Computer Graphics Training in Durban

This instructor-led, live training in Durban (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use OpenACC to program heterogeneous devices and exploit their parallelism.

By the end of this training, participants will be able to:

• Set up an OpenACC development environment.
• Write and run a basic OpenACC program.
• Annotate code with OpenACC directives and clauses.
• Use OpenACC API and libraries.
• Profile, debug, and optimize OpenACC programs.

This instructor-led, live training in Durban (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to learn the basics of GPU programming and the main frameworks and tools for developing GPU applications.

• By the end of this training, participants will be able to:
  Understand the difference between CPU and GPU computing and the benefits and challenges of GPU programming.
• Choose the right framework and tool for their GPU application.
• Create a basic GPU program that performs vector addition using one or more of the frameworks and tools.
• Use the respective APIs, languages, and libraries to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use the respective memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses.
• Use the respective execution models, such as work-items, work-groups, threads, blocks, and grids, to control the parallelism.
• Debug and test GPU programs using tools such as CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize GPU programs using techniques such as coalescing, caching, prefetching, and profiling.

This instructor-led, live training in Durban (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use different frameworks for GPU programming and compare their features, performance, and compatibility.

By the end of this training, participants will be able to:

• Set up a development environment that includes OpenCL SDK, CUDA Toolkit, ROCm Platform, a device that supports OpenCL, CUDA, or ROCm, and Visual Studio Code.
• Create a basic GPU program that performs vector addition using OpenCL, CUDA, and ROCm, and compare the syntax, structure, and execution of each framework.
• Use the respective APIs to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use the respective languages to write kernels that execute on the device and manipulate data.
• Use the respective built-in functions, variables, and libraries to perform common tasks and operations.
• Use the respective memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses.
• Use the respective execution models to control the threads, blocks, and grids that define the parallelism.
• Debug and test GPU programs using tools such as CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize GPU programs using techniques such as coalescing, caching, prefetching, and profiling.

This instructor-led, live training in Durban (online or on-site) is designed for artists, game developers, and complete beginners who wish to utilise Blender to create 3D models for interactive applications, video games, animated films, and more.

Upon completion of this training, participants will be able to:

• Learn how to create animations and visual effects with Blender.
• Add curves, surfaces, metaballs, and hair particles to simulate realistic 3D motions.
• Gain an introduction to non-destructive modelling and animation.
• Export 3D models and assets to a game engine, 3D printer, or other software.

This instructor-led, live training in Durban (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to install and use ROCm on Windows to program AMD GPUs and exploit their parallelism.

By the end of this training, participants will be able to:

• Set up a development environment that includes ROCm Platform, a AMD GPU, and Visual Studio Code on Windows.
• Create a basic ROCm program that performs vector addition on the GPU and retrieves the results from the GPU memory.
• Use ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use HIP language to write kernels that execute on the GPU and manipulate data.
• Use HIP built-in functions, variables, and libraries to perform common tasks and operations.
• Use ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
• Use ROCm and HIP execution models to control the threads, blocks, and grids that define the parallelism.
• Debug and test ROCm and HIP programs using tools such as ROCm Debugger and ROCm Profiler.
• Optimize ROCm and HIP programs using techniques such as coalescing, caching, prefetching, and profiling.

This instructor-led, live training in Durban (online or onsite) targets beginner to intermediate-level developers eager to use ROCm and HIP to program AMD GPUs and harness their parallel computing power.

Upon completion of this training, participants will be able to:

• Configure a development environment comprising the ROCm Platform, an AMD GPU, and Visual Studio Code.
• Develop a fundamental ROCm program that executes vector addition on the GPU and retrieves results from GPU memory.
• Employ the ROCm API to query device information, allocate and deallocate device memory, transfer data between host and device, launch kernels, and synchronise threads.
• Utilise the HIP language to write kernels that execute on the GPU and manipulate data.
• Leverage HIP built-in functions, variables, and libraries to carry out common tasks and operations.
• Apply ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimise data transfers and memory accesses.
• Use ROCm and HIP execution models to manage the threads, blocks, and grids that define parallelism.
• Debug and test ROCm and HIP programs using tools such as ROCm Debugger and ROCm Profiler.
• Optimise ROCm and HIP programs through techniques including coalescing, caching, prefetching, and profiling.

This instructor-led live training in Durban (online or onsite) is aimed at artists, game developers, or complete beginners who wish to use Blender to create 3D models for interactive applications, video games, animated films, and more.

By the end of this training, participants will be able to:

• Understand the principles and core concepts of 3D modelling.
• Explore a variety of modes and tools for modelling and editing 3D meshes.
• Utilise tools for UV mapping, unwrapping, sculpting, painting, and rendering 3D models.

This instructor-led, live training in Durban (online or onsite) is geared towards beginner to intermediate graphic designers and animators who aspire to learn how to create impressive animations, interactive media, and engaging web content using Adobe Animate.

By the conclusion of this training, participants will be able to:

• Navigate the Adobe Animate interface and tools.
• Create and edit animations using keyframes, motion tweens, and shape tweens.
• Design interactive animations and applications with ActionScript and JavaScript.
• Incorporate audio and video elements into projects.
• Export animations for web, video, and mobile platforms.

Optimise neural network inference performance on Ascend AI processors with this advanced, instructor-led training in Durban. Delve into CANN's runtime architecture, leveraging the Graph Engine, TIK, and TVM for profiling, custom operator development, and resolving memory bottlenecks.

This instructor-led, live training in Durban (delivered online or onsite) targets developers from beginner to intermediate levels who wish to use CUDA to program NVIDIA GPUs and leverage their parallel processing power.

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, manage device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use the CUDA C/C++ language to write kernels that execute on the GPU and manipulate data.
• Leverage CUDA built-in functions, variables, and libraries to perform common tasks and operations.
• Utilize CUDA memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
• Apply the CUDA execution model to control threads, blocks, and grids that define parallelism.
• Debug and test CUDA programs using tools such as CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize CUDA programs using techniques such as coalescing, caching, prefetching, and profiling.

97% of clients are satisfied. 

This instructor-led, live training in Durban (online or on-site) is tailored for artists, game developers, or complete beginners who wish to utilise Blender to create 3D models for interactive applications, video games, animated films, and more.

By the end of this training, participants will be able to:

• Understand the principles and core concepts of 3D modelling.
• Explore a variety of modes and tools for modelling and editing 3D meshes.
• Learn how to create animations and visual effects with Blender.
• Add curves, surfaces, metaballs, and hair particles to simulate realistic 3D motions.
• Use the tools for UV mapping/unwrapping, sculpting, and painting 3D models.
• Export 3D models and assets to a game engine, 3D printer, or other software.

This instructor-led, live training in Durban (online or onsite) is aimed at beginner-level system administrators and IT professionals who wish to install, configure, manage, and troubleshoot CUDA environments.

By the end of this training, participants will be able to:

• Understand the architecture, components, and capabilities of CUDA.
• Install and configure CUDA environments.
• Manage and optimize CUDA resources.
• Debug and troubleshoot common CUDA issues.

This live training in Durban, delivered either online or onsite, targets developers from beginner to intermediate levels who want to leverage OpenCL to program heterogeneous devices and maximize their parallel processing power.

By the end of the program, participants will be able to:

• Establish a robust development environment incorporating the OpenCL SDK, OpenCL-compatible hardware, and Visual Studio Code.
• Construct basic OpenCL programs that perform vector addition on the device and retrieve results from device memory.
• Employ the OpenCL API to query device details and manage contexts, command queues, buffers, kernels, and events.
• Write data-manipulating kernels using OpenCL C that execute efficiently on the device.
• Implement common tasks and operations using OpenCL built-in functions, extensions, and libraries.
• Optimize data movement and memory access by applying knowledge of both host and device memory models.
• Control parallel execution parameters, including work-items, work-groups, and ND-ranges, via the OpenCL execution model.
• Debug and test OpenCL code using specialized tools like CodeXL, Intel VTune, and NVIDIA Nsight.
• Improve program efficiency through vectorization, loop unrolling, local memory optimization, and profiling.

This instructor-led, live training in Durban (online or onsite) is aimed at beginner-level to advanced-level 3D design and 3D printing enthusiasts who wish to use Fusion 360 to design, simulate, and prepare models for 3D printing.

By the end of this training, participants will be able to:

• Install and configure Fusion 360 for optimal performance.
• Design, model, and simulate 3D objects in a unified environment.
• Optimize and prepare designs for the 3D printing process.
• Collaborate and share their designs using Fusion 360's cloud capabilities.

This instructor-led live training in Durban (online or onsite) targets C++ developers who wish to use CUDA to accelerate applications, develop high-performance GPU kernels, and exploit parallel algorithm libraries for scientific computing, data processing, and machine learning workloads.

This instructor-led, live training in Durban (online or onsite) is aimed at C/C++ developers who wish to use CUDA to accelerate compute-intensive applications, including data processing, scientific simulations, machine learning workloads, and image processing pipelines.

This instructor-led, live training in Durban (online or onsite) is aimed at software developers, data analysts, and technical professionals who wish to use TensorFlow 2.x and Keras to build, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.

This instructor-led, live training course in Durban covers how to program GPUs for parallel computing, how to use various platforms, how to work with the CUDA platform and its features, and how to perform various optimization techniques using CUDA. Some of the applications include deep learning, analytics, image processing and engineering applications.