OpenCL Training by AcadNXT is designed to provide participants with practical expertise in heterogeneous parallel programming using Open Computing Language (OpenCL). This instructor-led training program covers OpenCL architecture, compute devices, platform model, kernels, memory hierarchy, work-item execution model, buffer management, synchronization, and performance optimization techniques. Participants will gain hands-on experience in writing OpenCL kernels and developing cross-platform parallel applications that run on CPUs, GPUs, DSPs, and other accelerators. The course is ideal for developers, HPC engineers, embedded systems engineers, and performance optimization specialists working with parallel computing workloads.

• Understand OpenCL architecture and execution model
• Develop and execute OpenCL kernels effectively
• Manage host and device memory efficiently
• Implement parallel computation using work-items and work-groups
• Optimize performance across heterogeneous devices
• Debug and troubleshoot OpenCL applications
• Integrate OpenCL into C/C++ applications
• Build portable high-performance computing solutions

• C/C++ Developers
• HPC Engineers
• GPU and Parallel Computing Developers
• Embedded Systems Engineers
• AI/ML Performance Engineers
• Scientific Computing Professionals
• Software Developers working on optimization
• Research and Development Engineers

• Strong understanding of C/C++ programming
• Basic knowledge of data structures and algorithms
• Familiarity with parallel computing concepts is beneficial
• Understanding of computer architecture basics is helpful

• Understanding OpenCL architecture and heterogeneous computing model
• Writing and executing OpenCL kernels
• Managing memory buffers across host and device
• Implementing parallel execution using work-items and work-groups
• Optimizing compute performance across devices
• Debugging and profiling OpenCL applications
• Integrating OpenCL into C/C++ applications
• Handling synchronization and concurrency in kernels
• Developing portable cross-platform compute applications
• Applying best practices for high-performance computing

After completing the OpenCL Training, participants will be able to design and develop cross-platform parallel applications using OpenCL. Learners will gain practical expertise in kernel programming, memory management, parallel execution, and performance optimization across heterogeneous computing devices.

Introduction to OpenCL and Heterogeneous Computing

• Overview of parallel computing and OpenCL ecosystem
• CPU vs GPU vs accelerator computing models
• OpenCL platform and execution model
• Devices, contexts, and command queues
• Setting up OpenCL development environment

OpenCL Programming Fundamentals

• Writing first OpenCL program
• Understanding kernels and host code interaction
• Work-items, work-groups, and NDRange concepts
• Kernel execution flow
• Basic OpenCL application structure

Memory Model and Data Management

• Host and device memory concepts
• Buffers and memory objects
• Data transfer between host and device
• Memory alignment and optimization basics
• Efficient memory usage techniques

Kernel Execution and Synchronization

• Kernel launch parameters
• Synchronization techniques
• Barrier operations in kernels
• Managing execution dependencies
• Debugging kernel execution

Advanced OpenCL Programming

• Multi-device execution concepts
• Task parallelism vs data parallelism
• Sub-buffering and memory sharing
• Optimizing kernel performance
• Advanced kernel design techniques

Performance Optimization Techniques

• Profiling OpenCL applications
• Identifying bottlenecks
• Memory bandwidth optimization
• Compute optimization strategies
• Reducing execution overhead

Integration and Real-World Applications

• Integrating OpenCL with C/C++ applications
• Use cases in AI, image processing, and HPC
• Cross-platform deployment considerations
• OpenCL vs CUDA comparison overview
• Best practices for production systems

Mini Project and Practical Implementation

• Developing a GPU-accelerated OpenCL application
• Implementing parallel computation kernels
• Optimizing memory and execution performance
• Debugging and testing OpenCL program
• Final project review and discussion

• OpenCL architecture and platform model
• Kernel programming and execution flow
• Memory management and buffer concepts
• Parallel execution and synchronization techniques
• Performance optimization strategies
• Multi-device computing concepts
• Debugging and profiling OpenCL applications
• OpenCL mini project implementation

• Hands-on OpenCL kernel programming exercises
• Memory management and buffer handling tasks
• Parallel computation implementation assignments
• Performance profiling and optimization activities
• Mini project development and evaluation
• Interactive debugging and architecture discussions

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in OpenCL Training, validating their expertise in heterogeneous computing, OpenCL kernel programming, parallel execution models, memory optimization, performance tuning, and cross-platform high-performance computing development.