CUDA in Chicago

Partnering with NVIDIA, this professional four day course is designed for programmers who are looking to develop comprehensive skills in writing and optimizing applications that fully leverage multi-core processing capabilities of GPUs.

Your fee includes

  • Use of a laptop equipped with CUDA capable GPU
  • Choice of Windows and Linux OS
  • Printed manual of all lectures
  • Electronic copy of lab exercises
  • Certificate of Completion
  • Beverages and snacks

Space is limited - Please register early to guarantee your spot

Your Instructor

Kelly Goss - Training Program Manager
Kelly is the Training Program Manager for Acceleware. She has taught over 25 parallel programming courses for students from a diverse range of industries and backgrounds. Kelly is also a member of Acceleware’s development team, assisting in the writing and optimization of Accceleware applications and providing consulting services to our clients. Kelly has a PhD in Electrical Engineering from the University of Calgary.


Tue-Fri: 9:00AM – 5:00PM (includes a 1 hour break for lunch)


  • Day 1:
    • Lecture: Overview of GPU Computing
    • Hands-on-Exercise: Memory Allocation and Memory Transfers
    • Lecture: Data-Parallel Architectures and the CUDA Programming Model
    • Hands-on-Exercise: Simple Kernels
    • Lecture: The CUDA Memory Model & Thread Cooperation
    • Hands-on-Exercise: Shared Memory and Constant Memory
  • Day 2:
    • Lecture: Textures
    • Hands-on-Exercise: Textures
    • Lecture: Asynchronous Operations
    • Hands-on-Exercise: Asynchronous Operations
    • Lecture: Other GPU Features
    • Lecture: CUDA Libraries
    • Hands-on-Exercise: CUDA Libraries
  • Day 3:
    • Lecture: Debugging Tools and Techniques
    • Hands-on-Exercise: Debugging Tools and Techniques
    • Lecture: Introduction to Optimization
    • Hands-on-Exercise: Arithmetic Optimization
    • Lecture: Resource Management, Latency and Occupancy
    • Hands-on-Exercise: Occupancy Calculator
  • Day 4 :
    • Lecture: Memory Performance Optimizations
    • Hands-on-Exercise: Memory Performance Optimizations
    • Lecture: Profiling CUDA/OpenCL Applications
    • Hands-on-Exercise: Profiling CUDA/OpenCL Applications
    • Lecture: Driver API
    • Hands-on-Exercise: Driver API

All lectures are a combination of teaching and hands-on tutorials

NVIDIA’s foundational training material is augmented with Acceleware’s experience over the past 8 years and with examples specific to an HPC audience

Online registration is now closed.

Course details

Date:Jan 29 to Feb 1, 2013
Registration closes:Jan 15, 2013 at 8:45PM (MST)
Location:Chicago (venue TBC)
Cost:$3,250 USD


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