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Duration 28 hours
Course Outline
Introduction
- Definition and scope of CUDA
- Comparative analysis: CUDA vs. OpenCL vs. SYCL
- Architectural overview of CUDA features
- Configuring the development environment
Getting Started
- Initializing a new CUDA project in Visual Studio Code
- Analyzing the project structure and file organization
- Compiling and executing the application
- Outputting data using printf and fprintf functions
CUDA API
- The role of the CUDA API within the host program
- Querying device information and capabilities via the CUDA API
- Allocating and freeing device memory using the CUDA API
- Transferring data between host and device via the CUDA API
- Launching kernels and synchronizing threads using the CUDA API
- Managing errors and exceptions through the CUDA API
CUDA C/C++
- The role of CUDA C/C++ in device-side programming
- Writing kernels for GPU execution and data manipulation in CUDA C/C++
- Utilizing CUDA C/C++ data types, qualifiers, operators, and expressions
- Leveraging CUDA C/C++ built-in functions, including math, atomic, and warp operations
- Employing CUDA C/C++ built-in variables, such as threadIdx, blockIdx, and blockDim
- Integrating CUDA C/C++ libraries, such as cuBLAS, cuFFT, and cuRAND
CUDA Memory Model
- Distinguishing between host and device memory models
- Utilizing CUDA memory spaces, including global, shared, constant, and local memory
- Managing CUDA memory objects, such as pointers, arrays, textures, and surfaces
- Applying CUDA memory access modes, including read-only, write-only, and read-write
- Implementing the CUDA memory consistency model and synchronization mechanisms
CUDA Execution Model
- Distinguishing between host and device execution models
- Defining parallelism using CUDA threads, blocks, and grids
- Utilizing CUDA thread functions, such as threadIdx, blockIdx, and blockDim
- Employing CUDA block functions, such as __syncthreads and __threadfence_block
- Implementing CUDA grid functions, including gridDim, gridSync, and cooperative groups
Debugging
- Identifying common errors and bugs in CUDA programs
- Inspecting variables, breakpoints, and call stacks using the Visual Studio Code debugger
- Debugging CUDA programs on Linux using CUDA-GDB
- Detecting memory errors and leaks using CUDA-MEMCHECK
- Debugging and analyzing CUDA programs on Windows using NVIDIA Nsight
Optimization
- Identifying factors that impact CUDA program performance
- Enhancing memory throughput using CUDA coalescing techniques
- Reducing memory latency via CUDA caching and prefetching techniques
- Optimizing memory accesses and bandwidth using CUDA shared and local memory techniques
- Measuring and improving execution time and resource utilization using CUDA profiling tools
Requirements
- Proficiency in the C/C++ language and parallel programming concepts
- Foundational knowledge of computer architecture and memory hierarchy
- Experience with command-line tools and code editors
Audience
- Developers seeking to learn how to use CUDA to program NVIDIA GPUs and exploit their parallelism for government applications
- Developers aiming to write high-performance, scalable code capable of running on diverse CUDA devices
- Programmers interested in exploring low-level aspects of GPU programming and optimizing code performance
