GPU Programming with CUDA Training Course

Introduction

• An introduction to 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 host programs
• Using the CUDA API to query device information and capabilities
• Managing device memory allocation and deallocation 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 using the CUDA API

CUDA C/C++

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

CUDA Memory Model

• Understanding the differences between host and device memory models
• Working with CUDA memory spaces: global, shared, constant, and local
• Handling CUDA memory objects including pointers, arrays, textures, and surfaces
• Applying 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

• 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
• Applying 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 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
• Improving memory throughput using CUDA coalescing techniques
• Reducing memory latency through CUDA caching and prefetching
• Optimizing memory accesses and bandwidth using shared and local memory techniques
• Measuring and improving execution time and resource utilization using CUDA profiling and tools