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RTX Beyond Ray Tracing: Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location

Ingo Wald, Will Usher, Nate Morrical, Laura Lediaev, Valerio Pascucci

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Tags: 3D Graphics and Realism, CUDA, nVidia, nVidia Titan RTX, Raytracing

June 16, 2019 by hgpu

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Saptarshi Soham Mohanta, Collins Assisi

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Tags: Biology, Computer science, CUDA, Deep learning, Differential equations, Machine learning, Neural networks, nVidia, ODEs, Ordinary differential equations, Package, Partial differential equations, PDEs, Python, TensorFlow, Tutorial

June 12, 2019 by hgpu

ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data

Elias Stehle, Hans-Arno Jacobsen

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Tags: Computer science, CUDA, Data Structures and Algorithms, Databases, nVidia, nVidia GeForce GTX Titan X

June 5, 2019 by hgpu

Dynamic Distribution Pruning for Efficient Network Architecture Search

Xiawu Zheng, Rongrong Ji, Lang Tang, Yan Wan, Baochang Zhang, Yongjian Wu, Yunsheng Wu, Ling Shao

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla V100

June 5, 2019 by hgpu

A Development Platform for Embedded Domain-Specific Languages

Shigeru Chiba, YungYu Zhuang, Thanh-Chung Dao

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

June 2, 2019 by hgpu

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Yunfei Teng, Wenbo Gao, Francois Chalus, Anna Choromanska, Donald Goldfarb, Adrian Weller

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080

June 2, 2019 by hgpu

Breadth-First Search using Dynamic Parallelism on the GPU

Dominik Todling

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Tags: Algorithms, Breadth-First Search, Computer science, CUDA, nVidia, Package

May 30, 2019 by hgpu

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Zhenheng Tang, Yuxin Wang, Qiang Wang, Xiaowen Chu

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 2080 Ti, Performance, Tesla P100, Tesla V100

May 30, 2019 by hgpu

Massively Parallel GPU Memory Compaction

Matthias Springer, Hidehiko Masuhara

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan XP, Performance

May 30, 2019 by hgpu

Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Simon Garcia De Gonzalo, Sitao Huang, Juan Gomez-Luna, Simon Hammond, Onur Mutlu, Wen-mei Hwu

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Performance, performance portability, Tesla K40, Tesla P100

May 26, 2019 by hgpu

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

Murad Qasaimeh, Kristof Denolf, Jack Lo, Kees Vissers, Joseph Zambreno, Phillip H. Jones

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Tags: ARM, Benchmarking, Computer science, Computer vision, CUDA, FPGA, nVidia, nVidia Jetson TX2, OpenCV, Package

May 26, 2019 by hgpu

Performance Analysis of Deep Learning Workloads on Leading-edge Systems

Yihui Ren, Shinjae Yoo, Adolfy Hoisie

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Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia DGX-2, nVidia GeForce RTX 2080 Ti, Performance, Tesla V100

May 23, 2019 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

RTX Beyond Ray Tracing: Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data

Dynamic Distribution Pruning for Efficient Network Architecture Search

A Development Platform for Embedded Domain-Specific Languages

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Breadth-First Search using Dynamic Parallelism on the GPU

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Massively Parallel GPU Memory Compaction

Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

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)