GPU Programming with CUDA Training Course

Introduction

• What is 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 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 via the CUDA API
• Allocating and deallocating device memory using the CUDA API
• Copying data between host and device using the CUDA API
• Launching kernels and synchronizing threads via the CUDA API
• Handling errors and exceptions using 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
• 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, such as cuBLAS, cuFFT, and cuRAND

CUDA Memory Model

• Understanding the distinctions between host and device memory models
• Using 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, such as read-only, write-only, and read-write
• Utilizing the CUDA memory consistency model and synchronization mechanisms

CUDA Execution Model

• Understanding the differences between host and device execution models
• Defining parallelism using CUDA threads, blocks, and grids
• Using CUDA thread functions like threadIdx, blockIdx, and blockDim
• Using CUDA block functions such as __syncthreads and __threadfence_block
• Using CUDA grid functions like 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 analyze 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

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