GPU Programming with CUDA & Python Training by AcadNXT is designed to provide participants with practical expertise in accelerating computational workloads using NVIDIA CUDA along with Python-based GPU programming frameworks. This instructor-led training program covers GPU architecture, CUDA fundamentals, Python GPU programming with libraries like PyCUDA and Numba, kernel execution concepts, memory management, performance optimization, parallel computing techniques, and integration of GPU acceleration into Python applications. Participants will gain hands-on experience in building high-performance computing solutions for AI, machine learning, data processing, and scientific computing workloads using Python and CUDA. The course is ideal for developers, data scientists, AI engineers, and HPC professionals looking to leverage GPU acceleration through Python ecosystems.

• Understand GPU architecture and CUDA programming model
• Develop GPU-accelerated applications using Python
• Use PyCUDA and Numba for parallel computing
• Manage memory efficiently between CPU and GPU
• Implement and optimize parallel algorithms
• Profile and debug GPU applications effectively
• Integrate CUDA with Python-based workflows
• Build scalable GPU-accelerated solutions for AI and HPC

• Python Developers
• Data Scientists
• AI/ML Engineers
• HPC Engineers
• Software Developers working on performance optimization
• Research Scientists
• Analytics Engineers
• IT Professionals working with GPU-based systems

• Strong understanding of Python programming
• Basic knowledge of data structures and algorithms is beneficial
• Familiarity with linear algebra concepts is helpful
• Understanding of basic computer architecture is advantageous

• Understanding GPU architecture and CUDA programming model
• Writing GPU-accelerated Python programs using CUDA bindings
• Using PyCUDA and Numba for GPU computation
• Managing memory transfer between CPU and GPU
• Implementing parallel algorithms in Python
• Optimizing performance of GPU-based Python applications
• Debugging and profiling GPU workloads
• Integrating CUDA kernels with Python applications
• Building scalable GPU-accelerated data processing pipelines
• Applying best practices for high-performance Python computing

After completing the GPU Programming with CUDA & Python Training, participants will be able to design and develop GPU-accelerated applications using Python and CUDA frameworks. Learners will gain practical expertise in parallel programming, PyCUDA/Numba usage, memory optimization, performance tuning, and integrating GPU computing into AI, ML, and data processing workflows.

Introduction to GPU Computing and CUDA Basics

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

Python GPU Programming Fundamentals

• Introduction to PyCUDA and Numba
• Writing first GPU-accelerated Python program
• Memory allocation in Python GPU programming
• Data transfer between CPU and GPU
• Basic kernel execution concepts

Parallel Programming Concepts

• Understanding data parallelism
• Identifying parallel workloads
• Vectorized computation concepts
• Performance bottleneck analysis
• Optimization principles for GPU workloads

Memory Management in CUDA with Python

• Global and shared memory concepts
• Efficient memory transfer strategies
• Reducing latency in Python GPU applications
• Memory access optimization techniques
• Avoiding common memory bottlenecks

Advanced GPU Programming with Python

• Writing custom CUDA kernels in Python
• Multi-dimensional array processing
• Stream processing and asynchronous execution
• Overlapping computation and data transfer
• Advanced PyCUDA usage techniques

Performance Optimization Techniques

• Profiling GPU applications
• Identifying bottlenecks in Python GPU code
• Kernel optimization strategies
• Memory bandwidth optimization
• Execution efficiency improvements

Parallel Algorithms Implementation

• Matrix multiplication using GPU
• Vector operations and transformations
• Sorting and reduction techniques
• Data-intensive computation workflows
• Real-world HPC examples

Debugging and Error Handling

• Debugging GPU Python applications
• Handling CUDA runtime errors
• Memory leak detection techniques
• Performance tuning workflows
• Logging and monitoring GPU execution

Integration of CUDA with Python Applications

• Hybrid CPU-GPU application design
• Using Python libraries for GPU acceleration
• Integrating CUDA kernels into Python workflows
• Real-world application architecture
• Build and execution workflows

AI/ML and HPC Acceleration Concepts

• GPU acceleration for machine learning workloads
• Data pipeline optimization using GPU
• Scientific computing use cases
• Large-scale data processing concepts
• Scalability in GPU-based Python systems

Mini Project and Practical Implementation

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

• GPU architecture and CUDA fundamentals
• Python GPU programming with PyCUDA/Numba
• Kernel development and execution concepts
• Memory management and optimization techniques
• Parallel algorithms and computation models
• Debugging and performance profiling
• AI/ML GPU acceleration concepts
• GPU Python mini project implementation

• Hands-on GPU Python programming exercises
• CUDA kernel implementation tasks
• Memory management and optimization assignments
• Parallel algorithm development activities
• Mini project development and evaluation
• Interactive debugging and performance analysis 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 & Python Training, validating their expertise in GPU architecture, Python-based CUDA programming, parallel computing, performance optimization, and high-performance AI/HPC application development.