GPU Programming with CUDA Training Course

Introduction

• What is 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 in Visual Studio Code
• Exploring the project structure and file organization
• 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
• Copying data between host and device using the CUDA API
• Launching kernels and synchronizing threads with the CUDA API
• Handling errors and exceptions using the CUDA API

CUDA C/C++

• Understanding the role of CUDA C/C++ in device-side programming
• Writing kernels in CUDA C/C++ to execute on the GPU and manipulate data
• Using CUDA C/C++ data types, qualifiers, operators, and expressions
• Leveraging CUDA C/C++ built-in functions for math, atomics, warp operations, and more
• Utilizing CUDA C/C++ built-in variables such as threadIdx, blockIdx, and blockDim
• Working with CUDA C/C++ libraries including cuBLAS, cuFFT, and cuRAND

CUDA Memory Model

• Distinguishing between host and device memory models
• Using CUDA memory spaces: global, shared, constant, and local
• Managing CUDA memory objects such as pointers, arrays, textures, and surfaces
• Applying CUDA memory access modes like read-only, write-only, and read-write
• Understanding 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 and variables like threadIdx, blockIdx, and blockDim
• Employing CUDA block functions such as __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 that impact CUDA program performance
• Improving memory throughput with CUDA coalescing techniques
• Reducing memory latency using CUDA caching and prefetching techniques
• Optimizing memory access and bandwidth via CUDA shared and local memory techniques
• Measuring and improving execution time and resource utilization with CUDA profiling tools