Results 61 to 70 of about 4,836 (230)
Portable PGAS‐Based GPU‐Accelerated Branch‐And‐Bound Algorithms at Scale
Concurrency and Computation: Practice and Experience, Volume 37, Issue 25-26, 30 November 2025.ABSTRACT The Branch‐and‐Bound (B&B) technique plays a key role in solving many combinatorial optimization problems, enabling efficient problem‐solving and decision‐making in a wide range of applications. It incrementally constructs a tree by building candidates to the solutions and abandoning a candidate as soon as it determines that it cannot lead to ...
Guillaume Helbecque +4 more
wiley +1 more source
CUDA and OpenCL implementations of Conways Game of Life cellular automata [PDF]
Компьютерные исследования и моделирование, 2010 In this article the experience of reading "CUDA and OpenCL programming" course during high perfomance computing summer school MIPT-2010 is analyzed. Content of lectures and practical tasks, as well as manner of presenting of the material are regarded ...
Andrey Evgen'evich Alekseenko +1 more
doaj +1 more source
Pydidas: a tool for automated X‐ray diffraction data analysis
Journal of Applied Crystallography, Volume 58, Issue 4, Page 1476-1485, August 2025.Pydidas is a new Python package for processing X‐ray diffraction data, offering a user‐friendly interface and versatile processing options. It includes a graphical user interface for the entire data processing pipeline and is intended to be easily accessible for non‐experts.The processing and analysis of X‐ray diffraction (XRD) data at synchrotrons is ...
Malte Storm +2 more
wiley +1 more source
Challenges of the Knowledge Society, 2012 Since the first idea of using GPU to general purpose computing, things have evolved over the years and now there are several approaches to GPU programming. GPU computing practically began with the introduction of CUDA (Compute Unified Device Architecture)
BOGDAN OANCEA +2 more
doaj
International Journal of Reconfigurable Computing, 2019 Solving diagonally dominant tridiagonal linear systems is a common problem in scientific high-performance computing (HPC). Furthermore, it is becoming more commonplace for HPC platforms to utilise a heterogeneous combination of computing devices.
Hamish J. Macintosh +2 more
doaj +1 more source
Leveraging SYCL for Heterogeneous cDTW Computation on CPU, GPU, and FPGA
Concurrency and Computation: Practice and Experience, Volume 37, Issue 15-17, 25 July 2025.ABSTRACT One of the most time‐consuming kernels of a recent epileptic seizure detection application is the computation of the constrained Dynamic Time Warping (cDTW) Distance Matrix. In this paper, we explore the design space of heterogeneous CPU, GPU, and FPGA implementations of this kernel using SYCL as a programming model. First, we optimize the CPU
Cristian Campos +3 more
wiley +1 more source
Research on OpenCL optimization for FPGA deep learning application.
PLoS ONE, 2019 In recent years, with the development of computer science, deep learning is held as competent enough to solve the problem of inference and learning in high dimensional space.
Shuo Zhang +4 more
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The IMAGE beamline at the KIT Light Source
Journal of Synchrotron Radiation, Volume 32, Issue 4, Page 1036-1051, July 2025.The superconducting wiggler beamline IMAGE at the KIT Light Source, dedicated to full‐field hard X‐ray imaging applications in materials and life sciences, with a focus on high‐throughput computed tomography, laminography experiments and systematic in situ and operando studies, is described.The superconducting wiggler beamline IMAGE at the KIT Light ...
Angelica Cecilia +14 more
wiley +1 more source
Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies
Concurrency and Computation: Practice and Experience, Volume 37, Issue 12-14, 25 June 2025.ABSTRACT Over the last ten years, graphics processors have become the de facto accelerator for data‐parallel tasks in various branches of high‐performance computing, including machine learning and computational sciences. However, with the recent introduction of AMD‐manufactured graphics processors to the world's fastest supercomputers, tuning ...
Johannes Pekkilä +3 more
wiley +1 more source
Automatic Optimization of the Computation Graph in the Nengo Neural Network Simulator
Frontiers in Neuroinformatics, 2017 One critical factor limiting the size of neural cognitive models is the time required to simulate such models. To reduce simulation time, specialized hardware is often used.
Jan Gosmann, Chris Eliasmith
doaj +1 more source