GPU Programming with CUDA Training Course
CUDA is an open standard for GPU programming that allows developers to run code on NVIDIA GPUs, which are extensively used in high-performance computing, artificial intelligence (AI), gaming, and graphics. CUDA provides programmers with direct access to hardware details, giving them full control over the parallelization process. However, this also necessitates a strong understanding of device architecture, memory models, execution models, and optimization techniques.
This instructor-led, live training (online or onsite) is designed for beginner to intermediate level developers who want to use CUDA to program NVIDIA GPUs and leverage their parallel capabilities.
By the end of this training, participants will be able to:
- Set up a development environment that includes the CUDA Toolkit, an NVIDIA GPU, and Visual Studio Code.
- Create a basic CUDA program that performs vector addition on the GPU and retrieves the results from GPU memory.
- Use the CUDA API to query device information, allocate and deallocate device memory, transfer data between the host and device, launch kernels, and synchronize threads.
- Write kernels in CUDA C/C++ that execute on the GPU and manipulate data.
- Utilize CUDA built-in functions, variables, and libraries for common tasks and operations.
- Optimize data transfers and memory accesses using CUDA memory spaces such as global, shared, constant, and local.
- Control threads, blocks, and grids to define parallelism using the CUDA execution model.
- Debug and test CUDA programs using tools like CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
- Optimize CUDA programs with techniques such as coalescing, caching, prefetching, and profiling.
Format of the Course
- Interactive lecture and discussion.
- Plenty of exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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Course Outline
Introduction
- What is CUDA?
- CUDA vs OpenCL vs 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 the output using printf and fprintf
CUDA API
- Understanding the role of CUDA API in the host program
- Using CUDA API to query device information and capabilities
- Using CUDA API to allocate and deallocate device memory
- Using CUDA API to copy data between host and device
- Using CUDA API to launch kernels and synchronize threads
- Using CUDA API to handle errors and exceptions
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
- Using CUDA C/C++ data types, qualifiers, operators, and expressions
- Using CUDA C/C++ built-in functions, such as math, atomic, warp, etc.
- Using CUDA C/C++ built-in variables, such as threadIdx, blockIdx, blockDim, etc.
- Using CUDA C/C++ libraries, such as cuBLAS, cuFFT, cuRAND, etc.
CUDA Memory Model
- Understanding the difference between host and device memory models
- Using CUDA memory spaces, such as global, shared, constant, and local
- Using CUDA memory objects, such as pointers, arrays, textures, and surfaces
- Using CUDA memory access modes, such as read-only, write-only, read-write, etc.
- Using CUDA memory consistency model and synchronization mechanisms
CUDA Execution Model
- Understanding the difference between host and device execution models
- Using CUDA threads, blocks, and grids to define the parallelism
- Using CUDA thread functions, such as threadIdx, blockIdx, blockDim, etc.
- Using CUDA block functions, such as __syncthreads, __threadfence_block, etc.
- Using CUDA grid functions, such as gridDim, gridSync, cooperative groups, etc.
Debugging
- Understanding the common errors and bugs in CUDA programs
- Using Visual Studio Code debugger to inspect variables, breakpoints, call stack, etc.
- Using CUDA-GDB to debug CUDA programs on Linux
- Using CUDA-MEMCHECK to detect memory errors and leaks
- Using NVIDIA Nsight to debug and analyze CUDA programs on Windows
Optimization
- Understanding the factors that affect the performance of CUDA programs
- Using CUDA coalescing techniques to improve memory throughput
- Using CUDA caching and prefetching techniques to reduce memory latency
- Using CUDA shared memory and local memory techniques to optimize memory accesses and bandwidth
- Using CUDA profiling and profiling tools to measure and improve the execution time and resource utilization
Summary and Next Steps
Requirements
- An understanding of C/C++ language and parallel programming concepts
- Basic knowledge of computer architecture and memory hierarchy
- Experience with command-line tools and code editors
Audience
- Developers who wish to learn how to use CUDA to program NVIDIA GPUs and exploit their parallelism
- Developers who wish to write high-performance and scalable code that can run on different CUDA devices
- Programmers who wish to explore the low-level aspects of GPU programming and optimize their code performance
28 Hours
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