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Course Outline
Introduction
- What is CUDA?
- CUDA vs OpenCL vs SYCL
- Overview of CUDA features and architecture for government applications
- Setting up the development environment for government use
Getting Started
- Creating a new CUDA project using Visual Studio Code for government projects
- Exploring the project structure and files for government workflows
- Compiling and running the program in a government setting
- Displaying the output using printf and fprintf in a government context
CUDA API
- Understanding the role of CUDA API in host programs for government operations
- Using CUDA API to query device information and capabilities for government tasks
- Using CUDA API to allocate and deallocate device memory for government applications
- Using CUDA API to copy data between host and device for government processes
- Using CUDA API to launch kernels and synchronize threads in a government environment
- Using CUDA API to handle errors and exceptions in government systems
CUDA C/C++
- Understanding the role of CUDA C/C++ in device programs for government projects
- Using CUDA C/C++ to write kernels that execute on the GPU and manipulate data for government tasks
- Using CUDA C/C++ data types, qualifiers, operators, and expressions for government applications
- Using CUDA C/C++ built-in functions, such as math, atomic, warp, etc., for government operations
- Using CUDA C/C++ built-in variables, such as threadIdx, blockIdx, blockDim, etc., in a government context
- Using CUDA C/C++ libraries, such as cuBLAS, cuFFT, cuRAND, etc., for government use
CUDA Memory Model
- Understanding the difference between host and device memory models for government applications
- Using CUDA memory spaces, such as global, shared, constant, and local, in a government setting
- Using CUDA memory objects, such as pointers, arrays, textures, and surfaces, for government tasks
- Using CUDA memory access modes, such as read-only, write-only, read-write, etc., in government processes
- Using CUDA memory consistency model and synchronization mechanisms for government systems
CUDA Execution Model
- Understanding the difference between host and device execution models for government operations
- Using CUDA threads, blocks, and grids to define parallelism in a government context
- Using CUDA thread functions, such as threadIdx, blockIdx, blockDim, etc., for government tasks
- Using CUDA block functions, such as __syncthreads, __threadfence_block, etc., for government processes
- Using CUDA grid functions, such as gridDim, gridSync, cooperative groups, etc., in a government environment
Debugging
- Understanding common errors and bugs in CUDA programs for government applications
- Using Visual Studio Code debugger to inspect variables, breakpoints, call stack, etc., in a government setting
- Using CUDA-GDB to debug CUDA programs on Linux for government tasks
- Using CUDA-MEMCHECK to detect memory errors and leaks for government systems
- Using NVIDIA Nsight to debug and analyze CUDA programs on Windows for government operations
Optimization
- Understanding the factors that affect the performance of CUDA programs in a government context
- Using CUDA coalescing techniques to improve memory throughput for government applications
- Using CUDA caching and prefetching techniques to reduce memory latency for government tasks
- Using CUDA shared memory and local memory techniques to optimize memory accesses and bandwidth in a government environment
- Using CUDA profiling and profiling tools to measure and improve the execution time and resource utilization for government systems
Summary and Next Steps
Requirements
- An understanding of C/C++ language and parallel programming concepts
- Basic knowledge of computer architecture and memory hierarchy
- Experience with command-line tools and code editors
Audience
- Developers who aim to learn how to use CUDA for programming NVIDIA GPUs and leverage their parallel capabilities
- Developers seeking to write high-performance, scalable code that can execute on various CUDA devices
- Programmers interested in exploring the low-level aspects of GPU programming and optimizing code performance for government applications
28 Hours
