GPU Programming with CUDA Training by AcadNXT is designed to provide participants with practical expertise in parallel computing and GPU acceleration using NVIDIA CUDA architecture. This instructor-led training program covers GPU fundamentals, CUDA programming model, thread hierarchy, memory management, kernel development, performance optimization, parallel algorithms, and debugging techniques. Participants will gain hands-on experience in writing CUDA kernels, optimizing computation workloads, and accelerating data-intensive applications for AI, machine learning, scientific computing, and high-performance computing (HPC) environments. The course is ideal for developers, data engineers, AI practitioners, and HPC professionals looking to leverage GPU acceleration for performance-critical applications.

• Understand GPU architecture and CUDA programming model
• Develop and execute CUDA kernels for parallel computing
• Manage GPU memory efficiently between host and device
• Implement optimized parallel algorithms
• Apply performance profiling and tuning techniques
• Debug and troubleshoot CUDA applications effectively
• Integrate CUDA with C++ applications
• Build high-performance GPU-accelerated solutions

• C++ Developers
• AI and Machine Learning Engineers
• Data Scientists
• HPC (High Performance Computing) Engineers
• Software Developers working on performance optimization
• Research Scientists
• Embedded and Systems Engineers
• IT Professionals interested in GPU acceleration

• Strong understanding of C/C++ programming
• Basic knowledge of data structures and algorithms is beneficial
• Familiarity with parallel computing concepts is helpful
• Understanding of basic computer architecture is advantageous

• Understanding GPU architecture and CUDA programming model
• Writing and executing CUDA kernels for parallel computation
• Managing threads, blocks, and grid structures effectively
• Optimizing memory usage and data transfer between CPU and GPU
• Implementing parallel algorithms for performance improvement
• Debugging and profiling CUDA applications
• Applying performance optimization techniques for GPU workloads
• Integrating CUDA with C++ applications
• Developing scalable GPU-accelerated solutions
• Applying best practices in high-performance computing

After completing the GPU Programming with CUDA Training, participants will be able to design and develop high-performance GPU-accelerated applications using CUDA. Learners will gain practical expertise in parallel programming, kernel development, memory optimization, performance tuning, and integration of CUDA with C++ for AI and HPC workloads.

Introduction to GPU Architecture and CUDA Basics

• Overview of GPU computing and CUDA ecosystem
• Understanding CPU vs GPU architecture
• CUDA programming model fundamentals
• Thread hierarchy: threads, blocks, and grids
• Setting up CUDA development environment

CUDA Programming Fundamentals

• Writing first CUDA kernel
• Memory allocation and management
• Host and device memory concepts
• Kernel execution and synchronization
• Basic CUDA program structure

Parallel Computing Concepts

• Introduction to parallel execution models
• Data parallelism vs task parallelism
• Execution configuration strategies
• Identifying parallelizable problems
• Performance considerations in GPU computing

Memory Management in CUDA

• Global, shared, and local memory concepts
• Memory transfer between host and device
• Optimization of memory access patterns
• Reducing memory bottlenecks
• Efficient memory usage techniques

Advanced CUDA Programming

• Multi-dimensional thread indexing
• Kernel optimization techniques
• Stream processing concepts
• Asynchronous execution in CUDA
• Overlapping computation and data transfer

Parallel Algorithms Implementation

• Parallel reduction techniques
• Vector and matrix operations
• Sorting and searching algorithms on GPU
• Image and signal processing basics
• Optimization of parallel workflows

Performance Optimization Techniques

• Profiling CUDA applications
• Identifying bottlenecks
• Optimizing memory bandwidth usage
• Occupancy optimization strategies
• Reducing kernel execution time

Debugging and Error Handling

• CUDA debugging tools overview
• Error handling mechanisms
• Common programming mistakes
• Memory leak detection
• Performance tuning strategies

CUDA Integration with C++ Applications

• Integrating CUDA with C++ projects
• Managing hybrid CPU-GPU workflows
• Library usage (cuBLAS, cuDNN overview)
• Real-world application structure
• Build and compilation workflows

High Performance Computing Concepts

• HPC use cases for CUDA
• Scientific computing applications
• AI/ML acceleration overview
• Distributed GPU computing concepts
• Scalability considerations

Mini Project and Practical Implementation

• Developing a GPU-accelerated application
• Implementing parallel computation logic
• Optimizing performance using CUDA techniques
• Debugging and profiling application
• Final project review and discussion

• GPU architecture and CUDA fundamentals
• Threading and memory hierarchy concepts
• Kernel development and execution
• Parallel algorithms and optimization techniques
• Memory management and performance tuning
• Debugging and profiling CUDA applications
• HPC and AI acceleration concepts
• CUDA mini project implementation

• Hands-on CUDA programming exercises
• Kernel development and optimization assignments
• Memory management and performance tuning tasks
• Parallel algorithm implementation activities
• Mini project development and evaluation
• Interactive debugging and HPC discussions

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in GPU Programming with CUDA Training, validating their expertise in GPU architecture, CUDA kernel development, parallel computing, performance optimization, HPC programming, and GPU-accelerated application engineering practices.