high performance computing on graphics processing units: hgpu.org

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GPU-accelerated Faster Mean Shift with euclidean distance metrics

Le You, Han Jiang, Jinyong Hu, Chorng Chang, Lingxi Chen, Xintong Cui, Mengyang Zhao

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Tags: Algorithms, Clustering, CUDA, Image processing, nVidia, nVidia GeForce RTX 3070, nVidia GeForce RTX 3080 Ti, nVidia Quadro P 5000, Package, Pattern recognition, Tesla P100

January 2, 2022 by hgpu

A Unified FPGA Virtualization Framework for General-Purpose Deep Neural Networks in the Cloud

Shulin Zeng, Guohao Dai, Hanbo Sun, Jun Liu, Shiyao Li, Guangjun Ge, Kai Zhong, Kaiyuan Guo, Yu Wang, Huazhong Yang

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Tags: Cloud, Computer science, CUDA, FPGA, Neural networks, nVidia, Tesla V100, Virtualization

January 2, 2022 by hgpu

COX: CUDA on X86 by Exposing Warp-Level Functions to CPUs

Ruobing Han, Jaewon Lee, Jaewoong Sim, Hyesoon Kim

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia

December 26, 2021 by hgpu

TCUDB: Accelerating Database with Tensor Processors

Yu-Ching Hu, Yuliang Li, Hung-Wei Tseng

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Tags: Computer science, CUDA, Databases, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 3090

December 19, 2021 by hgpu

Evaluation of Pseudo-Random Number Generation on GPU Cards

Tair Askar, Bekdaulet Shukirgaliyev, Martin Lukac, Ernazar Abdikamalov

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 3080, nVidia GeForce RTX 3090, Pseudo-random number generators

December 19, 2021 by hgpu

Fast Neural Representations for Direct Volume Rendering

Sebastian Weiss, Philipp Hermüller, Rüdiger Westermann

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Tags: 3D Graphics and Realism, CUDA, GLSL, nVidia, nVidia GeForce RTX 2070, Package, Raytracing, Rendering, Visualization

December 12, 2021 by hgpu

Autotuning CUDA: Applying NLP Techniques to LS-CAT

Lars Bjertnes, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Computer science, CUDA, Machine learning, NLP, nVidia

December 5, 2021 by hgpu

Bayesian Optimization for auto-tuning GPU kernels

Floris-Jan Willemsen, Rob van Nieuwpoort, Ben van Werkhoven

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Tags: Bayesian, Computer science, CUDA, Machine learning, nVidia, nVidia A100, nVidia GeForce GTX Titan X, nVidia GeForce RTX 2070, OpenCL, Package, Performance

December 5, 2021 by hgpu

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

Yuxin Chen, Benjamin Brock, Serban Porumbescu, Aydın Buluç, Katherine Yelick, John D. Owens

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Tags: Computer science, CUDA, nVidia, Task scheduling, Tesla V100

December 5, 2021 by hgpu

Predictive Data Race Detection for GPUs

Sagnik Dey, Mayant Mukul, Parth Sharma, Swarnendu Biswas

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro RTX 5000, Performance

November 28, 2021 by hgpu

A Variant RSA Acceleration with Parallelization

Jun-jie Liu, Kang-Too Tsang, Yu-Hui Deng

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Tags: Computer science, CUDA, nVidia, RSA, Security, Tesla P100

November 28, 2021 by hgpu

BAT: A Benchmark suite for AutoTuners

Ingunn Sund, Knut A. Kirkhorn, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, Package, Performance, Tesla T4, Tesla V100

November 21, 2021 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

GPU-accelerated Faster Mean Shift with euclidean distance metrics

COX: CUDA on X86 by Exposing Warp-Level Functions to CPUs

TCUDB: Accelerating Database with Tensor Processors

Evaluation of Pseudo-Random Number Generation on GPU Cards

Fast Neural Representations for Direct Volume Rendering

Autotuning CUDA: Applying NLP Techniques to LS-CAT

Bayesian Optimization for auto-tuning GPU kernels

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

Predictive Data Race Detection for GPUs

A Variant RSA Acceleration with Parallelization

BAT: A Benchmark suite for AutoTuners

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)