GPU Programming with CUDA Training Course

Introduction

• What constitutes CUDA?
• Comparison of CUDA with OpenCL and SYCL
• Overview of CUDA features and architecture
• Setting up the Development Environment

Getting Started

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

CUDA API

• Understanding the role of the CUDA API within the host program
• Querying device information and capabilities using the CUDA API
• Allocating and deallocating device memory via the CUDA API
• Transferring data between host and device using the CUDA API
• Launching kernels and synchronizing threads with the CUDA API
• Handling errors and exceptions through the CUDA API

CUDA C/C++

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

CUDA Memory Model

• Distinguishing between host and device memory models
• Applying CUDA memory spaces such as global, shared, constant, and local
• Utilizing CUDA memory objects including pointers, arrays, textures, and surfaces
• Managing CUDA memory access modes such as 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
• Using CUDA thread functions such as threadIdx, blockIdx, and blockDim
• Applying 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
• Inspecting variables, breakpoints, and call stacks using the Visual Studio Code debugger
• Debugging CUDA programs on Linux with CUDA-GDB
• Detecting memory errors and leaks using CUDA-MEMCHECK
• Debugging and analyzing CUDA programs on Windows using NVIDIA Nsight

Optimization

• Understanding factors influencing CUDA program performance
• Improving memory throughput with CUDA coalescing techniques
• Reducing memory latency through 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 via CUDA profiling tools