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Introduction to Graphics Processing

Course Summary

The Introduction to Graphics Processing course is designed to provide an overview of the technology landscape: how it is changing, how and why each of these approaches is used/useful, and under what circumstances they make business/technology sense.

The course begins with students learning to use FPGA/ASIC programming. Next, the course will cover environments/simulators and will also focus on deployment options, architectural choices, security issues, and other common problems. The course concludes with an introduction to the OpenCL/Vulkan ecosystem as a standards-based competitor to CUDA.

Purpose
 Learn a working knowledge of graphics processing practices within Machine Learning using CUDA, PyCuda, OpenCL, Vulkan, and Tensorflow.
Audience
 Developers who need to skill up quickly on Machine Learning and Graphics Processing technologies.
Role
Software Developer
Skill Level
Introduction
Style
Learning Spikes - Workshops
Duration
3 Days
Related Technologies
CUDA | Python | Tensorflow

 

Productivity Objectives
  • Describe the technology landscape including use of CPUs, GPUs, FPGA, ASIC, and CUDA.
  • Use Python Bindings to build, run and debug.
  • Compare OpenCL and Vulkan and describe reasons for using.
  • Identify how TensorFlow can accelerate machine-learning activities.
  • Discuss cloud-based options for accelerating GPU.

What You'll Learn:

In the Introduction to Graphics Processing training course, you'll learn:
  • Introduction: Review of the Technology Landscape
    • CPUs
    • GPUs
    • FPGA
    • ASIC
  • CUDA - PyCuda: Running Code on GPUs
    • Concepts
    • Python Bindings
    • Basic, reusable CUDA code examples
  • OpenCL/Vulkan
    • History & Comparison
    • APIs and SPIR-V format
    • Converting GLSL into SPIR-V
  • Machine Learning
    • TensorFlow and GPUs to accelerate machine-learning activities
    • Development processes
      • Building/running examples
      • How tasks are scheduled to CPUs/GPUs
      • Scaling strategies and limitations
      • Cluster / anomaly-detection use cases
  • Accelerating the Edge
    • Machine-learning in phones, browsers, etc.
    • Accelerating TensorFlow Lite
    • Accelerating Tensorflow.js
    • Accelerating WebAssembly
    • Accelerating WebGPU
  • Accelerating the Cloud
    • Google's TPUs
    • Microsoft Project Brainwave/FPGA
    • Amazon F1 (FPGA) Instances
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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By filling out this form and clicking submit, you acknowledge our privacy policy.