high performance computing on graphics processing units: hgpu.org

Lukas Mazur, Dennis Bollweg, David A. Clarke, Luis Altenkort, Olaf Kaczmarek, Rasmus Larsen, Hai-Tao Shu, Jishnu Goswami, Philipp Scior, Hauke Sandmeyer, Marius Neumann, Henrik Dick, Sajid Ali, Jangho Kim, Christian Schmidt, Peter Petreczky, Swagato Mukherjee

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Tags: Algorithms, AMD Radeon Instinct MI250X, ATI, CUDA, High Energy Physics - Lattice, HIP, MPI, nVidia, nVidia A100, Package, Physics, QCD

June 11, 2023 by hgpu

GPU-Acceleration of Tensor Renormalization with PyTorch using CUDA

Raghav G. Jha, Abhishek Samlodia

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Tags: CUDA, High Energy Physics - Lattice, nVidia, nVidia GeForce RTX 2080 Ti, Physics, PyTorch

June 4, 2023 by hgpu

A High-Performance Computing Cluster for Distributed Deep Learning: A Practical Case of Weed Classification Using Convolutional Neural Network Models

Manuel López-Martínez, Germán Díaz-Flórez, Santiago Villagrana-Barraza, Luis O. Solís-Sánchez, Héctor A. Guerrero-Osuna, Genaro M. Soto-Zarazúa, Carlos A. Olvera-Olvera

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Tags: CUDA, Deep learning, Distributed computing, HPC, Image processing, Neural networks, nVidia, nVidia GeForce GTX 1050 Ti

June 4, 2023 by hgpu

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high performance computing on graphics processing units: hgpu.org

GPU First – Execution of Legacy CPU Codes on GPUs

ACC Saturator: Automatic Kernel Optimization for Directive-Based GPU Code

Improving Performance of Iterative Applications through Interleaved Execution of Approximated CUDA Kernels

Reducing branch divergence to speed up parallel execution of unit testing on GPUs

Efficient GPU implementation of a class of array permutations

cuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications

EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation

GPUHarbor: Testing GPU Memory Consistency at Large

Program Analysis and Machine Learning based Approach to Predict Power Consumption of CUDA Kernel

SIMULATeQCD: A simple multi-GPU lattice code for QCD calculations

GPU-Acceleration of Tensor Renormalization with PyTorch using CUDA

A High-Performance Computing Cluster for Distributed Deep Learning: A Practical Case of Weed Classification Using Convolutional Neural Network Models

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