CUDA and OpenCL Training

We provide high-quality 2- or 4-day CUDA™ and OpenCL training courses.

Since we specialize solely in CUDA and OpenCL work, we can uniquely immerse students in GPU and heterogeneous computing. Students of our courses walk away proficient at programming CUDA or OpenCL, receive the latest industry knowledge and techniques for GPU computing, and learn the tricks to maximize performance from heterogeneous computing devices.

For groups, we either travel to your location, host in our Atlanta office, or train remotely via video conference, tailoring our instruction to meet your application-specific needs. For individuals, we offer 2-day training quarterly.

We recommend that attendees have a working knowledge of C/C++ for a fruitful learning experience.

Talk to Us to Register a Group Training Course
individual students, register for an upcoming training session
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"Can't ask for better individualized instruction than the environment I was fortunate enough to encounter. The instructor was able to completely focus on my particular needs and concerns."

-Brian Rapp, US Army Research Lab

Included in All Courses

You provide the minds, and we'll take care of the rest. Each training comes with the following:

  • Instruction by an excellent and exciting expert
  • Hands-on exercises
  • Use of a laptop with CUDA and OpenCL capable GPUs and CPUs
  • Choice of Linux or Windows operating system
  • Printed manual of lecture material
  • Electronic copy of programming exercises
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CUDA and OpenCL Training Syllabus

* Courses are taught in either CUDA or OpenCL. Similar principles apply in each framework.

Day 1, Introduction

Lectures:

  • GPU Computing Overview
  • The Programming Model
  • Basic Dataset Mapping
  • Techniques
  • Libraries, ArrayFire
  • Profiling Tools

Practice:

  • A Simple Kernel
  • Equivalent ArrayFire Example
  • Using Libraries
  • Monte Carlo Pi Estimation
  • Timing and ArrayFire
  • Debugging Code
Day 2, Optimization

Lectures:

  • Architecture: Grids, Blocks, and Threads
  • Memory Model: Global, Shared, and Constant Memory
  • Advanced Mapping Techniques
  • Streams: Asynchronos Launches and Concurrent Execution
  • ArrayFire: Lazy Evaluation and Code Vectorization

Practice:

  • Matrix Transpose
  • Optimization Using Shared Memory
  • Median Filter
  • Optimization Using Constant Memory
  • Stream Example
  • ArrayFire Example: Nearest Neighbor Algorithm
Day 3, Multi-GPU

Lectures (customizable):

  • Multi-GPU Use Cases
  • Multi-GPUs: Contexts
  • Existing Libraries
  • Scaling Across Multiple GPUs

Practice:

  • Out of Core Problems: Matrix Multiply
  • Task Level Parallelism: Optimization
  • ArrayFire Multi-GPU
Day 4, Algorithm Problems

Lectures and Practice (customizable):

  • Reductions
  • Scan Algorithms
  • Sort
  • Convolution
  • Customer-Specific Problem
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