Accelerated C++ Programming with CUDA Training Course

Overview of GPU-Accelerated Computing Infrastructure

• Heterogeneous computing architectures and CPU-GPU integration models
• CUDA execution contexts and memory space management
• Compilation workflows for CUDA C++ using nvcc and CMake
• Validation procedures for GPU development environments for government applications

Parallel Algorithm Implementation with Thrust and CUB Libraries

• GPU-accelerated operations for sorting, reduction, and transformation
• Adaptation of Standard Template Library (STL) algorithms for GPU execution
• Management of Thrust device vectors and execution policies
• Utilization of CUB device-wide primitives for custom processing pipelines

GPU Memory Architecture and Resource Management

• Classification of global, constant, and texture memory types
• Explicit procedures for device memory allocation and data transfers
• Leveraging Unified Memory to streamline data access workflows
• Optimization of memory coalescing and access patterns for efficiency

Asynchronous Execution Management via CUDA Streams

• Procedures for creating and managing CUDA streams
• Strategies for overlapping kernel execution with data transfers
• Use of CUDA events to establish execution dependencies
• Tuning stream priorities and concurrency levels for optimal performance

Development of Custom CUDA Kernels

• Understanding the SIMT programming model and warp execution mechanics
• Configuration of kernel launch parameters and implementation of grid-stride loops
• Techniques for thread indexing within multidimensional grids
• Implementation of error handling protocols and CUDA runtime API validation

Thread Hierarchy and Execution Model Architecture

• Structure of grids, blocks, and threads in device code execution
• Application of warp-level primitives and ballot operations
• Execution of block-level synchronization and barrier mechanisms
• Analysis of occupancy rates and resource utilization metrics

Flexible Parallelism via Cooperative Groups

• Utilization of the cooperative_groups API and associated group types
• Implementation of thread-block tiles and tiled partitions
• Execution of grid-level cooperative launches
• Coordination of multi-grid synchronization patterns for distributed tasks

Optimization Techniques for Shared Memory

• Management of shared memory banks and prevention of bank conflicts
• Application of tiling strategies for matrix operations
• Configuration of shared memory as user-managed cache memory
• Utilization of cuda::shared_memory_mdspan for multidimensional data views

Kernel Fusion and Advanced Parallel Processing Patterns

• Techniques for f multiple kernels to minimize launch overhead
• Implementation of scan, reduce-by-key, and segmented algorithms
• Application of atomic operations and lock-free data structures
• Optimization of throughput through warp-aggregated atomic operations

Performance Profiling and Optimization with Nsight Systems

• Analysis of timeline data for CPU and GPU activity correlation
• Identification of bottlenecks related to memory transfers
• Profiling of kernel performance metrics and occupancy levels
• Iterative optimization processes utilizing Nsight Compute tools for government workloads

Integration of Modern C++ Features in CUDA Device Code

• Application of lambdas, constexpr variables, and auto type deduction within kernels
• Utilization of C++17 parallel algorithms and execution policies
• Incorporation of C++20 concepts and ranges for device-side operations
• Leverage of C++23 support features in nvcc and CCCL 3.x frameworks

CUDA Graphs and Advanced Asynchronous Workflows

• Procedures for defining and launching CUDA graphs
• Capture mechanisms for converting stream execution to graphs
• Modification of graph structures and management of conditional execution nodes
• Reduction of launch latency for iterative computational workloads

Integration Patterns for Legacy Application Modernization

• Encapsulation of GPU code behind robust C++ interfaces
• Management of multi-GPU configurations and Non-Uniform Memory Access (NUMA) systems
• Integration of build system processes with CMake and CUDA toolkits
• Debugging of device code using cuda-gdb for effective troubleshooting

Summary and Strategic Best Practices

• Criteria for selecting between Thrust, CUB, and custom kernel development
• Strategies for achieving performance portability across diverse GPU architectures
• Guidelines for code organization and Resource Acquisition Is Initialization (RAII) for CUDA resources
• Recommended next steps and advanced learning pathways for continued professional development