Exploring the Best Server for AI Speaker: Samuel D. Matzek, Sr. Software Engineer Speaker: Maria Ward, IBM Accelerated Server Offering Manager Explore the server at the heart of the Summit and Sierra supercomputers, and the best server for AI. We will discuss the technical details that set this server apart and why it matters for your machine learning and deep learning workloads. IBM Cloud for AI at Scale Speaker: Alex Hudak, IBM Cloud Offering Manager AI is fast changing the modern enterprise with new applications that are resource demanding, but provide new capabilities to drive insight from customer data. IBM Cloud is partnering with NVIDIA to provide a world class and customized cloud environment to meet the needs of these new applications. Learn about the wide range of NVIDIA GPU solutions inside the IBM Cloud virtual and bare metal server portfolio, and how customers are using them across Deep Learning, Analytics, HPC workloads, and more. IBM Spectrum LSF Family Overview & GPU Support Speaker: Larry Adams, Global Architect - Cross Sector, Developer, Consultant, IBM Systems How to Fuel the Data Pipeline Speaker: Kent Koeninger, IBM IBM Storage Reference Architecture for AI with Autonomous Driving Speaker: Kent Koeninger, IBM
We'll introduce the Microsoft open source, production-grade deep learning Cognitive Toolkit (formerly CNTK) in a talk that will be a prelude to a detailed hands-on tutorial. The Cognitive Toolkit was used recently to achieve a major breakthrough in speech recognition by reaching human parity in conversational speech. The toolkit has been powering use cases leveraging highly performant GPU platforms. It is being used by several customers both on-premises and on Azure cloud. We'll introduce different use cases leveraging fully connected CNN, RNN/LSTM, auto encoders, and reinforcement learning. We'll deep dive into topics that enable superior performance of the toolkit in comparison with similar open source toolkits. We'll showcase scalability across multiple GPUs and multiple servers. We'll provide a teaser hands-on experience with Jupyter notebooks running on Azure with simple introductory to very advanced end-to-end use cases.
This tutorial is for those with a basic understanding of CUDA who want to learn about the GPU memory model and optimal storage locations. Attend session 1, "An Introduction to GPU Programming," to learn the basics of CUDA programming that are required for Session 2. We'll begin with an essential overview of the GPU architecture and thread cooperation before focusing on different memory types available on the GPU. We'll define shared, constant, and global memory, and discuss the best locations to store your application data for optimized performance. We'll deliver a programming demonstration of shared and constant memory. We'll also provide printed copies of the material to all attendees for each session ? collect all four!
This tutorial builds on the two previous sessions ("An Introduction to GPU Programming" and "An Introduction to GPU Memory Model") and is intended for those with a basic understanding of CUDA programming. This tutorial dives deep into asynchronous operations and how to maximize throughput on both the CPU and GPU with streams. We'll demonstrate how to build a CPU/GPU pipeline and how to design your algorithm to take advantage of asynchronous operations. In the second part of the session, we'll focus on dynamic parallelism. We'll deliver a programming demo involving asynchronous operations. We'll also provide printed copies of the material to all attendees for each session - collect all four!