high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla K20

High-Speed GPU-Based Fully Three-Dimensional Diffuse Optical Tomographic System

Manob Jyoti Saikia, Rajan Kanhirodan, Ram Mohan Vasu

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Tags: CUDA, FEM, Finite element method, Image processing, Medicine, nVidia, Tesla K20, Tomography

June 6, 2014 by hgpu

Performance models for CPU-GPU data transfers

B. van Werkhoven, J. Maassen, F.J. Seinstra, H.E. Bal

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Tags: Computer science, CUDA, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, PCIe, Performance, Performance prediction, Tesla K20

June 5, 2014 by bennotsi

Integrated Modelling of Hydrodynamic Processes, Faecal Indicator Organisms and Related Parameters with Improved Accuracy using Parallel (GPU) Computing

Brian Adjetey Boye

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Tags: CUDA, Earth and Space Sciences, nVidia, nVidia GeForce GTX 590, Tesla K10, Tesla K20, Thesis

June 2, 2014 by hgpu

Multi-target DPA attacks: Pushing DPA beyond the limits of a desktop computer

Luke Mather, Elisabeth Oswald, Carolyn Whitnall

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Tags: AMD Radeon R9 290, ATI, Computational Complexity, Computer science, nVidia, nVidia GeForce GTX Titan, OpenCL, Security, Tesla K20

June 2, 2014 by hgpu

Efficient Implementation of Hyperspectral Anomaly Detection Techniques on GPUs and Multicore Processors

Jose M. Molero, Ester M. Garzon, Inmaculada Garcia, Enrique S. Quintana-Orti, Antonio Plaza

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Tags: Algorithms, CUDA, Data acquisition, Hyperspectral imaging, Image processing, nVidia, Tesla K20

June 1, 2014 by hgpu

A Performance Model for the Communication in Fast Multipole Methods on HPC Platforms

Huda Ibeid, Rio Yokota, David Keyes

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Tags: Algorithms, Astrophysics, Computer science, CUDA, Fast multipole method, Molecular dynamics, N-body simulation, nVidia, Physics, Tesla K20

June 1, 2014 by hgpu

Evaluating GPU Passthrough in Xen for High Performance Cloud Computing

Andrew J. Younge, John Paul Walters, Stephen Crago, Geoffrey C. Fox

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Tags: Cloud, Computer science, CUDA, nVidia, Tesla C2075, Tesla K20, Virtualization

June 1, 2014 by hgpu

Massive exploration of perturbed conditions of the blood coagulation cascade through GPU parallelization

Paolo Cazzaniga, Marco S. Nobile, Daniela Besozzi, Matteo Bellini, Giancarlo Mauri

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Tags: Bioinformatics, Biology, Computational biology, CUDA, Differential equations, nVidia, ODEs, Ordinary differential equations, Tesla K20

May 30, 2014 by hgpu

Performance Evaluation of Quicksort with GPU Dynamic Parallelism for Gene-Expression Quantile Normalization

Roberto Pinto Souto, Carla Osthoff, Douglas Augusto, Oswaldo Trelles, Ana Tereza Ribeiro de Vasconcelos

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, Sorting, Tesla K20

May 29, 2014 by hgpu

GPU Passthrough Performance: A Comparison of KVM, Xen, VMWare ESXi, and LXC for CUDA and OpenCL Applications

John Paul Walters, Andrew J. Younge, Dong-In Kang, Ke-Thia Yao, Mikyung Kang, Stephen P. Crago, Geoffrey C. Fox

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Tags: Cloud, Computer science, CUDA, nVidia, OpenCL, Tesla C2075, Tesla K20, Virtualization

May 26, 2014 by hgpu

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Andrea Manconi, Alessandro Orro, Emanuele Manca, Giuliano Armano, Luciano Milanesi

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Source codes

Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 480, Package, Python, Sequence alignment, Tesla K10, Tesla K20

May 25, 2014 by hgpu

Graphic Processing Unit Simulation of Axon Growth and Guidance through Cue Diffusion on Massively Parallel Processors

Ana M. Mihut, Graham Morgan, Marcus Kaiser

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Tags: Biology, CUDA, GLSL, Neuroscience, nVidia, nVidia GeForce GTX 460, nVidia Quadro 600, OpenGL, Programming techniques, Tesla K20

May 24, 2014 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

High-Speed GPU-Based Fully Three-Dimensional Diffuse Optical Tomographic System

Performance models for CPU-GPU data transfers

Integrated Modelling of Hydrodynamic Processes, Faecal Indicator Organisms and Related Parameters with Improved Accuracy using Parallel (GPU) Computing

Multi-target DPA attacks: Pushing DPA beyond the limits of a desktop computer

Efficient Implementation of Hyperspectral Anomaly Detection Techniques on GPUs and Multicore Processors

A Performance Model for the Communication in Fast Multipole Methods on HPC Platforms

Massive exploration of perturbed conditions of the blood coagulation cascade through GPU parallelization

Performance Evaluation of Quicksort with GPU Dynamic Parallelism for Gene-Expression Quantile Normalization

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Graphic Processing Unit Simulation of Axon Growth and Guidance through Cue Diffusion on Massively Parallel Processors

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)