Search Results - GPU programming in Python

Efficient Multi-GPU Programming in Python: Reducing Synchronization and Access Overheads by Oden, Lena, Nolp, Klaus

“… to the synchronization requirements between GPU kernels and communication libraries. In this work, we present a detailed performance analysis of multi-GPU programming in Python using CuPy, Numba, NCCL and mpi4py…”
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Comparative study of CUDA-based parallel programming in C and Python for GPU acceleration of the 4th order Runge-Kutta method by Fernandes, Davi F., Santos, Marcelo C., Silva, Adilson C., Lima, Alan M.M.

“…•Point reactor kinetics equations.•4th Order Runge-Kutta method.•Acceleration by GPU.•Python and C codes.•Speedup calculations…”
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GPU Programming with Python by Zaccone, Giancarlo

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Efficiency and accuracy of GPU-parallelized Fourier spectral methods for solving phase-field models by Boccardo, A.D., Tong, M., Leen, S.B., Tourret, D., Segurado, J.

“…, implemented in Python programming language and parallelized on a graphics processing unit (GPU), for solving a phase-field model coupling Cahn…”
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OpenDust: A fast GPU-accelerated code for the calculation of forces acting on microparticles in a plasma flow by Kolotinskii, D., Timofeev, A.

“…We present the first open-source, GPU-based code for complex plasmas. The code, OpenDust, pursues to provide researchers, both experimenters and theorists…”
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GPU-aware Communication with UCX in Parallel Programming Models: Charm++, MPI, and Python by Choi, Jaemin, Fink, Zane, White, Sam, Bhat, Nitin, Richards, David F., Kale, Laxmikant V.

“…-performance computing. For developers of parallel programming models, implementing support for GPU-aware communication using native APIs for GPUs such as CUDA can be a daunting task as it requires considerable effort…”
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Developing Graphics Frameworks with Python and OpenGL by Stemkoski, Lee, Pascale, Michael

“… You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language…”
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Sailfish: A flexible multi-GPU implementation of the lattice Boltzmann method by Januszewski, M., Kostur, M.

“…) on modern Graphics Processing Units (GPUs) using CUDA/OpenCL. We take a novel approach to GPU code implementation and use run-time code generation techniques and a high level programming language (Python…”
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Going faster to see further: graphics processing unit-accelerated value iteration and simulation for perishable inventory control using JAX by Farrington, Joseph, Wong, Wai Keong, Li, Kezhi, Utley, Martin

“… The adoption of GPU-accelerated approaches has been limited in operational research relative to other fields like machine learning, in which new software frameworks have made GPU programming widely accessible…”
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CuGBasis: High-performance CUDA/Python library for efficient computation of quantum chemistry density-based descriptors for larger systems by Tehrani, Alireza, Richer, Michelle, Heidar-Zadeh, Farnaz

“… CuGBasis integrates high-performance Graphical Processing Unit (GPU) computing with the ease and flexibility of Python programming, making it compatible with a vast ecosystem of libraries…”
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gpuRIR: A python library for room impulse response simulation with GPU acceleration by Diaz-Guerra, David, Miguel, Antonio, Beltran, Jose R.

“…The Image Source Method (ISM) is one of the most employed techniques to calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity…”
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Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration by Park, Chanwook, Lu, Ye, Saha, Sourav, Xue, Tianju, Guo, Jiachen, Mojumder, Satyajit, Apley, Daniel W., Wagner, Gregory J., Liu, Wing Kam

“…) programming using JAX library in Python. Instead of increasing the degrees of freedom like higher order FEM, C-HiDeNN takes advantage of neighboring elements to construct the so-called convolution patch functions…”
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Performance analysis of GPU accelerated meshfree q-LSKUM solvers in Fortran, C, Python, and Julia by Mamidi, Nischay Ram, Saxena, Dhruv, Prasun, Kumar, Nemili, Anil, Sharma, Bharatkumar, Deshpande, S. M.

“…This paper presents a comprehensive analysis of the performance of Fortran, C, Python, and Julia based GPU accelerated meshfree solvers for compressible flows…”
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MCR toolkit: A GPU‐based toolkit for multi‐channel reconstruction of preclinical and clinical x‐ray CT data by Clark, Darin P., Badea, Cristian T.

“…Background The advancement of x‐ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new…”
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NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs by Di Domenico, Daniel, Lima, João V. F., Cavalheiro, Gerson G. H.

“…Compiled low-level languages, such as C/C++ and Fortran, have been employed as programming tools to implement applications to explore GPU devices…”
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Fast, Cheap, and Turbulent—Global Ocean Modeling With GPU Acceleration in Python by Häfner, Dion, Nuterman, Roman, Jochum, Markus

“… Our ocean model Veros takes a different approach: it is implemented using the high‐level programming language Python…”
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Lessons learned from comparing C-CUDA and Python-Numba for GPU-Computing by Oden, Lena

“…Python as programming language is increasingly gaining importance, especially in data science, scientific, and parallel programming…”
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The fast azimuthal integration Python library: pyFAI by Ashiotis, Giannis, Deschildre, Aurore, Nawaz, Zubair, Wright, Jonathan P., Karkoulis, Dimitrios, Picca, Frédéric Emmanuel, Kieffer, Jérôme

“… It is written in Python (with binary submodules for improved performance), a language widely accepted and used by the scientific community today, which enables users to easily incorporate the pyFAI library into their processing pipeline…”
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Python Parallel Programming Cookbook by Zaccone, Giancarlo

“… You'll explore topics such as threading, multiprocessing, asynchronous programming, and GPU utilization, all through practical Python…”
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Optimized Python library for reconstruction of ensemble-based gene co-expression networks using multi-GPU by López-Fernández, Aurelio, Gómez-Vela, Francisco A., del Saz-Navarro, María, Delgado-Chaves, Fernando M., Rodríguez-Baena, Domingo S.

“… This paper presents pyEnGNet, a Python library that can generate gene co-expression networks in High-performance computing environments…”
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