high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures

Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan Sundaram, Jialin Liu, Peter Sadowski, Evan Racah, Suren Byna, Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey

View

Download (PDF)

Tags: Algorithms, classification, Computer science, Data mining, Intel Xeon Phi, KD-tree, Machine learning, Nearest neighbour

July 30, 2016 by hgpu

Many-Core Algorithms for Combinatorial Optimization

Francesco Strappaveccia

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, MPI, nVidia, nVidia GeForce GTX 570, nVidia GeForce GTX 770, nVidia GeForce GTX Titan, OpenMP, Optimization, Path problems, Thesis

July 28, 2016 by hgpu

PASSATA – Object oriented numerical simulation software for adaptive optics

G. Agapito, A. Puglisi, S. Esposito

View

Download (PDF)

Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, Numerical simulation, nVidia, Optics

July 28, 2016 by hgpu

Genetic Improvement of GPU Software

William B. Langdon, Brian Yee Hong Lam, Marc Modat, Justyna Petke, Mark Harman

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 580, Tesla C2050, Tesla K20, Tesla T10

July 28, 2016 by hgpu

Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs

Brian Ichter, Edward Schmerling, Ali-akbar Agha-mohammadi, Marco Pavone

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Package, Robotics

July 28, 2016 by hgpu

Strategies for Protecting Intellectual Property when Using CUDA Applications on Graphics Processing Units

X. Bellekens, G. Paul, J. M. Irvine, C. Tachtatzis, R. C. Atkinson

View

Download (PDF)

Tags: CUDA, nVidia

July 26, 2016 by xhgpu

A Data Parallel Algorithm for Seismic Raytracing

Allen D. Malony, Stephanie McCumsey, Joseph Byrnes, Craig Rasmusen, Soren Rasmusen, Erik Keever, Doug Toomey

View

Download (PDF)

Tags: Algorithms, Earth and Space Sciences, Geoscience, nVidia, OpenCL, OpenMP, Raytracing, Seismology, Tesla K80, Tesla M2070

July 26, 2016 by hgpu

FPGA-Based Accelerator Design from a Domain-Specific Language

M. Akif Ozkan, Oliver Reiche, Frank Hannig, Jurgen Teich

View

Download (PDF)

Tags: Algorithms, Computer vision, FPGA, Heterogeneous systems, Image processing, nVidia, nVidia Jetson TK1, OpenCL, Tesla K20

July 26, 2016 by hgpu

GPU-accelleration of image rendering and sorting algorithms with the OpenCL framework

Anders Soderholm, Justus Sorman

View

Download (PDF)

Tags: Algorithms, Computer science, Heterogeneous systems, Image processing, Intel HD 4600, OpenCL, Rendering, Sorting, Thesis

July 26, 2016 by hgpu

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Marius Erbert, Steffen Rechner, Matthias Muller-Hannemann

View

Download (PDF)

Source codes

Tags: Bioinformatics, Biology, Computer science, CUDA, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan, Package

July 26, 2016 by hgpu

Algorithmic Trading: A brief, computational finance case study on data centre FPGAs

Gordon Inggs

View

Download (PDF)

Tags: AMD FirePro W5000, ATI, Computational finance, Computer science, Finance, FPGA, nVidia, nVidia Quadro K4000, OpenCL

July 20, 2016 by hgpu

Lowering IrGL to CUDA

Sreepathi Pai, Keshav Pingali

View

Download (PDF)

Tags: Compilers, Computer science, CUDA, nVidia, Programming Languages

July 20, 2016 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

Programming

PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures

Many-Core Algorithms for Combinatorial Optimization

PASSATA – Object oriented numerical simulation software for adaptive optics

Genetic Improvement of GPU Software

Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs

Strategies for Protecting Intellectual Property when Using CUDA Applications on Graphics Processing Units

A Data Parallel Algorithm for Seismic Raytracing

FPGA-Based Accelerator Design from a Domain-Specific Language

GPU-accelleration of image rendering and sorting algorithms with the OpenCL framework

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Algorithmic Trading: A brief, computational finance case study on data centre FPGAs

Lowering IrGL to CUDA

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)