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Scaling Deep Learning on GPU and Knights Landing clusters

Yang You, Aydin Buluc, James Demmel

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Tags: Cloud, Computer science, Deep learning, GPU cluster, Intel Xeon Phi, Machine learning, Neural networks, nVidia, Package, PC cluster, Tesla K80, Tesla M100

August 17, 2017 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

Vina-GPU: A heterogeneous OpenCL implementation of AutoDock Vina

Accelerating AutoDock VINA with GPUs

HEVolRender: Homomorphic-Encrypted Volume Rendering

Homomorphic-Encrypted Volume Rendering

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

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