Intel® Artificial Intelligence Workshop: Deep Learning at Scale using Distributed Frameworks
|Date:||Thursday, October 10, 2019, 13:30-17:00|
|Location:||LRZ Building, Garching/Munich, Boltzmannstr. 1, LRZ Hörsaal|
The demand of using Deep Learning techniques in many scientific domains is rapidly emerging and the requirements for large compute and memory resources is increasing. One of the consequences is the need of the high-performance computing capability for processing and inferring the valuable information inherent in the data. The Leibniz Supercomputing Centre (LRZ) has recently installed its new high-end system, SuperMUC-NG. Based on Intel® technology, it targets among others also workloads at the crossroads of AI and HPC.
In this session you will learn various optimization methods to improve the runtime performance of Deep Learning algorithms on Intel® architecture. We cover how to accelerate the training of deep neural networks with Tensorflow, thanks to the highly optimized Intel® Math Kernel Library (Intel® MKL). We also demonstrate techniques on how to leverage deep neural network training on multiple nodes on a HPC cluster.
13:30 - 15:30
15:30 - 16:00 Coffee break
Basic knowledge of Python
Certificates of attendance for
All participants are expected to bring their own laptops.
Fabio Baruﬀa is a senior software technical consulting engineer at Intel. He provides customer support in the high performance computing (HPC) area and artificial intelligence software solutions at large scale. Prior to Intel, he has been working as HPC application specialist and developer in the largest supercomputing centers in Europe, mainly the Leibniz Supercomputing Centre and the Max-Plank Computing and Data Facility in Munich, as well as Cineca in Italy. He has been involved in software development, analysis of scientific code and optimization for HPC systems. He holds a PhD in Physics from University of Regensburg for his research in the area of spintronics devices and quantum computing.
|Registration:||Via the LRZ registration form. Please choose course HIDL1W19.|