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Duration 28 hours
Course Outline
Introduction
- Defining OpenCL
- Comparing OpenCL with CUDA and SYCL
- Overview of OpenCL features and architectural components
- Configuring the Development Environment
Getting Started
- Initializing a new OpenCL project in Visual Studio Code
- Examining the project structure and file organization
- Compiling and executing the application
- 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 retrieve 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
- Handling errors and exceptions using the OpenCL API
OpenCL C
- Understanding the role of OpenCL C in device-side programming
- Writing kernels in OpenCL C to execute on the device and manipulate data
- Utilizing OpenCL C data types, qualifiers, operators, and expressions
- Applying OpenCL C built-in functions for math, geometry, and relations
- Working with OpenCL C extensions and libraries, including atomic operations and image processing
OpenCL Memory Model
- Distinguishing between host and device memory models
- Working with OpenCL memory spaces: global, local, constant, and private
- Managing OpenCL memory objects such as buffers, images, and pipes
- Applying OpenCL memory access modes like read-only, write-only, and read-write
- Understanding the OpenCL memory consistency model and synchronization techniques
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 like get_global_id, get_local_id, and get_group_id
- Applying OpenCL work-group functions such as barrier, work_group_reduce, and work_group_scan
- Utilizing OpenCL device functions like get_num_groups, get_global_size, and get_local_size
Debugging
- Identifying common errors and bugs in OpenCL applications
- Using the Visual Studio Code debugger to inspect variables, breakpoints, and call stacks
- Debugging and analyzing OpenCL programs on AMD devices with CodeXL
- Debugging and analyzing OpenCL programs on Intel devices with Intel VTune
- Debugging and analyzing OpenCL programs on NVIDIA devices with NVIDIA Nsight
Optimization
- Analyzing factors that impact OpenCL program performance
- Enhancing arithmetic throughput using OpenCL vector data types and vectorization techniques
- Reducing control overhead and improving locality through OpenCL loop unrolling and tiling
- Optimizing memory access and bandwidth using OpenCL local memory and associated functions
- Measuring and improving execution time and resource utilization using OpenCL profiling tools
Requirements
- Familiarity with C/C++ programming and the principles of parallel processing
- Fundamental knowledge of computer architecture and memory hierarchy
- Practical experience with command-line utilities and code editors
Target Audience
- Developers seeking to harness OpenCL for programming heterogeneous devices and exploiting their parallel power
- Engineers aiming to develop portable, scalable code compatible with multiple platforms and devices
- Programmers interested in exploring the low-level intricacies of heterogeneous computing to enhance code performance
