high performance computing on graphics processing units: hgpu.org

Manuel López-Martínez, Germán Díaz-Flórez, Santiago Villagrana-Barraza, Luis O. Solís-Sánchez, Héctor A. Guerrero-Osuna, Genaro M. Soto-Zarazúa, Carlos A. Olvera-Olvera

View

Download (PDF)

Tags: CUDA, Deep learning, Distributed computing, HPC, Image processing, Neural networks, nVidia, nVidia GeForce GTX 1050 Ti

June 4, 2023 by hgpu

PyTorch Hyperparameter Tuning – A Tutorial for spotPython

Thomas Bartz-Beielstein

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Package, Python, PyTorch, Tutorial

May 28, 2023 by hgpu

ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time

Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce RTX 3070

May 28, 2023 by hgpu

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Zhiyi Zhang, Pengfei Zhang, Qi Wang

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Java, nVidia, nVidia GeForce RTX 3060 Ti, Package

May 21, 2023 by hgpu

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Yueming Hao, Xu Zhao, Bin Bao, David Berard, Will Constable, Adnan Aziz, Xu Liu

View

Download (PDF)

Source codes

Tags: AMD Radeon Instinct MI210, ATI, Benchmarking, Computer science, Deep learning, nVidia, nVidia A100, Package, PyTorch

May 14, 2023 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_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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Increased reliability on Intel GPUs via software diverse redundancy

Monadic Deep Learning

Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU

Improving Automatic Parallel Training via Balanced Memory Workload Optimization

Deep Language Models for Software Testing and Optimisation

DGEMM on Integer Matrix Multiplication Unit

Compiler Technologies in Deep Learning Co-Design: A Survey

A High-Performance Computing Cluster for Distributed Deep Learning: A Practical Case of Weed Classification Using Convolutional Neural Network Models

PyTorch Hyperparameter Tuning – A Tutorial for spotPython

ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Recent source codes

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

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

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Most viewed papers (last 30 days)