suboptimal or incorrect resulting because of round-off errors or the use of numerical tolerances. Exact or correct results are necessary for some applications. Implementing software entirely in rational arithmetic can be prohibitively slow. A viable alternative is the use of hybrid methods that use fast numerical computation to obtain approximate results that are then verified or corrected with s
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Robeyns, Matthieu. "Mixed precision algorithms for low-rank matrix and tensor approximations." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG095.

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La gestion des données est souvent réalisée par des objets mathématiques tels que les matrices et les tenseurs, qui sont la généralisation des matrices à plus de deux dimensions.Certains domaines d'application nécessitent de stocker trop d'éléments, créant des tenseurs trop grands ; ce problème est connu sous le nom de emph curse of dimensionality.Des méthodes mathématiques telles que les approximations de rang faible ont été développées pour réduire la dimensionnalité de ces objets malgré un coût très élevé en temps de calcul.De plus, de nouvelles architectures informatiques telles que les GP
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Books on the topic "Mixed precision computation"

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Li, Wei, Leilei Ji, Ramesh Agarwal, Weidong Shi, and Ling Zhou. Mixed-flow Pumps: Modeling, Simulation, and Measurements. ASME-Wiley, 2024. http://dx.doi.org/10.1115/1.862mfp.

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Learn to improve and optimize the design and operation of mixed-flow pumps. Mixed-flow pumps have a huge range of applications in agriculture, hydroelectric power, and other industries that incorporate fluid transport. They are centrifugal pumps incorporating the characteristics of both axial and radial pumps to increase the flow rate and discharge pressure. Though essential in a variety of industries, they pose serious challenges to numerical simulation methods, challenges which are starting to be met by the application of computational fluid dynamics using high-performance computing. Mixed-f
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Book chapters on the topic "Mixed precision computation"

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Giraud, Luc, Azzam Haidar, and Layne T. Watson. "Mixed-Precision Preconditioners in Parallel Domain Decomposition Solvers." In Lecture Notes in Computational Science and Engineering. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75199-1_44.

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Ben Khalifa, Dorra, Matthieu Martel, and Assalé Adjé. "POP: A Tuning Assistant for Mixed-Precision Floating-Point Computations." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46902-3_5.

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Carson, Erin, and Eda Oktay. "Mixed Precision FGMRES-Based Iterative Refinement for Weighted Least Squares." In Lecture Notes in Computational Science and Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86173-4_23.

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Carrillo, Carlos, Tomás Margalef, Antonio Espinosa, and Ana Cortés. "Impact of Mixed-Precision: A Way to Accelerate Data-Driven Forest Fire Spread Systems." In Computational Science – ICCS 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36021-3_5.

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Glimberg, S. L., A. P. Engsig-Karup, and M. G. Madsen. "A Fast GPU-Accelerated Mixed-Precision Strategy for Fully Nonlinear Water Wave Computations." In Numerical Mathematics and Advanced Applications 2011. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33134-3_68.

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Halbiniak, Kamil, Krzysztof Rojek, Sergio Iserte, and Roman Wyrzykowski. "Unleashing the Potential of Mixed Precision in AI-Accelerated CFD Simulation on Intel CPU/GPU Architectures." In Computational Science – ICCS 2024. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63778-0_15.

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Freytag, Gabriel, João V. F. Lima, Paolo Rech, and Philippe O. A. Navaux. "Impact of Reduced and Mixed-Precision on the Efficiency of a Multi-GPU Platform on CFD Applications." In Computational Science and Its Applications – ICCSA 2022 Workshops. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10542-5_39.

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Baccar, Sahbi, Timothée Levi, Dominique Dallet, and François Barbara. "Optimizing Model Precision in High Temperatures for Efficient Analog and Mixed-Signal Circuit Design Using Modern Behavioral Modeling Technique: An Industrial Case Study." In Computational Intelligence in Analog and Mixed-Signal (AMS) and Radio-Frequency (RF) Circuit Design. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19872-9_7.

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Goddeke, Dominik, and Robert Strzodka. "Mixed-Precision GPU-Multigrid Solvers with Strong Smoothers." In Chapman & Hall/CRC Computational Science. CRC Press, 2010. http://dx.doi.org/10.1201/b10376-11.

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Shubneet, Mr, Anushka Raj Yadav, Paras Mahajan, Partha Chanda, and Atahar Shihab. "DEEP LEARNING FOR DATA SCIENCE: ARCHITECTURES, ALGORITHMS, AND REALWORLD APPLICATIONS." In Artificial Intelligence Technology in Healthcare: Security and Privacy Issues. Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd., 2025. https://doi.org/10.58532/nbennuraith7.

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Deep learning, a specialized subset of machine learning, employs multi-layered neural networks to autonomously learn hierarchical data representations, eliminating the need for manual feature engineering required in traditional machine learning. Key innovations like convolutional neural networks (CNNs) rev olutionized computer vision through spatial hierarchy learning, while recurrent neural networks (RNNs) enabled sequential data processing for time-series and NLP tasks. The backpropagation algorithm remains central to training these models, optimizing weights via gradient descent while lever
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Conference papers on the topic "Mixed precision computation"

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Bertaccini, Luca, Siyuan Shen, Torsten Hoefler, and Luca Benini. "Extending RISC-V for Efficient Overflow Recovery in Mixed-Precision Computations." In 2024 IEEE 42nd International Conference on Computer Design (ICCD). IEEE, 2024. https://doi.org/10.1109/iccd63220.2024.00048.

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Wang, Junjie, Zhi-Ming Li, Sheng Zuo, Shugang Jiang, and Xiaojie Dang. "A Mixed Precision Direct Electromagnetic Finite Element Solver on GPUs." In 2024 International Applied Computational Electromagnetics Society Symposium (ACES-China). IEEE, 2024. http://dx.doi.org/10.1109/aces-china62474.2024.10699568.

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Gao, Bin. "Memristor Based Mixed-Precision Computation-in-Memory System." In 2023 International Conference on IC Design and Technology (ICICDT). IEEE, 2023. http://dx.doi.org/10.1109/icicdt59917.2023.10332328.

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Lam, Michael O., Jeffrey K. Hollingsworth, Bronis R. de Supinski, and Matthew P. Legendre. "Automatically adapting programs for mixed-precision floating-point computation." In the 27th international ACM conference. ACM Press, 2013. http://dx.doi.org/10.1145/2464996.2465018.

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Ren, Xuanzhengbo, Masatoshi Kawai, Tetsuya Hoshino, Takahiro Katagiri, and Toru Nagai. "Auto-tuning Mixed-precision Computation by Specifying Multiple Regions." In 2023 Eleventh International Symposium on Computing and Networking (CANDAR). IEEE, 2023. http://dx.doi.org/10.1109/candar60563.2023.00031.

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Lam, Michael O., Bronis R. de Supinksi, Matthew P. LeGendre, and Jeffrey K. Hollingsworth. "Abstract: Automatically Adapting Programs for Mixed-Precision Floating-Point Computation." In 2012 SC Companion: High Performance Computing, Networking, Storage and Analysis (SCC). IEEE, 2012. http://dx.doi.org/10.1109/sc.companion.2012.231.

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Lam, Michael O., Bronis R. de Supinksi, Matthew P. LeGendre, and Jeffrey K. Hollingsworth. "Poster: Automatically Adapting Programs for Mixed-Precision Floating-Point Computation." In 2012 SC Companion: High Performance Computing, Networking, Storage and Analysis (SCC). IEEE, 2012. http://dx.doi.org/10.1109/sc.companion.2012.232.

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Miret, Santiago, Vui Seng Chua, Mattias Marder, Mariano Phiellip, Nilesh Jain, and Somdeb Majumdar. "Neuroevolution-enhanced multi-objective optimization for mixed-precision quantization." In GECCO '22: Genetic and Evolutionary Computation Conference. ACM, 2022. http://dx.doi.org/10.1145/3512290.3528692.

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Abdelfattah, Ahmad, Stanimire Tomov, and Jack Dongarra. "Towards Half-Precision Computation for Complex Matrices: A Case Study for Mixed Precision Solvers on GPUs." In 2019 IEEE/ACM 10th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA). IEEE, 2019. http://dx.doi.org/10.1109/scala49573.2019.00008.

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Tang, Ray. "Use Mixed Precision Data Types to Speed up Computation for Ultrasound Imaging Software." In 2022 7th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2022. http://dx.doi.org/10.1109/iciibms55689.2022.9971490.

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