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AI in Accelerated Computing

NVIDIA AI Essentials · Accelerated computing

Make a numerical job run on a GPU, then find the step that is still slow

RIA’s AI in Accelerated Computing course in Chennai teaches parallel work on a GPU: how threads share a job, how data moves between the processor and the GPU, and how a profile shows the slow step.

You practise in Python and in modern C++. This is a programming course, not a hardware-purchase guide and not a vendor certificate.

The subject map follows the public self-paced catalogue at NVIDIA training. RIA teaches these subjects in Chennai. NVIDIA courses, cloud labs and certificates stay with NVIDIA. Completion here is a RIA record.

Classroom batches run at Purasawalkam and Saligramam, Chennai. Call +91 98843 63200.

What you will learn

  • Why a GPU is a grid of workers, not a faster CPU
  • Launching a simple parallel kernel and checking the result
  • Moving arrays between host memory and device memory
  • Python acceleration for numerical loops
  • Parallel patterns you can write in standard C++
  • Reading a profile to see whether the code or the data move is slow
  • A before-and-after timing table you can defend
  • What not to parallelise

Full program syllabus & curriculum

Domain literacy + applied AI labs + responsible review + capstone

  • AI in Accelerated Computing

    We teach parallel GPU work: a kernel, the memory move, and a profile. The subject map follows the public self-paced catalogue at NVIDIA training. RIA teaches these subjects in Chennai. NVIDIA courses, cloud labs and certificates stay with NVIDIA. Completion here is a RIA record.

    Domain Knowledge We Teach
    • Threads, blocks and why the result must still be checked
    • Host memory, device memory and the cost of copying
    • Jobs that belong on a CPU
    • What a profiler is showing
    • Libraries versus code you write yourself
    AI Skills You Will Learn
    • Write a small parallel numerical kernel and test it
    • Time the copy and the compute separately
    • Accelerate a Python loop and compare it with the plain version
    • Read a profile and name one change
    Hands-on Outcomes
    • A notebook or program that is faster for a stated input size
    • A one-page timing table
    • A profile note a teammate can act on
    • A spoken explanation without vendor slogans

Common questions

  • Is this NVIDIA’s CUDA certificate?

    No. You learn the same kind of subject. The certificate, if you want NVIDIA’s own, is taken with NVIDIA. RIA issues a RIA record.

  • Do I need C++?

    Python is enough to start. C++ sessions are included so you can read accelerated code, not so you must arrive as a C++ engineer.

  • Will I build a full deep-learning framework?

    No. You accelerate a numerical job and learn to profile it. Model training sits in AI in Deep Learning.

  • Where is the course?

    RIA Purasawalkam and RIA Saligramam, Chennai. Call +91 98843 63200.

Skills you build

  • A small kernel with a correct result
  • A timing table: CPU, GPU, and the copy cost
  • A profile that names the slow step
  • A note of jobs that should stay on the CPU

Who this is for

  • Python developers who hit a slow loop
  • Engineering students in computing or electronics
  • Data scientists who want to know why a GPU library is fast
  • Developers preparing for the data-science or deep-learning specialization

Enquire about AI in Accelerated Computing

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Ready to start AI in Accelerated Computing?

Enquire for batch dates at Purasawalkam or Saligramam. This is a RIA course. NVIDIA certificates stay with NVIDIA.

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