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Статті в журналах з теми "GPU code generation"
EMMART, NIALL, and CHARLES WEEMS. "SEARCH-BASED AUTOMATIC CODE GENERATION FOR MULTIPRECISION MODULAR EXPONENTIATION ON MULTIPLE GENERATIONS OF GPU." Parallel Processing Letters 23, no. 04 (December 2013): 1340009. http://dx.doi.org/10.1142/s0129626413400094.
Повний текст джерелаAfar Nazim, Allazov. "Automatic Generation of GPU Code in DVOR." University News. North-Caucasian Region. Technical Sciences Series, no. 3 (September 2015): 3–9. http://dx.doi.org/10.17213/0321-2653-2015-3-3-9.
Повний текст джерелаBlazewicz, Marek, Ian Hinder, David M. Koppelman, Steven R. Brandt, Milosz Ciznicki, Michal Kierzynka, Frank Löffler, Erik Schnetter, and Jian Tao. "From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation." Scientific Programming 21, no. 1-2 (2013): 1–16. http://dx.doi.org/10.1155/2013/167841.
Повний текст джерелаRodrigues, A. Wendell O., Frédéric Guyomarc'h, Jean-Luc Dekeyser, and Yvonnick Le Menach. "Automatic Multi-GPU Code Generation Applied to Simulation of Electrical Machines." IEEE Transactions on Magnetics 48, no. 2 (February 2012): 831–34. http://dx.doi.org/10.1109/tmag.2011.2179527.
Повний текст джерелаRawat, Prashant Singh, Miheer Vaidya, Aravind Sukumaran-Rajam, Mahesh Ravishankar, Vinod Grover, Atanas Rountev, Louis-Noel Pouchet, and P. Sadayappan. "Domain-Specific Optimization and Generation of High-Performance GPU Code for Stencil Computations." Proceedings of the IEEE 106, no. 11 (November 2018): 1902–20. http://dx.doi.org/10.1109/jproc.2018.2862896.
Повний текст джерелаBasu, Protonu, Samuel Williams, Brian Van Straalen, Leonid Oliker, Phillip Colella, and Mary Hall. "Compiler-based code generation and autotuning for geometric multigrid on GPU-accelerated supercomputers." Parallel Computing 64 (May 2017): 50–64. http://dx.doi.org/10.1016/j.parco.2017.04.002.
Повний текст джерелаKlöckner, Andreas, Nicolas Pinto, Yunsup Lee, Bryan Catanzaro, Paul Ivanov, and Ahmed Fasih. "PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation." Parallel Computing 38, no. 3 (March 2012): 157–74. http://dx.doi.org/10.1016/j.parco.2011.09.001.
Повний текст джерелаHagiescu, Andrei, Bing Liu, R. Ramanathan, Sucheendra K. Palaniappan, Zheng Cui, Bipasa Chattopadhyay, P. S. Thiagarajan, and Weng-Fai Wong. "GPU code generation for ODE-based applications with phased shared-data access patterns." ACM Transactions on Architecture and Code Optimization 10, no. 4 (December 2013): 1–19. http://dx.doi.org/10.1145/2541228.2555311.
Повний текст джерелаHolzer, Markus, Martin Bauer, Harald Köstler, and Ulrich Rüde. "Highly efficient lattice Boltzmann multiphase simulations of immiscible fluids at high-density ratios on CPUs and GPUs through code generation." International Journal of High Performance Computing Applications 35, no. 4 (May 13, 2021): 413–27. http://dx.doi.org/10.1177/10943420211016525.
Повний текст джерелаWalsh, Stuart D. C., and Martin O. Saar. "Developing Extensible Lattice-Boltzmann Simulators for General-Purpose Graphics-Processing Units." Communications in Computational Physics 13, no. 3 (March 2013): 867–79. http://dx.doi.org/10.4208/cicp.351011.260112s.
Повний текст джерелаДисертації з теми "GPU code generation"
Holewinski, Justin A. "Automatic Code Generation for Stencil Computations on GPU Architectures." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1354545992.
Повний текст джерелаBeaugnon, Ulysse. "Efficient code generation for hardware accelerators by refining partially specified implementation." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE050.
Повний текст джерелаCompilers looking for an efficient implementation of a function must find which optimizations are the most beneficial. This is a complex problem, especially in the early steps of the compilation process. Each decision may impact the transformations available in subsequent steps. We propose to represent the compilation process as the progressive refinement of a partially specified implementation. All potential decisions are exposed upfront and commute. This allows for making the most discriminative decisions first and for building a performance model aware of which optimizations may be applied in subsequent steps. We apply this approach to the generation of efficient GPU code for linear algebra and yield performance competitive with hand-tuned libraries
Membarth, Richard [Verfasser]. "Code Generation for GPU Accelerators from a Domain-Specific Language for Medical Imaging / Richard Membarth." München : Verlag Dr. Hut, 2013. http://d-nb.info/1037287142/34.
Повний текст джерелаMueller-Roemer, Johannes Sebastian Verfasser], Dieter W. [Akademischer Betreuer] Fellner, André [Akademischer Betreuer] [Stork, and Heinrich [Akademischer Betreuer] Müller. "GPU Data Structures and Code Generation for Modeling, Simulation, and Visualization / Johannes Sebastian Mueller-Roemer ; Dieter W. Fellner, André Stork, Heinrich Müller." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://d-nb.info/1204200823/34.
Повний текст джерелаMueller-Roemer, Johannes Sebastian [Verfasser], Dieter W. [Akademischer Betreuer] Fellner, André [Akademischer Betreuer] Stork, and Heinrich [Akademischer Betreuer] Müller. "GPU Data Structures and Code Generation for Modeling, Simulation, and Visualization / Johannes Sebastian Mueller-Roemer ; Dieter W. Fellner, André Stork, Heinrich Müller." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://d-nb.info/1204200823/34.
Повний текст джерелаShanmugam, Sakthivadivel Saravanakumar. "Fast-NetMF: Graph Embedding Generation on Single GPU and Multi-core CPUs with NetMF." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1557162076041442.
Повний текст джерелаZhengxuan, Zhang, Kou Yanhong, and Zhang Qishan. "DESIGN OF A SOFTWARE RADIO GPS RECEIVER." International Foundation for Telemetering, 2005. http://hdl.handle.net/10150/605032.
Повний текст джерелаThe GPS receiver based on software radio technology is a kind of general purpose GPS signal processing platform which makes use of advanced design ideas and advanced design tools nowadays. We used FPGA device and lots of necessary peripherals such as DSP and PCI controller in our design to promote flexibility and practicability effectively. Various fast acquisition means and accurate tracking algorithms could be realized, improved and validated on this platform, besides basic GPS receiver function.
Kim, Jinsung. "Optimizing Tensor Contractions on GPUs." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563237825735994.
Повний текст джерелаMasliah, Ian. "Méthodes de génération automatique de code appliquées à l’algèbre linéaire numérique dans le calcul haute performance." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS285/document.
Повний текст джерелаParallelism in today's computer architectures is ubiquitous whether it be in supercomputers, workstations or on portable devices such as smartphones. Exploiting efficiently these systems for a specific application requires a multidisciplinary effort that concerns Domain Specific Languages (DSL), code generation and optimization techniques and application-specific numerical algorithms. In this PhD thesis, we present a method of high level programming that takes into account the features of heterogenous architectures and the properties of matrices to build a generic dense linear algebra solver. Our programming model supports both implicit or explicit data transfers to and from General-Purpose Graphics Processing Units (GPGPU) and Integrated Graphic Processors (IGPs). As GPUs have become an asset in high performance computing, incorporating their use in general solvers is an important issue. Recent architectures such as IGPs also require further knowledge to program them efficiently. Our methodology aims at simplifying the development on parallel architectures through the use of high level programming techniques. As an example, we developed a least-squares solver based on semi-normal equations in mixed precision that cannot be found in current libraries. This solver achieves similar performance as other mixed-precision algorithms. We extend our approach to a new multistage programming model that alleviates the interoperability problems between the CPU and GPU programming models. Our multistage approach is used to automatically generate GPU code for CPU-based element-wise expressions and parallel skeletons while allowing for type-safe program generation. We illustrate that this work can be applied to recent architectures and algorithms. The resulting code has been incorporated into a C++ library called NT2. Finally, we investigate how to apply high level programming techniques to batched computations and tensor contractions. We start by explaining how to design a simple data container using modern C++14 programming techniques. Then, we study the issues around batched computations, memory locality and code vectorization to implement a highly optimized matrix-matrix product for small sizes using SIMD instructions. By combining a high level programming approach and advanced parallel programming techniques, we show that we can outperform state of the art numerical libraries
Mueller-Roemer, Johannes Sebastian. "GPU Data Structures and Code Generation for Modeling, Simulation, and Visualization." Phd thesis, 2020. https://tuprints.ulb.tu-darmstadt.de/11291/1/dissertation-2019-12-20.pdf.
Повний текст джерелаЧастини книг з теми "GPU code generation"
Konstantinidis, Athanasios, Paul H. J. Kelly, J. Ramanujam, and P. Sadayappan. "Parametric GPU Code Generation for Affine Loop Programs." In Languages and Compilers for Parallel Computing, 136–51. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09967-5_8.
Повний текст джерелаTrevisan Jost, Tiago, Arun Thangamani, Raphaël Colin, Vincent Loechner, Stéphane Genaud, and Bérenger Bramas. "GPU Code Generation of Cardiac Electrophysiology Simulation with MLIR." In Euro-Par 2023: Parallel Processing, 549–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39698-4_37.
Повний текст джерелаHu, Weifang, Lin Han, Pu Han, and Jiandong Shang. "Automatic Thread Block Size Selection Strategy in GPU Parallel Code Generation." In Parallel Architectures, Algorithms and Programming, 390–404. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0010-4_34.
Повний текст джерелаMembarth, Richard, Anton Lokhmotov, and Jürgen Teich. "Generating GPU Code from a High-Level Representation for Image Processing Kernels." In Euro-Par 2011: Parallel Processing Workshops, 270–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29737-3_31.
Повний текст джерелаShashidhar, G., and Rupesh Nasre. "LightHouse: An Automatic Code Generator for Graph Algorithms on GPUs." In Languages and Compilers for Parallel Computing, 235–49. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52709-3_18.
Повний текст джерелаSosa, J., Tomás Bautista, Daniel Alcaraz, S. García-Alonso, and Juan A. Montiel-Nelson. "Generation of New Detection Codes for GPS Satellites Using NSGA-II." In Computational Methods in Applied Sciences, 511–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11541-2_34.
Повний текст джерелаKlöckner, Andreas, Nicolas Pinto, Bryan Catanzaro, Yunsup Lee, Paul Ivanov, and Ahmed Fasih. "GPU Scripting and Code Generation with PyCUDA." In GPU Computing Gems Jade Edition, 373–85. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-12-385963-1.00027-7.
Повний текст джерелаEastman, Peter, and Vijay Pande. "Accelerating Development and Execution Speed with Just-in-Time GPU Code Generation." In GPU Computing Gems Jade Edition, 399–407. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-12-385963-1.00029-0.
Повний текст джерелаHolm, Håvard H., André R. Brodtkorb, and Martin L. Sætra. "Performance and Energy Efficiency of CUDA and OpenCL for GPU Computing Using Python." In Parallel Computing: Technology Trends. IOS Press, 2020. http://dx.doi.org/10.3233/apc200089.
Повний текст джерелаRockenbach, Dinei A., Dalvan Griebler, Marco Danelutto, and Luiz G. Fernandes. "High-Level Stream Parallelism Abstractions with SPar Targeting GPUs." In Parallel Computing: Technology Trends. IOS Press, 2020. http://dx.doi.org/10.3233/apc200083.
Повний текст джерелаТези доповідей конференцій з теми "GPU code generation"
Zhou, Keren, Xiaozhu Meng, Ryuichi Sai, and John Mellor-Crummey. "GPA: A GPU Performance Advisor Based on Instruction Sampling." In 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2021. http://dx.doi.org/10.1109/cgo51591.2021.9370339.
Повний текст джерелаBuck, Ian. "GPU Computing: Programming a Massively Parallel Processor." In International Symposium on Code Generation and Optimization (CGO'07). IEEE, 2007. http://dx.doi.org/10.1109/cgo.2007.13.
Повний текст джерелаElmqvist, Hilding, Hans Olsson, Axel Goteman, Vilhelm Roxling, Dirk Zimmer, and Alexander Pollok. "Automatic GPU Code Generation of Modelica Functions." In The 11th International Modelica Conference. Linköping University Electronic Press, 2015. http://dx.doi.org/10.3384/ecp15118235.
Повний текст джерелаLi, Ao, Bojian Zheng, Gennady Pekhimenko, and Fan Long. "Automatic Horizontal Fusion for GPU Kernels." In 2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2022. http://dx.doi.org/10.1109/cgo53902.2022.9741270.
Повний текст джерелаMishra, Alok, Martin Kong, and Barbara Chapman. "Kernel Fusion/Decomposition for Automatic GPU-Offloading." In 2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2019. http://dx.doi.org/10.1109/cgo.2019.8661188.
Повний текст джерелаRemmelg, Toomas, Thibaut Lutz, Michel Steuwer, and Christophe Dubach. "Performance portable GPU code generation for matrix multiplication." In PPoPP '16: 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2884045.2884046.
Повний текст джерелаMotta, Paulo. "Declaring Lua data types for GPU code generation." In SPLASH '17: Conference on Systems, Programming, Languages, and Applications: Software for Humanity. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3141865.3142466.
Повний текст джерелаVießmann, Hans-Nikolai, and Sven-Bodo Scholz. "Effective Host-GPU Memory Management Through Code Generation." In IFL 2020: 32nd Symposium on Implementation and Application of Functional Languages. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3462172.3462199.
Повний текст джерелаKatel, Navdeep, Vivek Khandelwal, and Uday Bondhugula. "MLIR-based code generation for GPU tensor cores." In CC '22: 31st ACM SIGPLAN International Conference on Compiler Construction. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3497776.3517770.
Повний текст джерелаBrahmakshatriya, Ajay, Yunming Zhang, Changwan Hong, Shoaib Kamil, Julian Shun, and Saman Amarasinghe. "Compiling Graph Applications for GPU s with GraphIt." In 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2021. http://dx.doi.org/10.1109/cgo51591.2021.9370321.
Повний текст джерелаЗвіти організацій з теми "GPU code generation"
Berney, Ernest, Jami Lynn Daugherty, and Lulu Edwards. Validation of the automatic dynamic cone penetrometer. Engineer Research and Development Center (U.S.), July 2022. http://dx.doi.org/10.21079/11681/44704.
Повний текст джерела