Leveraging Microsoft Azures GPU N -Series for Compute and - - PowerPoint PPT Presentation

Leveraging Microsoft Azure’s GPU N-Series for Compute and Visualization

Karan Batta, Program Manager, Microsoft Azure Alexey Kamenev, Software Engineer, Microsoft Research S6839

Agenda

• Azure
• HPC in the Cloud
• T

echnology/Architecture

• CNTK Overview & Demo

Vision

 GPU based capabilities in cloud infrastructure  High end performance  Not “Swiss-army knife” approach  Deliver and empower developer scenarios  Achieve true “HPC PC in the Cl e Clou

• ud”

 Critical workloads

HPC in the Cloud

APP

exe exe exe exe

Workflow

Rendering Algorithm Executable

Azure

GPU VMs

Upload data Submit job Split job/ setup execution pipeline

Manage

GPU Visualization

Analytics Dynamic Modelling Virtual Desktops

{REST API}

Return results

Outpu tputs ts

Where?

Fi Financ nce

• FX Options
• Risk Management
• Hedge Fund Management

Manufa fact cturin uring g & Oi & Oil/Gas

• Automotive design
• Reservoir modelling
• Manipulation of models & parts

Medi dia

• Streaming games/video
• Transcoding
• Social media analysis

Re Rende dering ring

• VFX/Ray-Tracing rendering
• CAD applications
• Simulations

T echnology  DDA (Discrete Device Assignment)  Introduced in Windows Server 2016  Pass-through PCIe devices  Allows for close to bare-metal performance

Architecture

Applications GPU Provisioning Host OS Client OS Hardware

• Custom Applications
• Data and Applications from the Azure Marketplace
• Bring your own Image
• Azure VM Marketplace Images
• Hyper-V
• DDA
• NVIDIA M60 GPU (Viz SKU)
• NVIDIA K80 GPU (Compute SKU)

Visualization VMs

NV6 NV12 NV24 Cores 6 (E5-2690v3) 12 (E5-2690v3) 24 (E5-2690v3) GPU 1 x M60 GPU (1/2 Physical Card) 2 x M60 GPU (1 Physical Card) 4 x M60 GPU (2 Physical Cards) Memory 56 GB 112 GB 224 GB Disk ~380 GB SSD ~680 GB SSD ~1.5 TB SSD Network Azure Network Azure Network Azure Network

Compute VMs

NC6 NC12 NC24 NC24r Cores 6 (E5-2690v3) 12 (E5-2690v3) 24 (E5-2690v3) 24 (E5-2690v3) GPU 1 x K80 GPU (1/2 Physical Card) 2 x K80 GPU (1 Physical Card) 4 x K80 GPU (2 Physical Cards) 4 x K80 GPU (2 Physical Cards) Memory 56 GB 112 GB 224 GB 224 GB Disk ~380 GB SSD ~680 GB SSD ~1.5 TB SSD ~1.5 TB SSD Network Azure Network Azure Network Azure Network Azure Network + RDMA (RoCE)

CNTK

Alexey Kamenev Senior Software Engineer Microsoft Research

CNTK Overview

• A deep learning tool that balances
• Efficienc

ciency: Can train production systems as fast as possible

• Perfor

formanc mance: Can achieve state-of-the-art performance on benchmark tasks and production systems

• Flex

exib ibility lity: Can support various tasks such as speech, image, and text, and can try out new ideas quickly

• Inspiration: Legos
• Each brick is very simple and performs a specific function
• Create arbitrary objects by combining many bricks
• CNTK enables the creation of existing and novel models by combining simple

functions in arbitrary ways.

• Historical facts:
• Created by Microsoft Speech researchers (Dong Yu et al.) 4 years ago
• Was quickly extended to handle other workloads (image/text)
• Open-sourced (CodePlex) in early 2015
• Moved to GitHub in Jan 2016

Resources

• “Deep Learning in Microsoft with CNTK” – Alexey

exey Kame menev nev, , Micr croso soft – Hall l 3 – 4.30pm pm

• CNTK (Deep-Learning toolkit)
• htt

ttps:// ://github github.co .com/ m/Micr Microso

• soft/

t/CNTK CNTK

• DDA (Direct Device Assignment)
• htt

ttp:// //blo blogs. gs.techn technet.com et.com/b/vi /b/virtuali tualizatio zation/ar n/archiv chive/ e/2015/11/23/ 015/11/23/disc discrete ete- devic ice-as assi signmen nment-gp gpus. us.as aspx

• NVIDIA announcement
• htt

ttp://nvidianew //nvidianews.nvidia s.nvidia.co .com/ m/news/nvidia ews/nvidia-gpus pus-to to-accel accelerate erate-mic microso soft- azure

Thank you!