Literatura académica sobre el tema "GPU code generation"
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Artículos de revistas sobre el tema "GPU code generation"
EMMART, NIALL y CHARLES WEEMS. "SEARCH-BASED AUTOMATIC CODE GENERATION FOR MULTIPRECISION MODULAR EXPONENTIATION ON MULTIPLE GENERATIONS OF GPU". Parallel Processing Letters 23, n.º 04 (diciembre de 2013): 1340009. http://dx.doi.org/10.1142/s0129626413400094.
Texto completoAfar Nazim, Allazov. "Automatic Generation of GPU Code in DVOR". University News. North-Caucasian Region. Technical Sciences Series, n.º 3 (septiembre de 2015): 3–9. http://dx.doi.org/10.17213/0321-2653-2015-3-3-9.
Texto completoBlazewicz, Marek, Ian Hinder, David M. Koppelman, Steven R. Brandt, Milosz Ciznicki, Michal Kierzynka, Frank Löffler, Erik Schnetter y Jian Tao. "From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation". Scientific Programming 21, n.º 1-2 (2013): 1–16. http://dx.doi.org/10.1155/2013/167841.
Texto completoRodrigues, A. Wendell O., Frédéric Guyomarc'h, Jean-Luc Dekeyser y Yvonnick Le Menach. "Automatic Multi-GPU Code Generation Applied to Simulation of Electrical Machines". IEEE Transactions on Magnetics 48, n.º 2 (febrero de 2012): 831–34. http://dx.doi.org/10.1109/tmag.2011.2179527.
Texto completoRawat, Prashant Singh, Miheer Vaidya, Aravind Sukumaran-Rajam, Mahesh Ravishankar, Vinod Grover, Atanas Rountev, Louis-Noel Pouchet y P. Sadayappan. "Domain-Specific Optimization and Generation of High-Performance GPU Code for Stencil Computations". Proceedings of the IEEE 106, n.º 11 (noviembre de 2018): 1902–20. http://dx.doi.org/10.1109/jproc.2018.2862896.
Texto completoBasu, Protonu, Samuel Williams, Brian Van Straalen, Leonid Oliker, Phillip Colella y Mary Hall. "Compiler-based code generation and autotuning for geometric multigrid on GPU-accelerated supercomputers". Parallel Computing 64 (mayo de 2017): 50–64. http://dx.doi.org/10.1016/j.parco.2017.04.002.
Texto completoKlöckner, Andreas, Nicolas Pinto, Yunsup Lee, Bryan Catanzaro, Paul Ivanov y Ahmed Fasih. "PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation". Parallel Computing 38, n.º 3 (marzo de 2012): 157–74. http://dx.doi.org/10.1016/j.parco.2011.09.001.
Texto completoHagiescu, Andrei, Bing Liu, R. Ramanathan, Sucheendra K. Palaniappan, Zheng Cui, Bipasa Chattopadhyay, P. S. Thiagarajan y Weng-Fai Wong. "GPU code generation for ODE-based applications with phased shared-data access patterns". ACM Transactions on Architecture and Code Optimization 10, n.º 4 (diciembre de 2013): 1–19. http://dx.doi.org/10.1145/2541228.2555311.
Texto completoHolzer, Markus, Martin Bauer, Harald Köstler y 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, n.º 4 (13 de mayo de 2021): 413–27. http://dx.doi.org/10.1177/10943420211016525.
Texto completoWalsh, Stuart D. C. y Martin O. Saar. "Developing Extensible Lattice-Boltzmann Simulators for General-Purpose Graphics-Processing Units". Communications in Computational Physics 13, n.º 3 (marzo de 2013): 867–79. http://dx.doi.org/10.4208/cicp.351011.260112s.
Texto completoTesis sobre el tema "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.
Texto completoBeaugnon, Ulysse. "Efficient code generation for hardware accelerators by refining partially specified implementation". Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE050.
Texto completoCompilers 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.
Texto completoMueller-Roemer, Johannes Sebastian Verfasser], Dieter W. [Akademischer Betreuer] Fellner, André [Akademischer Betreuer] [Stork y 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.
Texto completoMueller-Roemer, Johannes Sebastian [Verfasser], Dieter W. [Akademischer Betreuer] Fellner, André [Akademischer Betreuer] Stork y 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.
Texto completoShanmugam, 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.
Texto completoZhengxuan, Zhang, Kou Yanhong y Zhang Qishan. "DESIGN OF A SOFTWARE RADIO GPS RECEIVER". International Foundation for Telemetering, 2005. http://hdl.handle.net/10150/605032.
Texto completoThe 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.
Texto completoMasliah, 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.
Texto completoParallelism 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.
Texto completoCapítulos de libros sobre el tema "GPU code generation"
Konstantinidis, Athanasios, Paul H. J. Kelly, J. Ramanujam y P. Sadayappan. "Parametric GPU Code Generation for Affine Loop Programs". En 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.
Texto completoTrevisan Jost, Tiago, Arun Thangamani, Raphaël Colin, Vincent Loechner, Stéphane Genaud y Bérenger Bramas. "GPU Code Generation of Cardiac Electrophysiology Simulation with MLIR". En Euro-Par 2023: Parallel Processing, 549–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39698-4_37.
Texto completoHu, Weifang, Lin Han, Pu Han y Jiandong Shang. "Automatic Thread Block Size Selection Strategy in GPU Parallel Code Generation". En Parallel Architectures, Algorithms and Programming, 390–404. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0010-4_34.
Texto completoMembarth, Richard, Anton Lokhmotov y Jürgen Teich. "Generating GPU Code from a High-Level Representation for Image Processing Kernels". En 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.
Texto completoShashidhar, G. y Rupesh Nasre. "LightHouse: An Automatic Code Generator for Graph Algorithms on GPUs". En 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.
Texto completoSosa, J., Tomás Bautista, Daniel Alcaraz, S. García-Alonso y Juan A. Montiel-Nelson. "Generation of New Detection Codes for GPS Satellites Using NSGA-II". En Computational Methods in Applied Sciences, 511–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11541-2_34.
Texto completoKlöckner, Andreas, Nicolas Pinto, Bryan Catanzaro, Yunsup Lee, Paul Ivanov y Ahmed Fasih. "GPU Scripting and Code Generation with PyCUDA". En GPU Computing Gems Jade Edition, 373–85. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-12-385963-1.00027-7.
Texto completoEastman, Peter y Vijay Pande. "Accelerating Development and Execution Speed with Just-in-Time GPU Code Generation". En GPU Computing Gems Jade Edition, 399–407. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-12-385963-1.00029-0.
Texto completoHolm, Håvard H., André R. Brodtkorb y Martin L. Sætra. "Performance and Energy Efficiency of CUDA and OpenCL for GPU Computing Using Python". En Parallel Computing: Technology Trends. IOS Press, 2020. http://dx.doi.org/10.3233/apc200089.
Texto completoRockenbach, Dinei A., Dalvan Griebler, Marco Danelutto y Luiz G. Fernandes. "High-Level Stream Parallelism Abstractions with SPar Targeting GPUs". En Parallel Computing: Technology Trends. IOS Press, 2020. http://dx.doi.org/10.3233/apc200083.
Texto completoActas de conferencias sobre el tema "GPU code generation"
Zhou, Keren, Xiaozhu Meng, Ryuichi Sai y John Mellor-Crummey. "GPA: A GPU Performance Advisor Based on Instruction Sampling". En 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2021. http://dx.doi.org/10.1109/cgo51591.2021.9370339.
Texto completoBuck, Ian. "GPU Computing: Programming a Massively Parallel Processor". En International Symposium on Code Generation and Optimization (CGO'07). IEEE, 2007. http://dx.doi.org/10.1109/cgo.2007.13.
Texto completoElmqvist, Hilding, Hans Olsson, Axel Goteman, Vilhelm Roxling, Dirk Zimmer y Alexander Pollok. "Automatic GPU Code Generation of Modelica Functions". En The 11th International Modelica Conference. Linköping University Electronic Press, 2015. http://dx.doi.org/10.3384/ecp15118235.
Texto completoLi, Ao, Bojian Zheng, Gennady Pekhimenko y Fan Long. "Automatic Horizontal Fusion for GPU Kernels". En 2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2022. http://dx.doi.org/10.1109/cgo53902.2022.9741270.
Texto completoMishra, Alok, Martin Kong y Barbara Chapman. "Kernel Fusion/Decomposition for Automatic GPU-Offloading". En 2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2019. http://dx.doi.org/10.1109/cgo.2019.8661188.
Texto completoRemmelg, Toomas, Thibaut Lutz, Michel Steuwer y Christophe Dubach. "Performance portable GPU code generation for matrix multiplication". En 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.
Texto completoMotta, Paulo. "Declaring Lua data types for GPU code generation". En 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.
Texto completoVießmann, Hans-Nikolai y Sven-Bodo Scholz. "Effective Host-GPU Memory Management Through Code Generation". En 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.
Texto completoKatel, Navdeep, Vivek Khandelwal y Uday Bondhugula. "MLIR-based code generation for GPU tensor cores". En CC '22: 31st ACM SIGPLAN International Conference on Compiler Construction. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3497776.3517770.
Texto completoBrahmakshatriya, Ajay, Yunming Zhang, Changwan Hong, Shoaib Kamil, Julian Shun y Saman Amarasinghe. "Compiling Graph Applications for GPU s with GraphIt". En 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2021. http://dx.doi.org/10.1109/cgo51591.2021.9370321.
Texto completoInformes sobre el tema "GPU code generation"
Berney, Ernest, Jami Lynn Daugherty y Lulu Edwards. Validation of the automatic dynamic cone penetrometer. Engineer Research and Development Center (U.S.), julio de 2022. http://dx.doi.org/10.21079/11681/44704.
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