high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

GPU-based Multi-start Local Search Algorithms

The Van Luong, Nouredine Melab, El-Ghazali Talbi

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTX 280, Optimization, Search

November 22, 2011 by hgpu

Using Graphics Processors for a High Performance Normalization of Gene Expressions

Andres Rodriguez, Oswaldo Trelles, Manuel Ujaldon

View

Download (PDF)

Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce 9800 GX2

November 22, 2011 by hgpu

Dataflow-Based Implementation of Layered Sensing Applications

Shuvra Bhattacharyya, Chung-Ching Shen, William Plishker, Nimish Sane, Hsiang-Huang Wu, Ruirui Gu

View

Download (PDF)

Tags: CUDA, Flow analysis, nVidia, nVidia GeForce GTX 260, Optimization, Sensing, Signal processing

November 22, 2011 by hgpu

Experiences with Achieving Portability across Heterogeneous Architectures

Lukasz G. Szafaryn, Todd Gamblin, Bronis R. de Supinski, Kevin Skadron

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 460, OpenMP, Performance

November 22, 2011 by hgpu

A new approach for sparse matrix vector product on NVIDIA GPUs

F. Vazquez, J. J. Fernandez, E. M. Garzon

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Optimization, Sparse matrix

November 21, 2011 by hgpu

GPU-Based Image Processing Use Cases: A High-Level Approach

Volkmar Wieser, Clemens Grelck, Holger Schoner, Peter Haslinger, Karoly Bosa, Bernhard Moser

View

Download (PDF)

Tags: Benchmarking, CUDA, Image processing, nVidia, nVidia GeForce GT 425 M, Software Engineering, Virtualization

November 21, 2011 by hgpu

CT image reconstruction with half precision floating-point values

Clemens Maass, Sven Steckmann, Matthias Baer, Lars Hillebrand, Willi A. Kalender, Marc Kachelriess

View

Download (PDF)

Tags: Algorithms, Computed tomography, CT, CUDA, Image processing, Image reconstruction, Larrabee, Medicine, nVidia, nVidia GeForce GTX 280

November 21, 2011 by hgpu

Efficient GPGPU-based parallel packet classification

Che-Lun Hung, Yaw-Ling Lin, Kuan-Ching Li, Hsiao-Hsi Wang, Shih-Wei Guo

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Filtering, Network communications, nVidia, nVidia GeForce GTS 450, Security

November 21, 2011 by hgpu

Conflux: Embedding Massively Parallel Semantics in a High-Level Programming Language

Eugene Burmako, Rauf Sadykhov

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, High-level Languages, nVidia, Package

November 21, 2011 by hgpu

Graph-based Parallel Analysis of Large Analog Circuits Based on GPU Platforms

Jianan Lu, Zhigang Hao, Sheldon X.-D. Tan

View

Download (PDF)

Tags: Algorithms, CAD, Computer science, CUDA, Electronic design automation, Graph theory, nVidia, Tesla S1070

November 21, 2011 by hgpu

Challenge benchmarks that must be conquered to sustain the gpu revolution

Emily Blem, Matthew Sinclair, Karthikeyan Sankaralingam

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, nVidia, Tesla C1060

November 21, 2011 by hgpu

Efficient Stack-less BVH Traversal for Ray Tracing

Michal Hapala, Tomas Davidovic, Ingo Wald, Vlastimil Havran, Philipp Slusallek

View

Download (PDF)

Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 260, nVidia GeForce GTX 470, Raytracing

November 20, 2011 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

* * *

high performance computing on graphics processing units: hgpu.org

GPU-based Multi-start Local Search Algorithms

Using Graphics Processors for a High Performance Normalization of Gene Expressions

Dataflow-Based Implementation of Layered Sensing Applications

Experiences with Achieving Portability across Heterogeneous Architectures

A new approach for sparse matrix vector product on NVIDIA GPUs

GPU-Based Image Processing Use Cases: A High-Level Approach

CT image reconstruction with half precision floating-point values

Efficient GPGPU-based parallel packet classification

Conflux: Embedding Massively Parallel Semantics in a High-Level Programming Language

Graph-based Parallel Analysis of Large Analog Circuits Based on GPU Platforms

Challenge benchmarks that must be conquered to sustain the gpu revolution

Efficient Stack-less BVH Traversal for Ray Tracing

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

Most viewed papers (last 30 days)