high performance computing on graphics processing units: hgpu.org

hgpu.org » Package

A Comparison of Optimal Scanline Voxelization Algorithms

Tim Håkansson

View

Tags: 3D Graphics and Realism, CUDA, GLSL, nVidia, OpenGL, Package, Tesla T4, Thesis, Voxelization

September 20, 2020 by hgpu

PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems

Anirban Ghose, Siddharth Singh, Vivek Kulaharia, Lokesh Dokara, Srijeeta Maity, Soumyajit Dey

View

Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 970, OpenCL, Package

September 20, 2020 by hgpu

HPX – The C++ Standard Library for Parallelism and Concurrency

Hartmut Kaiser, Patrick Diehl, Adrian S. Lemoine, Bryce Adelstein Lelbach, Parsa Amini, Agustín Berge, John Biddiscombe, Steven R. Brandt, Nikunj Gupta, Thomas Heller, Kevin Huck, Zahra Khatami, Alireza Kheirkhahan, Auriane Reverdell, Shahrzad Shirzad, Mikael Simberg, Bibek Wagle, Weile Wei, Tianyi Zhang

View

Tags: Computer science, CUDA, nVidia, OpenCL, Package, Performance

September 13, 2020 by hgpu

Accelerating High-Order Stencils on GPUs

Ryuichi Sai, John Mellor-Crummey, Xiaozhu Meng, Mauricio Araya-Polo, Jie Meng

View

Tags: Computer science, HPC, nVidia, nVidia Quadro NVS 510, Package, Performance, Stencil computation, Tesla P100, Tesla V100

September 13, 2020 by hgpu

Hierarchical Roofline Analysis: How to Collect Data using Performance Tools on Intel CPUs and NVIDIA GPUs

Charlene Yang

View

Tags: Computer science, CUDA, nVidia, Package, Performance, Tesla V100

September 13, 2020 by hgpu

Performance portability through machine learning guided kernel selection in SYCL libraries

John Lawson

View

Tags: AMD R9 Nano, ATI, Auto-Tuning, Benchmarking, Clustering, Computer science, Machine learning, Package, Performance, performance portability, SYCL

September 6, 2020 by hgpu

Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer

Suyoung Lee, HyungSeok Han, Sang Kil Cha, Sooel Son

View

Tags: Computer science, Java, Neural networks, nVidia, nVidia GeForce GTX Titan XP, Package, Python, PyTorch, Security

August 30, 2020 by hgpu

CRAC: Checkpoint-Restart Architecture for CUDA with Streams and UVM

Twinkle Jain, Gene Cooperman

View

Tags: Computer science, CUDA, HPC, nVidia, nVidia Quadro K600, Package, Tesla V100

August 30, 2020 by hgpu

8 Steps to 3.7 TFLOP/s on NVIDIA V100 GPU: Roofline Analysis and Other Tricks

Charlene Yang

View

Tags: Computer science, HPC, nVidia, OpenACC, Optimization, Package, Performance, Tesla V100

August 30, 2020 by hgpu

AIPerf: Automated machine learning as an AI-HPC benchmark

Zhixiang Ren, Yongheng Liu, Tianhui Shi, Lei Xie, Yue Zhou, Jidong Zhai, Youhui Zhang, Yunquan Zhang, Wenguang Chen

View

Tags: Algorithms, Artificial intelligence, Benchmarking, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti, Package, Tesla V100

August 23, 2020 by hgpu

Heterogeneous parallel computing for image registration and linear algebra applications

Orestis Zachariadis

View

Tags: CUDA, Heterogeneous systems, Image processing, Image registration, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 1050, nVidia GeForce RTX 2070, Package, Sparse matrix, Thesis

August 9, 2020 by hgpu

Ignite-GPU: a GPU-enabled in-memory computing architecture on clusters

Amir Hossein Sojoodi, Majid Salimi Beni, Farshad Khunjush

View

Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 680, Package

August 9, 2020 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 plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

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

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: