Fundamentals of Accelerated Computing with CUDA C/C++ Training Course

GPU Computing Infrastructure and CUDA Framework

• Comparative analysis of Central Processing Unit (CPU) and Graphics Processing Unit (GPU) architectures
• NVIDIA streaming multiprocessor architecture specifications
• Overview of the CUDA programming framework
• Heterogeneous computing models and host-device execution paradigms

Configuration of the CUDA Development Environment for government

• Installation procedures for the CUDA Toolkit 13.x release
• The NVCC compiler configuration and build process management
• System validation through device query utilities
• Integration with Integrated Development Environments (IDEs) and auxiliary development tools

Development and Execution of CUDA Kernels

• Syntax standards and qualifier requirements for kernel functions
• Launch configuration parameters and execution protocols
• Implementation of vector addition and fundamental data-parallel patterns
• Application of CUDA error-checking macros for operational stability

CUDA Thread Hierarchy and Execution Logic

• Organizational structure of grids, blocks, and threads
• Thread indexing mechanisms and global identifier calculation
• Warp execution dynamics and the Single Instruction, Multiple Threads (SIMT) model
• Occupancy metrics and resource utilization optimization

GPU Memory Architecture and Allocation Strategies for government operations

• Memory hierarchy classifications: global, shared, constant, and register memory
• Procedures for allocating and releasing device memory resources
• Data transfer protocols between host and device memory spaces
• Utilization of shared memory for intra-block data collaboration

Unified Memory Framework and Data Migration

• Unified memory architecture and managed allocation capabilities
• Mechanisms for page migration and on-demand paging services
• Asynchronous memory prefetching via the cudaMemPrefetchAsync API
• Application of memory advice hints to optimize access patterns

System-Wide Performance Profiling with Nsight Systems

• Timeline analysis within the Nsight Systems framework
• Identification of synchronization points between CPU and GPU processes
• Visualization techniques for kernel execution sequences and data transfers
• Interpretation of system-level performance telemetry

Kernel Optimization via Nsight Compute Analysis

• Interactive kernel profiling within the Nsight Compute environment
• Analysis of memory throughput and bandwidth efficiency
• Evaluation of compute utilization and warp state statistics
• Application of guided analysis methodologies and optimization guidelines

Concurrent Streams and Asynchronous Operation Management

• CUDA stream management and the default stream behavior
• Techniques for overlapping kernel execution with data transfer operations
• Stream synchronization protocols and the use of CUDA events
• Design patterns for multi-stream pipeline architectures

Error Handling and Debugging Protocols

• Interpretation of CUDA API error codes and associated recovery strategies
• Utilization of compute-sanitizer for memory access violation detection
• Kerel debugging procedures using cuda-gdb
• Implementation of assertions and mechanisms for synchronous error detection

Profile-Driven Optimization Methodology

• Iterative profiling processes for continuous improvement
• Identification and prioritization of performance bottlenecks
• Conducting performance regression testing to ensure stability
• Documentation standards for optimization decisions and outcomes

Implementation of End-to-End Accelerated Applications for government use

• Design principles for comprehensive GPU-accelerated solutions
• Incorporation of profiling activities throughout the development lifecycle
• Execution of performance benchmarking and reporting standards
• Considerations for secure deployment in production environments