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Duration 28 hours
Course Outline
Introduction
- Understanding OpenCL fundamentals
- Comparing OpenCL with CUDA and SYCL
- Overview of OpenCL features and architectural design
- Setting up the Development Environment
Getting Started
- Creating a new OpenCL project using Visual Studio Code
- Exploring the project structure and file organization
- Compiling and executing the program
- Displaying output using printf and fprintf functions
OpenCL API
- Understanding the role of the OpenCL API within the host program
- Using the OpenCL API to query device information and capabilities
- Creating contexts, command queues, buffers, kernels, and events via the OpenCL API
- Enqueueing commands such as read, write, copy, map, unmap, execute, and wait using the OpenCL API
- Handling errors and exceptions with the OpenCL API
OpenCL C
- Understanding the role of OpenCL C within the device program
- Writing kernels in OpenCL C that execute on the device and manipulate data
- Utilizing OpenCL C data types, qualifiers, operators, and expressions
- Using OpenCL C built-in functions, including math, geometric, and relational operations
- Leveraging OpenCL C extensions and libraries, such as atomic operations, image handling, and cl_khr_fp16
OpenCL Memory Model
- Distinguishing between host and device memory models
- Utilizing OpenCL memory spaces, including global, local, constant, and private
- Working with OpenCL memory objects such as buffers, images, and pipes
- Applying OpenCL memory access modes like read-only, write-only, and read-write
- Implementing OpenCL memory consistency models and synchronization mechanisms
OpenCL Execution Model
- Distinguishing between host and device execution models
- Defining parallelism using OpenCL work-items, work-groups, and ND-ranges
- Using OpenCL work-item functions such as get_global_id, get_local_id, and get_group_id
- Employing OpenCL work-group functions like barrier, work_group_reduce, and work_group_scan
- Utilizing OpenCL device functions such as get_num_groups, get_global_size, and get_local_size
Debugging
- Identifying common errors and bugs in OpenCL programs
- Using the Visual Studio Code debugger to inspect variables, set breakpoints, and analyze the call stack
- Debugging and analyzing OpenCL programs on AMD devices using CodeXL
- Debugging and analyzing OpenCL programs on Intel devices using Intel VTune
- Debugging and analyzing OpenCL programs on NVIDIA devices using NVIDIA Nsight
Optimization
- Understanding factors that impact the performance of OpenCL programs
- Improving arithmetic throughput using OpenCL vector data types and vectorization techniques
- Reducing control overhead and increasing locality through OpenCL loop unrolling and loop tiling
- Optimizing memory accesses and bandwidth using OpenCL local memory and related functions
- Measuring and improving execution time and resource utilization using OpenCL profiling and specialized tools
Requirements
- A solid understanding of C/C++ language and parallel programming concepts
- Familiarity with computer architecture and memory hierarchy
- Experience with command-line tools and code editors
Target Audience
- Developers seeking to learn how to use OpenCL to program heterogeneous devices and exploit their parallelism
- Developers aiming to write portable and scalable code capable of running on diverse platforms and devices
- Programmers interested in exploring the low-level aspects of heterogeneous programming to optimize code performance
