high performance computing on graphics processing units: hgpu.org

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Efficient Implementation of Bi-directional Path Tracer on GPU

Vilem Otte, Marek Vinkler

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 720 M, Raytracing, Rendering

May 3, 2015 by hgpu

Fine-Grained Synchronizations and Dataflow Programming on GPUs

Ang Li, Gert-Jan van den Braak, Henk Corporaa, Akash Kumar

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 570, Performance, PTX

May 3, 2015 by hgpu

Massively Parallel kNN using CUDA on Spam-Classification

Joshua M. Smithrud, Patrick McElroy, Razvan Andonie

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Tags: Algorithms, Computer science, CUDA, Nearest neighbour, nVidia, nVidia GeForce GTX 260

May 3, 2015 by hgpu

Parallel Genetic Algorithms on a GPU to Solve the Travelling Salesman Problem

Leopoldo Noel Gaxiola Sanchez, Juan Jose Tapia Armenta, Victor Hugo Diaz Ramirez

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Tags: Algorithms, Computer science, CUDA, Genetic algorithm, nVidia, nVidia GeForce GTX 780, Path problems

April 27, 2015 by hgpu

Speculative Segmented Sum for Sparse Matrix-Vector Multiplication on Heterogeneous Processors

Weifeng Liu, Brian Vinter

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Tags: ATI, Computer science, CUDA, Intel HD 5500, Mathematical Software, Numerical Analysis, nVidia, nVidia Jetson TK1, OpenCL, Package, Sparse matrix

April 27, 2015 by hgpu

GPU Accelerated framework for financial nested simulations

Joris Cramwinckel

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Tags: CUDA, Finance, nVidia, Risk Management, Tesla C2050, Tesla K20, Thesis

April 27, 2015 by hgpu

Algorithm 9xx: Sparse QR Factorization on the GPU

Sencer Nuri Yeralan, Timothy A. Davis, Sanjay Ranka

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Package, Sparse matrix, Tesla C2070

April 25, 2015 by hgpu

A Study of Scheduling a Neuro-imaging Application On a Heterogeneous CPU-GPU Cluster

Reza Nakhjavani

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Tags: Algorithms, CUDA, GPU cluster, Heterogeneous systems, Image processing, Medicine, nVidia, nVidia GeForce GTX Titan, PC cluster, Thesis

April 25, 2015 by hgpu

Direct Communication Methods for Distributed GPUs

Lena Oden

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Tags: Algorithms, Cluster computing, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Thesis

April 25, 2015 by hgpu

Flexible Software Profiling of GPU Architectures

Mark Stephenson, Siva Kumar Sastry Hari, Yunsup Lee, Eiman Ebrahimi, Daniel R. Johnson, David Nellans, Mike O'Connor, Stephen W. Keckler

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Tags: Computer science, CUDA, Design space exploration, nVidia, PTX, Tesla K10, Tesla K20, Tesla K40

April 25, 2015 by hgpu

A Framework for General Sparse Matrix-Matrix Multiplication on GPUs and Heterogeneous Processors

Weifeng Liu, Brian Vinter

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Tags: AMD Radeon R9 290X, ATI, Computer science, CUDA, Mathematical Software, Matrix multiplication, Numerical Analysis, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, OpenCL, Package, Path problems, Sparse matrix

April 23, 2015 by hgpu

Multi-GPU Graph Analytics

Yuechao Pan, Yangzihao Wang, Yuduo Wu, Carl Yang, John D. Owens

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Tags: Computer science, CUDA, Graph theory, nVidia, Tesla K40, Tesla K80

April 23, 2015 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

Efficient Implementation of Bi-directional Path Tracer on GPU

Fine-Grained Synchronizations and Dataflow Programming on GPUs

Massively Parallel kNN using CUDA on Spam-Classification

Parallel Genetic Algorithms on a GPU to Solve the Travelling Salesman Problem

Speculative Segmented Sum for Sparse Matrix-Vector Multiplication on Heterogeneous Processors

GPU Accelerated framework for financial nested simulations

Algorithm 9xx: Sparse QR Factorization on the GPU

A Study of Scheduling a Neuro-imaging Application On a Heterogeneous CPU-GPU Cluster

Direct Communication Methods for Distributed GPUs

Flexible Software Profiling of GPU Architectures

A Framework for General Sparse Matrix-Matrix Multiplication on GPUs and Heterogeneous Processors

Multi-GPU Graph Analytics

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)