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Managed Memory -- Part 6 of 9 CUDA Training Series, June 18, 2020

Introduction

CUDA® is a parallel computing platform and programming model that extends C++ to allow developers to program GPUs with a familiar programming language and simple APIs.

NVIDIA will present a 9-part CUDA training series intended to help new and existing GPU programmers understand the main concepts of the CUDA platform and its programming model. Each part will include a 1-hour presentation and example exercises. The exercises are meant to reinforce the material from the presentation and can be completed during a 1-hour hands-on session following each lecture (for in-person participants) or on your own (for remote participants).  OLCF and NERSC will both be holding in-person events for each part of the series. Please see the topics of each part and dates here.

Part 6 of 9:  Managed Memory

Date and Time: 10 am - 12 pm (Pacific time), Thursday, June 18, 2020

The format of this event will be online only. Unified Memory creates a pool of managed memory that is shared between the CPU and GPU, bridging the CPU-GPU divide. Managed memory is accessible to both the CPU and GPU using a single pointer. The system will automatically migrate data between the host and device as needed. This module will show you how to allocate managed memory, as well as how to use it to store data structures like linked lists or C++ objects that might otherwise be difficult to manage manually in a heterogeneous system. 

After the presentation, there will be a hands-on session where participants can complete example exercises meant to reinforce the presented concepts and get help from the experts.

Registration

Please register here.  This event is online only. 

Remote Connection Information

Join Zoom Meeting
https://lbnl.zoom.us/j/5104865180?pwd=a2E2T0VMTFlpeHg5ODVGUTA0ajdyUT09
Meeting ID: 510 486 5180
Password: cuda 

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Presentation Materials 

  • Slides
  • Recording: TBA (to be posted shortly after the event)
  • Exercises: The example exercises for this module can be found in the "exercises/hw6" folder of this GitHub repo
  • Survey: Please help us to provide feedback by completing a 3-min short survey (access via the "Survey" tab located in the middle of this page)