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Duration 28 hours
Course Outline
Introduction
- What is OpenCL?
- Comparison: OpenCL vs CUDA vs SYCL
- Overview of OpenCL features and architecture
- Setting up the Development Environment
Getting Started
- Creating a new OpenCL project using Visual Studio Code
- Exploring the project structure and files
- Compiling and running the program
- Displaying output using printf and fprintf
OpenCL API
- Understanding the role of the OpenCL API in the host program
- Querying device information and capabilities using the OpenCL API
- Creating contexts, command queues, buffers, kernels, and events with the OpenCL API
- Enqueuing commands such as read, write, copy, map, unmap, execute, and wait via the OpenCL API
- Handling errors and exceptions using the OpenCL API
OpenCL C
- Understanding the role of OpenCL C in the device program
- Writing kernels in OpenCL C for device execution and data manipulation
- Using OpenCL C data types, qualifiers, operators, and expressions
- Applying OpenCL C built-in functions, including math, geometric, and relational operations
- Utilizing OpenCL C extensions and libraries, such as atomic, image, and cl_khr_fp16
OpenCL Memory Model
- Understanding the differences between host and device memory models
- Working with OpenCL memory spaces: global, local, constant, and private
- Using OpenCL memory objects: buffers, images, and pipes
- Applying OpenCL memory access modes: read-only, write-only, read-write, and more
- Implementing OpenCL memory consistency models and synchronization mechanisms
OpenCL Execution Model
- Understanding the differences between host and device execution models
- Defining parallelism using OpenCL work-items, work-groups, and ND-ranges
- Using OpenCL work-item functions: get_global_id, get_local_id, get_group_id, and others
- Applying OpenCL work-group functions: barrier, work_group_reduce, work_group_scan, and others
- Using OpenCL device functions: get_num_groups, get_global_size, get_local_size, and others
Debugging
- Identifying common errors and bugs in OpenCL programs
- Using the Visual Studio Code debugger to inspect variables, set breakpoints, and examine 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
- Identifying factors that impact OpenCL program performance
- 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 access and bandwidth using OpenCL local memory and local memory functions
- Measuring and improving execution time and resource utilization using OpenCL profiling and profiling tools
Requirements
- A solid understanding of C/C++ programming and parallel computing concepts.
- Basic knowledge of computer architecture and memory hierarchy.
- Proficiency with command-line interfaces and code editors.
Target Audience
- Developers looking to learn OpenCL for programming heterogeneous devices and exploiting their parallelism.
- Developers aiming to write portable and scalable code compatible with various platforms and devices.
- Programmers interested in exploring the low-level aspects of heterogeneous programming to optimize code performance.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 5200 € + VAT*
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