high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla T4

Performance of CPU and GPU HPC Architectures for off-design aircraft simulation

Mike Turner, Jamil Appa, Neil Ashton

View

Tags: cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenMP, SYCL, Tesla A100, Tesla T4, Tesla V100

January 31, 2021 by hgpu

TopicBERT for Energy Efficient Document Classification

Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni, Thomas Runkler, Hinrich Schütze

View

Tags: classification, Computer science, CUDA, Deep learning, NLP, nVidia, Tesla T4

November 8, 2020 by hgpu

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

Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM

Zijing Gu

View

Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, nVidia, Package, Sparse matrix, Tesla T4

August 2, 2020 by hgpu

GPU coprocessors as a service for deep learning inference in high energy physics

Jeffrey Krupa, Kelvin Lin, Maria Acosta Flechas, Jack Dinsmore, Javier Duarte, Philip Harris, Scott Hauck, Burt Holzman, Shih-Chieh Hsu, Thomas Klijnsma, Mia Liu, Kevin Pedro, Natchanon Suaysom, Matt Trahms, Nhan Tran

View

Tags: Deep learning, FPGA, HEP, nVidia, Package, Physics, Tesla T4, Tesla V100

July 26, 2020 by hgpu

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Parallel Gaussian process with kernel approximation in CUDA

See all packages

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: