high performance computing on graphics processing units: hgpu.org

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Efficient parallel implementation of the lattice Boltzmann method on large clusters of graphic processing units

Xiong QinGang, Li Bo, Xu Ji, Fang XiaoJian, Wang XiaoWei, Wang LiMin, He XianFeng, Ge Wei

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Tags: Algorithms, CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, MPI, Numerical simulation, nVidia, Tesla C2050

February 22, 2012 by hgpu

High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures

Daehyun Kim, Joshua Trzasko, Mikhail Smelyanskiy, Clifton Haider, Pradeep Dubey, Armando Manduca

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Tags: Algorithms, CUDA, Image processing, Medicine, MRI, nVidia, Sensing, Tesla C2050

February 21, 2012 by hgpu

GPU Parallel Statistical and Cube Test Analysis of the SHA-3 Finalist Candidate Hash Functions

Alan Kaminsky

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Tags: Computer science, CUDA, Hashing, nVidia, Package, Security, Tesla C2050

February 18, 2012 by hgpu

GPUs as Storage System Accelerators

Samer Al-Kiswany, Abdullah Gharaibeh, Matei Ripeanu

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Tags: Computer science, CUDA, Hashing, nVidia, Security, Storage system, Tesla C2050

February 17, 2012 by hgpu

A 3D radiative transfer framework. VIII. OpenCL implementation

P. H. Hauschildt, E. Baron

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Tags: Astrophysics, ATI, ATI Radeon HD 4870, Earth and Planetary Astrophysics, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce 8600 M, nVidia GeForce 8800 GT, nVidia GeForce GT 120, nVidia GeForce GTX 285, nVidia Quadro FX 4800, OpenCL, Solar and Stellar Astrophysics, Tesla C2050

February 17, 2012 by hgpu

Advancing Large Scale Many-Body QMC Simulations on GPU Accelerated Multicore Systems

Andres Tomas, Chia-Chen Chang, Richard Scalettar, Zhaojun Bai

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Tags: Computer science, CUDA, Linear Algebra, Monte Carlo simulation, nVidia, QMC, Tesla C2050

February 15, 2012 by hgpu

Graph Coarsening and Clustering on the GPU

B. O. Fagginger Auer, R. H. Bisseling

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Tags: Algorithms, Clustering, Computer science, CUDA, Graph theory, nVidia, Package, Tesla C2050

February 15, 2012 by hgpu

Acceleration of information-theoretic data analysis with graphics processing units

Davor Sluga, Tomaz Curk, Blaz Zupan, Uros Lotric

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Tags: Algorithms, Computer science, CUDA, Data mining, nVidia, Tesla C2050

February 14, 2012 by hgpu

Work Stealing Inside GPUs

Julio Toss

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 280, Task scheduling, Tesla C2050, Thesis

February 13, 2012 by hgpu

High-throughput protein crystallization on the World Community Grid and the GPU

Yulia Kotseruba, Christian A Cumbaa, Igor Jurisica

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Tags: ATI, ATI Radeon HD 5770, Biochemistry, Image processing, nVidia, nVidia GeForce GT 330 M, nVidia Quadro FX 3800, OpenCL, Tesla C2050

February 11, 2012 by hgpu

Auto-Generation and Auto-Tuning of 3D Stencil Codes on GPU Clusters

Yongpeng Zhang, Frank Mueller

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, Nearest neighbour, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tesla C2050

February 8, 2012 by hgpu

Comparing Linear and Convex Relaxations for Stereo and Motion

Thomas Schoenemann

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Tags: Algorithms, Computer science, Computer vision, nVidia, Optimization, Tesla C2050

February 5, 2012 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Efficient parallel implementation of the lattice Boltzmann method on large clusters of graphic processing units

High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures

GPU Parallel Statistical and Cube Test Analysis of the SHA-3 Finalist Candidate Hash Functions

GPUs as Storage System Accelerators

A 3D radiative transfer framework. VIII. OpenCL implementation

Advancing Large Scale Many-Body QMC Simulations on GPU Accelerated Multicore Systems

Graph Coarsening and Clustering on the GPU

Acceleration of information-theoretic data analysis with graphics processing units

Work Stealing Inside GPUs

High-throughput protein crystallization on the World Community Grid and the GPU

Auto-Generation and Auto-Tuning of 3D Stencil Codes on GPU Clusters

Comparing Linear and Convex Relaxations for Stereo and Motion

Recent source codes

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

ROCm's implementation of Gromacs

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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