NVIDIA CUDA Training

Acceleware CUDA Training Acceleware offers advanced CUDA training courses for NVIDIA GPUs delivered by the industry’s most experienced instructors. Since 2008, Acceleware has delivered detailed instruction to hundreds of programmers needing to achieve maximum performance from compute tasked GPUs. This is the industry leading course on GPU programming!

Clients will access our top rated training techniques for parallel programming in CUDA, OpenCL, MPI, Microsoft HPC Server, Visual Studio and many others. Acceleware's training consists of classroom lectures and several practical hands-on exercises using supplied laptops equipped with NVIDIA GPUs.

Your fee includes:

  • Use of a laptop equipped with CUDA capable GPU
  • Manual of all lectures
  • Electronic copy of lab exercises
  • Certificate of Completion

We recommend that the attendees have a background C/C++ (2 or more years) in order to get the most out of the course. Contact services@acceleware.com if you are interested in a beginner level CUDA courses.

Attendees should be familiar with the following C/C++ concepts:

  • Pointers and pointer to pointers (*, **)
  • Taking the address of a variable (&)
  • Writing functions, for loops, if/else statements
  • Printing to standard output (printf, cout)
  • Memory allocation and deallocation
  • Arrays and indexing
  • Structures
  • General debugging

Entirely optional (but helpful) experiences:

  • Multithreading
  • Optimization of programs
  • Low level programming (e.g., assembly languages)
  • Familiarity with computer architectures

4 Day Course Syllabus

  • 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 Applications
    • Hands-on-Exercise: Profiling CUDA Applications
    • Lecture: Driver API
    • Hands-on-Exercise: Driver API

Acceleware Past Training Course Customers
Customers of previous Acceleware CUDA Courses

Contact us for pricing information and to schedule your training session.