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Numerical Accuracy Differences in CPU and GPGPU Codes

Devon Yablonski

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Tags: Computer science, CUDA, Floating point error, nVidia, Tesla C1060, Thesis

September 25, 2011 by hgpu

GPU-Based Acceleration of the MLEM Algorithm for SPECT Parallel Imaging with Attenuation Correction and Compensation for Detector Response

Akos Szlavecz, Gabor Hesz, Tamas Bukki, Bela Kari, Balazs Benyo

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Tags: Algorithms, Computed tomography, CT, CUDA, Medicine, nVidia, nVidia GeForce GTX 480, Tomography, Visualization

September 25, 2011 by hgpu

Algorithm Acceleration from GPGPUs for the ATLAS Upgrade

P.J. Clark, C. Jones, D. Emeilyanov, M. Rovatsou, A. Washbrook

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Tags: Algorithms, CUDA, High Energy Physics - Experiment, nVidia, Physics, Tesla C1060, Tesla C2050

September 25, 2011 by hgpu

Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

Keun Soo Yim, Cuong Pham, Mushfiq Saleheen, Zbigniew Kalbarczyk, Ravishankar Iyer

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Tags: Computer science, CUDA, Error recovery, Fault tolerance, nVidia, Tesla S1070

September 25, 2011 by hgpu

Automatic Translation of CUDA to OpenCL and Comparison of Performance Optimizations on GPUs

Deepthi Nandakumar

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Thesis

September 24, 2011 by hgpu

CU2CL: A CUDA-to-OpenCL Translator for Multi-and Many-core Architectures

Gabriel Martinez, Wu-chun Feng, Mark Gardner

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Code generation, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 280, OpenCL

September 24, 2011 by hgpu

Improving SIMT Efficiency of Global Rendering Algorithms with Architectural Support for Dynamic Micro-Kernels

Michael Steffen, Joseph Zambreno

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia Quadro FX 5800, Performance, PTX, Rendering

September 23, 2011 by hgpu

Accelerating reaction-diffusion simulations with general-purpose graphics processing units

Matthias Vigelius, Aidan Lane, Bernd Meyer

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, Package, Rendering, Stochastic simulation

September 23, 2011 by hgpu

Parallel processing on NVIDIA graphics processing units using CUDA

Erik Wynters

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Tags: Computer science, CUDA, Education, nVidia, nVidia GeForce 9800 GT, Tutorial

September 23, 2011 by hgpu

Simple optimizations for an applicative array language for graphics processors

Bradford Larsen

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, Optimization, Programming Languages, Programming techniques

September 23, 2011 by hgpu

Compact data structure and scalable algorithms for the sparse grid technique

Alin Murarasu, Josef Weidendorfer, Gerrit Buse, Daniel Butnaru, Dirk Pfluger

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Tags: Algorithms, Compression, Computer science, CUDA, Numerical Analysis, nVidia, Optimization, Programming techniques, Tesla C1060

September 23, 2011 by hgpu

Unstructured grid applications on GPU: performance analysis and improvement

Lizandro Solano-Quinde, Zhi Jian Wang, Brett Bode, Arun K. Somani

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Software Engineering, Tesla T10

September 23, 2011 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

Numerical Accuracy Differences in CPU and GPGPU Codes

GPU-Based Acceleration of the MLEM Algorithm for SPECT Parallel Imaging with Attenuation Correction and Compensation for Detector Response

Algorithm Acceleration from GPGPUs for the ATLAS Upgrade

Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

Automatic Translation of CUDA to OpenCL and Comparison of Performance Optimizations on GPUs

CU2CL: A CUDA-to-OpenCL Translator for Multi-and Many-core Architectures

Improving SIMT Efficiency of Global Rendering Algorithms with Architectural Support for Dynamic Micro-Kernels

Accelerating reaction-diffusion simulations with general-purpose graphics processing units

Parallel processing on NVIDIA graphics processing units using CUDA

Simple optimizations for an applicative array language for graphics processors

Compact data structure and scalable algorithms for the sparse grid technique

Unstructured grid applications on GPU: performance analysis and improvement

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)