Udemy - GPU Acceleration in C - Write Faster Code with CUDA

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Free Download Udemy - GPU Acceleration in C - Write Faster Code with CUDA
Published: 4/2025
Created by: Rakhi Pundir
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 8 Lectures ( 49m ) | Size: 373 MB

Boost Performance with Parallel Computing
What you'll learn
Understand GPU Acceleration and Parallel Computing
Write and optimize CUDA Programs
Implement Real-Worls Parallel Algorithms
Analyze & Compare CPU vs. GPU Performance
Requirements
Basics of C language are good to have, otherwise, Beginners are also welcome
Description
GPU Acceleration in C - Write Faster Code with CUDA Are your C programs running slow on large datasets? Do you want to harness the power of GPUs to speed up computations? This course is designed to help you understand and implement parallel programming with NVIDIA CUDA to significantly improve performance. Whether you are working on scientific simulations, AI models, or high-performance computing (HPC) tasks, this course will provide you with the essential knowledge to get started with CUDA. What You'll Learn: - Understand GPU architecture and why it is faster than traditional CPUs for parallel tasks. - Learn CUDA programming from scratch with hands-on examples that demonstrate key concepts. - Implement parallel algorithms, such as matrix multiplication, and analyze their efficiency. - Compare CPU vs. GPU performance using real-time benchmarks and performance metrics. - Optimize CUDA programs to achieve maximum efficiency and speed for your applications. Who is This Course For?- C and C++ Developers who want to accelerate existing code or write optimized algorithms. - AI/ML Enthusiasts looking to optimize deep learning models or image processing tasks. - HPC Professionals working on computationally intensive problems in scientific research. - Students and Researchers exploring parallel computing for applications like radar signal processing, simulations, and data-intensive tasks. What You'll Need: - A basic understanding of C programming, including loops, functions, and pointers. - A system with an NVIDIA GPU or access to a cloud-based GPU instance. By the end of this course, you will be able to write, optimize, and benchmark CUDA programs, allowing you to take full advantage of GPU acceleration for high-performance computing tasks. This course will equip you with the practical skills needed to integrate CUDA into your projects, making your programs significantly faster and more efficient. If you are ready to explore parallel computing and unlock the potential of GPU acceleration, enroll today and take your programming skills to the next level.
Who this course is for
C & C++ Developers, HPC(High -Performance Computing) Professionals, AI & Machine Learning Enthusiasts
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