CUDA Programming Training
Introduction to CUDA
Learn the fundamentals of CUDA (Compute Unified Device Architecture) programming. Understand the architecture of CUDA-enabled GPUs and how they differ from traditional CPUs.
CUDA Programming Model
Explore the CUDA programming model, including the concept of kernels, threads, blocks, and grids. Learn how to write and manage CUDA kernels to perform parallel computations.
CUDA Memory Management
Study memory management techniques in CUDA programming. Understand the different types of memory (global, shared, constant, and texture memory) and how to optimize memory usage for better performance.
CUDA Kernels and Thread Organization
Learn how to design and implement CUDA kernels. Explore thread organization, synchronization, and how to manage thread execution to achieve efficient parallel processing.
Error Handling and Debugging
Understand common errors and issues in CUDA programming. Learn debugging techniques and tools to identify and resolve problems in your CUDA applications.
Optimizing CUDA Performance
Explore techniques for optimizing the performance of CUDA applications. Study strategies for improving computational efficiency, memory access patterns, and reducing latency.
Parallel Algorithms and Libraries
Learn about parallel algorithms and libraries available for CUDA programming. Explore CUDA libraries such as cuBLAS, cuFFT, and Thrust, and understand how to leverage them for various applications.
Applications of CUDA Programming
Study real-world applications of CUDA programming, including high-performance computing, machine learning, and data processing. Learn how CUDA is used to accelerate complex computations and improve application performance.
Case Studies and Practical Exercises
Engage in case studies and practical exercises to apply CUDA programming concepts. Work on real-world projects and scenarios to gain hands-on experience and reinforce your learning.
Future Trends in CUDA Programming
Explore emerging trends and advancements in CUDA programming. Learn about the latest developments in GPU technology and how they are shaping the future of parallel computing.
CUDA Programming syllabus
Introduction to GPU Computing and CUDA
- Overview of GPU Architecture and CUDA Programming Model
- Evolution and Benefits of GPU Computing
- CUDA Programming Paradigm and Basic Concepts
CUDA Programming Basics
- Setting Up CUDA Development Environment (CUDA Toolkit, IDE)
- Writing and Compiling CUDA Programs
- Understanding CUDA Threads, Blocks, and Grids
Memory Hierarchy in CUDA
- Overview of CUDA Memory Model (Global, Shared, Constant, and Local Memory)
- Memory Allocation and Management in CUDA
- Optimization Techniques for Memory Access Patterns
CUDA Thread Coordination
- Synchronization and Communication Between CUDA Threads
- Thread Divergence and Warp Execution Model
- Utilizing Thread Synchronization Primitives (e.g., Barriers, Locks)
CUDA Kernel Optimization
- Techniques for Optimizing CUDA Kernels (Memory Coalescing, Loop Unrolling)
- Performance Considerations and Profiling Tools (nvprof)
- Hands-on Exercises in Optimizing CUDA Code
Advanced CUDA Memory Management
- Unified Memory and Managed Memory in CUDA
- Asynchronous Memory Operations and Data Transfers
- Best Practices for Memory Usage in CUDA Applications
CUDA Libraries and Utilities
- Overview of CUDA-Accelerated Libraries (cuBLAS, cuFFT, cuDNN)
- Integrating CUDA Libraries into Applications
- Using CUDA Thrust for High-Level GPU Programming
Multi-GPU Programming with CUDA
- Scalable Parallelism with Multiple GPUs
- CUDA Multi-GPU Programming Techniques (MPI, CUDA-aware MPI)
CUDA Applications and Case Studies
- Real-World Applications of CUDA in Various Domains (e.g., Scientific Computing, Deep Learning)
- Case Studies of CUDA-Accelerated Projects and Success Stories
CUDA and Deep Learning
- Overview of CUDA Support in Deep Learning Frameworks (e.g., TensorFlow, PyTorch)
- Accelerating Neural Network Training and Inference with CUDA
- Implementing Custom Layers and Optimizations in CUDA
CUDA and Image Processing
- GPU-Accelerated Image Processing Techniques with CUDA
- Implementing Filters, Transformations, and Feature Extraction Using CUDA
- Case Studies in CUDA-Powered Image Processing Applications
CUDA and Parallel Algorithms
- Parallel Algorithm Design and Implementation in CUDA
- Implementing Parallel Sorting, Reduction, and Other Algorithms
- Analyzing Performance and Scalability of Parallel Algorithms
CUDA and Real-Time Systems
- CUDA Applications in Real-Time and Embedded Systems
- Challenges and Considerations for Real-Time CUDA Programming
- Case Studies in Real-Time CUDA Applications
CUDA and High Performance Computing (HPC)
- CUDA in High-Performance Computing (HPC) Clusters
- Optimizing CUDA Applications for Large-Scale Distributed Computing
- Managing Data Locality and Communication Overhead in CUDA HPC
Future Trends in CUDA and GPU Computing
- Emerging Technologies and Advancements in CUDA
- GPU Architectures and Trends in Parallel Computing
- Exploring CUDA for AI, IoT, and Other Emerging Fields
Capstone Project (if applicable)
- Design and Implementation of a CUDA-Accelerated Application
- Project-Based Learning with Mentorship and Feedback
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details