high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

wav2letter++: The Fastest Open-source Speech Recognition System

Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Speech recognition, Tesla V100

December 23, 2018 by hgpu

Deep Learning by Doing: The NVIDIA Deep Learning Institute and University Ambassador Program

Xi Chen, Gregory S. Gutmann, Joe Bungo

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Tags: Cloud, Computer science, CUDA, Deep learning, Distributed computing, Education, Neural networks, nVidia, OpenACC

December 23, 2018 by hgpu

On Runtime Systems for Task-based Programming on Heterogeneous Platforms

Samuel Thibault

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia Quadro FX 5800, OpenCL, Operating systems, StarPU, Task scheduling, Tesla C2050, Tesla K20, Tesla M2075, Thesis

December 23, 2018 by hgpu

Targeting GPUs with OpenMP Directives on Summit: A Simple and Effective Fortran Experience

Reuben D. Budiardja, Christian Y. Cardall

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Tags: cfd, CUDA, Fluid dynamics, Fortran, MPI, nVidia, OpenMP, Package, Physics, Tesla V100

December 23, 2018 by hgpu

cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU

Behrooz Zare, Foad Jafarinejad, Matin Hashemi, Saber Salehkaleybar

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Tags: Bayesian, Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 1080

December 23, 2018 by hgpu

Software Platform for Hybrid Resource Management of Many-core Accelerators

Taeyoung Kim

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Tags: Computer science, Design space exploration, Intel Xeon Phi, OpenCL, Thesis

December 16, 2018 by hgpu

Performance Analysis of a Stereo Matching Implementation in OpenCL

Stephan Rotheneder

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 750 Ti, nVidia Quadro 1000 M, OpenCL, Stereo vision

December 16, 2018 by hgpu

Developing acquisition systems based on FPGA with OpenCL

Víctor Calvo Pampliega

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Tags: Computer science, Data acquisition, FPGA, OpenCL, Thesis

December 16, 2018 by hgpu

Delivering Performance-Portable Stencil Computations on CPUs and GPUs Using Bricks

Tuowen Zhao, Samuel Williams, Mary Hall, Hans Johansen

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Tags: Computer science, CUDA, Intel Xeon Phi, nVidia, performance portability, Stencil computation, Tesla P100

December 16, 2018 by hgpu

SIMD-X: Programming and Processing of Graph Algorithms on GPUs

Hang Liu, H. Howie Huang

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Tags: Computer science, CUDA, Graph theory, nVidia, Tesla K20, Tesla K40, Tesla P100

December 16, 2018 by hgpu

High Performance Portable Tsunami Simulations on Many-core CPU, GPU, and FPGA

Fumiya Kono

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Tags: AMD FirePro W8100, AMD FirePro W9100, AMD Radeon R9 280X, ATI, CUDA, Earth and Space Sciences, FPGA, Intel Xeon Phi, Numerical simulation, nVidia, OpenCL, Tesla K20, Thesis

December 9, 2018 by hgpu

ASW: Accelerating Smith-Waterman Algorithm on Coupled CPU-GPU Architecture

Huihui Zou, Shanjiang Tang, Ce Yu, Hao Fu, Yusen Li, Wenjie Tang

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Tags: Algorithms, APU, Bioinformatics, Biology, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 750, OpenCL, Sequence alignment, Smith-Waterman algorithm

December 9, 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

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high performance computing on graphics processing units: hgpu.org

Programming

wav2letter++: The Fastest Open-source Speech Recognition System

On Runtime Systems for Task-based Programming on Heterogeneous Platforms

Targeting GPUs with OpenMP Directives on Summit: A Simple and Effective Fortran Experience

cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU

Software Platform for Hybrid Resource Management of Many-core Accelerators

Performance Analysis of a Stereo Matching Implementation in OpenCL

Developing acquisition systems based on FPGA with OpenCL

Delivering Performance-Portable Stencil Computations on CPUs and GPUs Using Bricks

SIMD-X: Programming and Processing of Graph Algorithms on GPUs

High Performance Portable Tsunami Simulations on Many-core CPU, GPU, and FPGA

ASW: Accelerating Smith-Waterman Algorithm on Coupled CPU-GPU Architecture

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)