high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia Tesla GP100

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung

View

Tags: Artificial intelligence, Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce GTX Titan XP, nVidia Tesla GP100

November 11, 2018 by hgpu

ComScribe: a tool that identifies communication among all GPU-GPU and CPU-GPU pairs in a single-node multi-GPU system

Monitoring Collective Communication Among GPUs

PHCpack

Least Squares on GPUs in Multiple Double Precision

HEVolRender: Homomorphic-Encrypted Volume Rendering

Homomorphic-Encrypted Volume Rendering

Vina-GPU: A heterogeneous OpenCL implementation of AutoDock Vina

Accelerating AutoDock VINA with GPUs

GCN Inference Acceleration HLS

GCN Inference Acceleration using High-Level Synthesis

Climate Machine: an Earth System Model that automatically learns from data

Large-eddy simulations with ClimateMachine: a new open-source code for atmospheric simulations on GPUs and CPUs

HLS Portability from Intel to Xilinx: A Case Study

HLS Portability from Intel to Xilinx: A Case Study

HEXL: Intel Homomorphic Encryption Acceleration Library

Accelerating Encrypted Computing on Intel GPUs

Taskflow: A General-purpose Parallel and Heterogeneous Task Programming System

Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System

CompilerGym: a library performant reinforcement learning environments for compiler tasks

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

See all packages

* * *

* * *

HGPU group © 2010-2021 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: