Course Outline
Introduction to GPU-Accelerated Computing
- Heterogeneous computing and the CPU-GPU architecture
- CUDA execution and memory spaces
- Compiling CUDA C++ with nvcc and CMake
- Verifying the GPU development environment
Parallel Algorithms with Thrust and CUB
- GPU-accelerated sort, reduce, and transform
- Refactoring STL algorithms for GPU execution
- Thrust device vectors and execution policies
- CUB device-wide primitives for custom pipelines
GPU Memory Architecture and Management
- Global, constant, and texture memory types
- Explicit device memory allocation and transfers
- Unified Memory for simplified data access
- Memory coalescing and access pattern optimization
Asynchronous Execution with CUDA Streams
- Creating and managing CUDA streams
- Overlapping kernel execution with data transfers
- CUDA events for dependency management
- Stream priorities and concurrency tuning
Writing Custom CUDA Kernels
- The SIMT programming model and warp execution
- Kernel launch configuration and grid-stride loops
- Thread indexing and multidimensional grids
- Error handling and CUDA runtime API checks
Thread Hierarchy and Execution Model
- Grids, blocks, and threads in device code
- Warp-level primitives and ballot operations
- Block-level synchronization and barriers
- Occupancy and resource utilization analysis
Cooperative Groups for Flexible Parallelism
- The cooperative_groups API and group types
- Thread-block tiles and tiled_partition
- Grid-level cooperative launches
- Multi-grid synchronization patterns
Shared Memory Optimization Techniques
- Shared memory banks and bank conflict avoidance
- Tiling strategies for matrix operations
- Shared memory as user-managed cache
- cuda::shared_memory_mdspan for multidimensional views
Kernel Fusion and Advanced Parallel Patterns
- Fusing multiple kernels to reduce launch overhead
- Scan, reduce-by-key, and segmented algorithms
- Atomic operations and lock-free data structures
- Warp-aggregated atomics for throughput
Profiling and Optimization with Nsight Systems
- Timeline analysis of CPU and GPU activity
- Identifying memory transfer bottlenecks
- Kernel performance and occupancy profiling
- Iterative optimization with Nsight Compute
Modern C++ Features in CUDA Device Code
- Lambdas, constexpr, and auto in kernels
- C++17 parallel algorithms and execution policies
- C++20 concepts and ranges on device
- C++23 support in nvcc and CCCL 3.x
CUDA Graphs and Advanced Asynchrony
- Defining and launching CUDA graphs
- Graph capture from stream execution
- Graph update and conditional execution nodes
- Reducing launch latency for iterative workloads
Integration Patterns for Existing Applications
- Wrapping GPU code behind C++ interfaces
- Managing multi-GPU and NUMA systems
- Build system integration with CMake and CUDA
- Debugging device code with cuda-gdb
Summary and Best Practices
- Choosing between Thrust, CUB, and custom kernels
- Performance portability across GPU architectures
- Code organization and RAII for CUDA resources
- Next steps and advanced CUDA learning paths
Requirements
- Basic C++ competency including lambda expressions, templates, and the STL
- Familiarity with standard algorithms, containers, and iterators
- Comfortable with loops, conditionals, and functions
- No prior CUDA or GPU programming knowledge required
Audience
- C++ developers seeking to accelerate compute-intensive applications with GPUs
- Software engineers transitioning from CPU-only to heterogeneous parallel programming
- Performance engineers and quantitative developers working with large datasets
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
Contact us for an exact quote and to hear our latest promotions
Testimonials (3)
Detailed explanation, re-iteration of points in a quite subtle way that really drove the knowledge home very well. Rod's willingness to double-check the odd obscure question that we rasied, to be sure that his answers were 100% right. Also, his interest in discussing the pros & cons of alternate coding styles so that we learnt not only how to use C++ in our intended way, but why it should be done that way.
Nick Dillon - cellxica Ltd
Course - Using C++ in Embedded Systems - Applying C++11/C++14
Experience sharing, it's teacher's know-how and valuable.
Carey Fan - Logitech
Course - C/C++ Secure Coding
The online-ness of it meant that we could save a lot of time. Much appreciated. Also, the fact that the trainer knew both c# and Cpp was a huge help since he could explain everything through knowledge we already possessed.
