In recent years, with the advancement of technology in the IT industry, we can see that several breakthrough domains have emerged, to which significant resources are directed for intensive and complex developments across a wide range of life areas.

Today, tech companies in both local and international markets are striving to provide advanced solutions for problem-solving, process optimization, and the creation of new products.

The challenge is that such advanced solutions, which take technology's involvement in our lives and its effectiveness to the next level, often require substantial resources and intensive computational power to deliver relevant services. This is where the CUDA platform comes into play.

The CUDA platform was developed by NVIDIA to harness the computing power of GPUs for performing tasks that demand intensive computation, performance, and more. In many fields, particularly image and video processing, the CPU's power is insufficient.

When developing solutions in fields like autonomous transportation, automated medical treatments, and more, we frequently need to bridge the gap between the code we've developed and the graphic card to obtain robust resources for software operations. Through learning development with the CUDA platform, we can precisely achieve this goal and explore a new realm of software development and IT work opportunities.

The CUDA course includes lectures and practical exercises:

  • Classroom exercises accompanied by explanations, assignments, and solutions on the course website.
  • Course booklet.
  • Videos and presentations on the course website.
  • Lectures take place once a week during evening hours.
  • Total academic study hours: 35 hours.

Cuda Course Content

Introduction to GPU Computing
Installing and first program development
Simple Matrix Multiplication
CUDA Memory Model
Accelerated Code on GPUs
Additional CUDA API Features
Useful Information on CUDA Tools
Threading Hardware
Memory Hardware
Linux GPU Debugging
Parallel Thread Execution
Talk to an Advisor

Alex Shoihat

Head of Machine Learning

Alex holds a bachelor's degree in Information Systems (B.Sc.) and a master's degree in Electrical and Electronics Engineering.

Alex is a Machine Learning Engineer at RT. He specializes in the AI field, with over 13 years of experience in project development, management, and transitioning from development to production in various domains such as Linux Embedded.

Alex has experience working with the integration of Machine Learning and Deep Learning in the Computer Vision and Data Analysis field.

Department Head
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