GPU Programming with CUDA Training Course

Introduction

• Definition and scope of CUDA.
• Comparison of CUDA, OpenCL, and SYCL.
• Overview of CUDA features and architecture.
• Setting up the Development Environment.

Getting Started

• Creating a new CUDA project in Visual Studio Code.
• Exploring project structure and files.
• Compiling and executing the program.
• Displaying output using printf and fprintf.

CUDA API

• The role of the CUDA API in host-side programming.
• Querying device information and capabilities via the CUDA API.
• Managing device memory allocation and deallocation using the CUDA API.
• Transferring data between host and device using the CUDA API.
• Launching kernels and synchronizing threads through the CUDA API.
• Handling errors and exceptions with the CUDA API.

CUDA C/C++

• The role of CUDA C/C++ in device-side programming.
• Writing kernels in CUDA C/C++ for GPU execution and data manipulation.
• Using CUDA C/C++ data types, qualifiers, operators, and expressions.
• Employing CUDA C/C++ built-in functions, including math, atomic, and warp operations.
• Utilizing CUDA C/C++ built-in variables such as threadIdx, blockIdx, and blockDim.
• Working with CUDA C/C++ libraries like cuBLAS, cuFFT, and cuRAND.

CUDA Memory Model

• Differences between host and device memory models.
• Using CUDA memory spaces: global, shared, constant, and local.
• Working with CUDA memory objects such as pointers, arrays, textures, and surfaces.
• Applying CUDA memory access modes, including read-only, write-only, and read-write.
• Understanding the CUDA memory consistency model and synchronization mechanisms.

CUDA Execution Model

• Differences between host and device execution models.
• Defining parallelism using CUDA threads, blocks, and grids.
• Using CUDA thread functions such as threadIdx, blockIdx, and blockDim.
• Employing CUDA block functions like __syncthreads and __threadfence_block.
• Utilizing CUDA grid functions including gridDim, gridSync, and cooperative groups.

Debugging

• Identifying common errors and bugs in CUDA programs.
• Using the Visual Studio Code debugger to inspect variables, set breakpoints, and review the call stack.
• Debugging CUDA programs on Linux with CUDA-GDB.
• Detecting memory errors and leaks using CUDA-MEMCHECK.
• Debugging and analyzing CUDA programs on Windows with NVIDIA Nsight.

Optimization

• Factors influencing CUDA program performance.
• Improving memory throughput with CUDA coalescing techniques.
• Reducing memory latency through CUDA caching and prefetching.
• Optimizing memory access and bandwidth using CUDA shared and local memory.
• Measuring and improving execution time and resource utilization with CUDA profiling tools.