high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

Implementing Push-Pull Efficiently in GraphBLAS

Carl Yang, Aydin Buluc, John D. Owens

View

Tags: Algorithm optimization, Algorithms, BLAS, Computer science, CUDA, Graph theory, Linear Algebra, nVidia, Sparse matrix, Tesla K40

April 15, 2018 by hgpu

G-NET: Effective GPU Sharing in NFV Systems

Kai Zhang, Bingsheng He, Jiayu Hu, Zeke Wang, Bei Hua, Jiayi Meng, Lishan Yang

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Virtualization

April 15, 2018 by hgpu

Towards a Unified CPU-GPU code hybridization: A GPU Based Optimization Strategy Efficient on Other Modern Architectures

Ludomir Oteski, Guillaume De Verdiere, Sylvain Contassot-Vivier, Stephane Vialle, Juliet Ryan

View

Download (PDF)

Tags: Computer science, CUDA, Fluid dynamics, Intel Xeon Phi, nVidia, Tesla K20, Tesla P100

April 15, 2018 by hgpu

Sparse Matrix-Matrix Multiplication on Multilevel Memory Architectures : Algorithms and Experiments

Mehmet Deveci, Simon D. Hammond, Michael M. Wolf, Sivasankaran Rajamanickam

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tesla P100

April 7, 2018 by hgpu

A Comparison between GPU-based Volume Ray Casting Implementations: Fragment Shader, Compute Shader, OpenCL, and CUDA

Francisco Sans, Rhadames Carmona

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 660, OpenCL, OpenGL, Performance, Rendering

March 31, 2018 by hgpu

Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU)

Jyh-Miin Lin

View

Download (PDF)

Source codes

Tags: Algorithms, CUDA, FFT, Heterogeneous systems, Image processing, Image reconstruction, MRI, nVidia, nVidia GeForce GTX 965 M, OpenCL, Package, Python, Tesla K80

March 31, 2018 by hgpu

Design Principles for Sparse Matrix Multiplication on the GPU

Carl Yang, Aydin Buluc, John D. Owens

View

Download (PDF)

Tags: Computer science, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tesla K40

March 31, 2018 by hgpu

Scalable Breadth-First Search on a GPU Cluster

Yuechao Pan, Roger Pearce, John D. Owens

View

Download (PDF)

Tags: Computer science, CUDA, GPU cluster, nVidia, OpenMP, Performance, Tesla P100

March 25, 2018 by hgpu

Optimization of Hierarchical Matrix Computation on GPU

Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota

View

Download (PDF)

Tags: Computer science, CUDA, Intel Xeon Phi, Linear Algebra, nVidia, OpenMP, Tesla P100

March 25, 2018 by hgpu

MALBEC: a new CUDA-C ray-tracer in General Relativity

G. D. Quiroga

View

Download (PDF)

Source codes

Tags: CUDA, General Relativity and Quantum Cosmology, nVidia, nVidia GeForce GTX 960 M, Package, Physics

March 25, 2018 by hgpu

The VOLNA-OP2 Tsunami Code (Version 1.0)

Istvan Z Reguly, Devaraj Gopinathan, Joakim H Beck, Michael B Giles, Serge Guillas, Frederic Dias

View

Download (PDF)

Source codes

Tags: CUDA, Earth and Space Sciences, Intel Xeon Phi, nVidia, Package, Tesla P100

March 22, 2018 by hgpu

TBD: Benchmarking and Analyzing Deep Neural Network Training

Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan XP, nVidia Quadro P 4000

March 22, 2018 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

* * *

high performance computing on graphics processing units: hgpu.org

Implementing Push-Pull Efficiently in GraphBLAS

G-NET: Effective GPU Sharing in NFV Systems

Towards a Unified CPU-GPU code hybridization: A GPU Based Optimization Strategy Efficient on Other Modern Architectures

Sparse Matrix-Matrix Multiplication on Multilevel Memory Architectures : Algorithms and Experiments

A Comparison between GPU-based Volume Ray Casting Implementations: Fragment Shader, Compute Shader, OpenCL, and CUDA

Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU)

Design Principles for Sparse Matrix Multiplication on the GPU

Scalable Breadth-First Search on a GPU Cluster

Optimization of Hierarchical Matrix Computation on GPU

MALBEC: a new CUDA-C ray-tracer in General Relativity

The VOLNA-OP2 Tsunami Code (Version 1.0)

TBD: Benchmarking and Analyzing Deep Neural Network Training

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)