GPU Programming with CUDA Training Course

Introduction

• Defining CUDA
• Comparing CUDA with OpenCL and SYCL
• Overview of CUDA features and architecture
• Setting up the Development Environment

Getting Started

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

CUDA API

• Understanding the role of the CUDA API in the host program
• Using the CUDA API to query device information and capabilities
• Using the CUDA API to allocate and deallocate device memory
• Using the CUDA API to copy data between host and device
• Using the CUDA API to launch kernels and synchronize threads
• Handling errors and exceptions using the CUDA API

CUDA C/C++

• Understanding the role of CUDA C/C++ in the device program
• Using CUDA C/C++ to write kernels that execute on the GPU and manipulate data
• Utilizing CUDA C/C++ data types, qualifiers, operators, and expressions
• Using CUDA C/C++ built-in functions, such as math, atomic, and warp operations
• Using CUDA C/C++ built-in variables, such as threadIdx, blockIdx, and blockDim
• Leveraging CUDA C/C++ libraries, including cuBLAS, cuFFT, and cuRAND

CUDA Memory Model

• Distinguishing between host and device memory models
• Utilizing CUDA memory spaces, including global, shared, constant, and local
• Working with CUDA memory objects such as pointers, arrays, textures, and surfaces
• Applying CUDA memory access modes like 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
• Using CUDA threads, blocks, and grids to define parallelism
• Applying CUDA thread functions such as threadIdx, blockIdx, and blockDim
• Utilizing CUDA block functions like __syncthreads and __threadfence_block
• Employing CUDA grid functions such as gridDim, gridSync, and cooperative groups

Debugging

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

Optimization

• Understanding factors affecting the performance of CUDA programs
• Applying CUDA coalescing techniques to improve memory throughput
• Using CUDA caching and prefetching techniques to reduce memory latency
• Optimizing memory accesses and bandwidth using CUDA shared and local memory techniques
• Measuring and improving execution time and resource utilization through CUDA profiling and profiling tools