🗊Презентация High Performance Deep Learning on Intel Architecture

High Performance Deep Learning on Intel® Architecture Ivan Kuzmin Engineering Manager for AI Performance Libraries December 19, 2016

Fast Evolution of Technology

Classical Machine Learning

Deep learning

End-to-End Deep Learning

Automating previously “human” tasks

Deep Learning Challenges

Deep Learning Challenges

Scaling is I/O Bound

Intel Provides the Compute Foundation for DL

INTEL® MKL-DNN

Deep learning with Intel® MKL-DNN

Deep learning with Intel® MKL-DNN

Deep learning with Intel® MKL-DNN

Intel® Xeon Phi ™ processor 7250 up to 400x performance increase with Intel Optimized Frameworks compared to baseline out of box performance

Intel® Xeon Phi ™ processor Knights Mill up to 4x estimated performance improvement over Intel® Xeon Phi™ processor 7290

INTEL® Machine Learning Scaling Library

Intel® Machine Learning Scaling Library (MLSL) Deep learning abstraction of message-passing implementations. Built on top of MPI, allows other communication libraries to be used Optimized to drive scalability of communication patterns Works across various interconnects: Intel® Omni-Path Architecture, InfiniBand, and Ethernet Common API to support Deep Learning frameworks (Caffe, Theano, Torch etc.)

Intel® Xeon Phi™ Processor 7250 GoogleNet V1 Time-To-Train Scaling Efficiency up to 97% on 32 nodes

NeON framework

Neon: DL Framework with Blazing Performance

Intel® Nervana™ Graph Compiler Intel® Nervana™ Graph Compiler: High-level execution graph for neural networks to enable optimizations that are applicable across multiple HW targets.

Intel® Nervana™ Graph Compiler as the performance building block…

INTEL® DEEP Learning SDK

Intel® Deep Learning SDK Accelerate Your Deep Learning Solution A free set of tools for data scientists and software developers to develop, train, and deploy deep learning solutions

Deep Learning Training Tool Intel® Deep Learning SDK Simplify installation of Intel optimized Deep Learning Frameworks Easy and Visual way to Set-up, Tune and Run Deep Learning Algorithms: Create training dataset Design model with automatically optimized hyper-parameters Launch and monitor training of multiple candidate models Visualize training performance and accuracy

Deep Learning Deployment Tool Intel® Deep Learning SDK Unleash fast scoring performance on Intel products while abstracting the HW from developers Imports trained models from all popular DL framework regardless of training HW Compresses model for improved execution, storage & transmission (pruning, quantization) Generate Inference HW-Specific Code (C/C++, OpenVX, OpenCL, etc.) Enables seamless integration with full system / application software stack

Deep Learning Tools for End-to-End Workflow Intel® Deep Learning SDK

Leading AI research

Summary Intel provides highly optimized libraries to accelerate all DL frameworks Intel® Machine Learning Scaling Library (MLSL) allow to scale DL to 32 nodes and beyond Nervana graph compiler, next innovation for DL performance Intel® Deep Learning SDK make it easy for you to start exploring DeepLearning Intel is committed to provide algorithmic, SW and HW innovations to get best performance for DL on IA Get more details at: https://software.intel.com/en-us/ai/deep-learning

Legal Disclaimer & Optimization Notice INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS”. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. INTEL ASSUMES NO LIABILITY WHATSOEVER AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO THIS INFORMATION INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. Copyright © 2016, Intel Corporation. All rights reserved. Intel, Pentium, Xeon, Xeon Phi, Core, VTune, Cilk, and the Intel logo are trademarks of Intel Corporation in the U.S. and other countries.

Configuration details BASELINE: Caffe Out Of the Box, Intel® Xeon Phi™ processor 7250 (68 Cores, 1.4 GHz, 16GB MCDRAM: cache mode), 96GB memory, Centos 7.2 based on Red Hat* Enterprise Linux 7.2, BVLC-Caffe: https://github.com/BVLC/caffe, with OpenBLAS, Relative performance 1.0 NEW: Caffe: Intel® Xeon Phi™ processor 7250 (68 Cores, 1.4 GHz, 16GB MCDRAM: cache mode), 96GB memory, Centos 7.2 based on Red Hat* Enterprise Linux 7.2, Intel® Caffe: : https://github.com/intel/caffe based on BVLC Caffe as of Jul 16, 2016, MKL GOLD UPDATE1, Relative performance up to 400x AlexNet used for both configuration as per https://papers.nips.cc/paper/4824-Large image database-classification-with-deep-convolutional-neural-networks.pdf, Batch Size: 256

Configuration details BASELINE: Intel® Xeon Phi™ Processor 7290 (16GB, 1.50 GHz, 72 core) with 192 GB Total Memory on Red Hat Enterprise Linux* 6.7 kernel 2.6.32-573 using MKL 11.3 Update 4, Relative performance 1.0 NEW: Intel® Xeon phi™ processor family – Knights Mill, Relative performance up to 4x

Configuration details