Computer Graphics Training in Copenhagen

This live, instructor-led training in Copenhagen (available online or onsite) is designed for developers at beginner to intermediate levels who wish to utilize OpenACC to program heterogeneous devices and leverage their parallelism.

Upon completion, participants will be able to:

• Configure an OpenACC development environment.
• Develop and execute a fundamental OpenACC program.
• Annotate code using OpenACC directives and clauses.
• Utilize the OpenACC API and associated libraries.
• Profile, debug, and optimize OpenACC applications.

This instructor-led, live training in Copenhagen (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 live, instructor-led training in Copenhagen (available online or onsite) is tailored for developers from beginner to intermediate levels who wish to utilize various GPU programming frameworks and evaluate their features, performance, and compatibility.

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

• Establish a development environment that includes the OpenCL SDK, CUDA Toolkit, ROCm Platform, a device supporting OpenCL, CUDA, or ROCm, and Visual Studio Code.
• Create a basic GPU program performing vector addition using OpenCL, CUDA, and ROCm, and compare the syntax, structure, and execution flow of each framework.
• Use the corresponding APIs to query device details, manage device memory allocation and deallocation, copy data between host and device, launch kernels, and synchronize threads.
• Use the specific languages to write kernels that execute on the device and manipulate data.
• Utilize the respective built-in functions, variables, and libraries to carry out common tasks and operations.
• Apply the respective memory spaces, including 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 parallelism.
• Debug and test GPU programs using tools like CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize GPU programs by applying techniques such as coalescing, caching, prefetching, and profiling.

This instructor-led live training, delivered either online or onsite in Copenhagen, is designed for artists, game developers, and complete beginners who wish to utilize Blender to create 3D models for interactive applications, video games, animated films, and similar projects.

By the end of this training, participants will be capable of:

• Creating animations and visual effects with Blender.
• Adding curves, surfaces, metaballs, and hair particles to simulate realistic 3D motion.
• Grasping the fundamentals of non-destructive modelling and animation.
• Exporting 3D models and assets to game engines, 3D printers, or other software.

This instructor-led, live training is available online or on-site in Copenhagen and is designed for developers ranging from beginner to intermediate levels. The primary goal is to guide participants through installing and utilizing ROCm on Windows to program AMD GPUs and effectively harness their parallel processing capabilities.

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

• Configure a development environment on Windows that integrates ROCm Platform, an AMD GPU, and Visual Studio Code.
• Develop a fundamental ROCm application that executes vector addition on the GPU and retrieves the computed results from GPU memory.
• Leverage the ROCm API to query device specifications, manage device memory allocation and deallocation, transfer data between host and device, initiate kernels, and manage thread synchronization.
• Compose kernels using the HIP language to execute tasks and manipulate data directly on the GPU.
• Apply HIP built-in functions, variables, and libraries to streamline common operations and tasks.
• Utilize ROCm and HIP memory spaces—including global, shared, constant, and local—to enhance data transfer efficiency and optimize memory access patterns.
• Employ ROCm and HIP execution models to manage threads, blocks, and grids, thereby defining and controlling the degree of parallelism.
• Debug and test ROCm and HIP applications using specialized tools such as the ROCm Debugger and ROCm Profiler.
• Enhance the performance of ROCm and HIP programs by applying optimization techniques such as memory coalescing, caching, prefetching, and profiling.

This instructor-led, live training in Copenhagen (delivered online or onsite) is designed for developers from beginner to intermediate levels who intend to use ROCm and HIP to program AMD GPUs and exploit their parallelism.

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

• Set up a development environment featuring the ROCm Platform, an AMD GPU, and Visual Studio Code.
• Construct a basic ROCm program that performs vector addition on the GPU and fetches results from GPU memory.
• Utilize the ROCm API to query device information, manage device memory, copy data between host and device, launch kernels, and synchronize threads.
• Write kernels using the HIP language that execute on the GPU and manipulate data.
• Leverage HIP built-in functions, variables, and libraries to perform standard tasks and operations.
• Apply 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 defining parallelism.
• Debug and test ROCm and HIP programs using tools like the ROCm Debugger and ROCm Profiler.
• Optimize ROCm and HIP programs using techniques such as coalescing, caching, prefetching, and profiling.

This live, instructor-led training in Copenhagen (online or in person) is designed for artists, game developers, and beginners looking to utilize Blender for creating 3D models for interactive applications, video games, and animated films.

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

• Comprehend the key principles and core concepts of 3D modeling.
• Investigate a range of modes and tools for modeling and editing 3D meshes.
• Apply tools for UV mapping/unwrapping, sculpting, painting, and rendering 3D models.

This instructor-led, live training in Copenhagen (delivered online or onsite) targets graphic designers and animators from beginner to intermediate levels who aspire to produce captivating animations, interactive media, and engaging web content using Adobe Animate.

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

• Navigate the Adobe Animate interface and utilize its tools effectively.
• Create and modify animations using keyframes, motion tweens, and shape tweens.
• Design interactive animations and applications employing ActionScript and JavaScript.
• Incorporate audio and video elements into their projects.
• Export animations suitable for web, video, and mobile platforms.

This instructor-led, live training in Copenhagen (available online or onsite) is designed for beginner-to-intermediate developers aiming to use CUDA to program NVIDIA GPUs and exploit their parallelism.

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

• Establish a development environment featuring the CUDA Toolkit, an NVIDIA GPU, and Visual Studio Code.
• Develop a basic CUDA application that executes vector addition on the GPU and retrieves results from GPU memory.
• Leverage the CUDA API to query device information, manage device memory allocation and deallocation, transfer data between host and device, launch kernels, and synchronize threads.
• Utilize the CUDA C/C++ language to author kernels that run on the GPU and manipulate data.
• Apply CUDA built-in functions, variables, and libraries to execute common tasks and operations.
• Optimize data transfers and memory accesses by utilizing CUDA memory spaces such as global, shared, constant, and local.
• Manage the CUDA execution model to control the threads, blocks, and grids that dictate parallelism.
• Debug and test CUDA applications using tools like CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Enhance CUDA program performance through techniques including coalescing, caching, prefetching, and profiling.

97% of clients are satisfied. 

This live, instructor-led training, delivered in Copenhagen either online or on-site, is specifically designed for artists, game developers, and complete beginners seeking to use Blender to build 3D models for interactive applications, video games, and animated films.

By the conclusion of this program, participants will be capable of:

• Grasping the foundational principles and core ideas of 3D modeling.
• Exploring a wide array of modes and tools for modeling and manipulating 3D meshes.
• Learning to produce animations and visual effects within Blender.
• Implementing curves, surfaces, metaballs, and hair particles to achieve realistic 3D movements.
• Applying tools for UV mapping/unwrapping, sculpting, and texturing 3D models.
• Exporting 3D models and assets to game engines, 3D printers, or other software. 

Offered live by an instructor in Copenhagen (whether online or on-site), this training is tailored for novice system administrators and IT experts aiming to install, configure, administer, and troubleshoot CUDA environments.

By the conclusion of this program, learners will be equipped to:

• Comprehend the architecture, constituent elements, and potential of CUDA.
• Set up and configure CUDA environments.
• Administer and fine-tune CUDA resources.
• Diagnose and rectify standard CUDA problems.

This live training session, available in Copenhagen (online or onsite), is designed for developers with beginner to intermediate experience who want to use OpenCL to program heterogeneous devices and utilize their parallel potential.

By the end of the training, participants will be equipped to:

• Set up a development environment featuring the OpenCL SDK, an OpenCL-compatible device, and Visual Studio Code.
• Create a basic OpenCL program that performs vector addition on the device and retrieves results from device memory.
• Employ the OpenCL API to query device information and create contexts, command queues, buffers, kernels, and events.
• Use the OpenCL C language to write kernels that execute on the device and manipulate data.
• Leverage OpenCL built-in functions, extensions, and libraries for common tasks and operations.
• Apply OpenCL host and device memory models to optimize data transfers and memory access.
• Control work-items, work-groups, and ND-ranges using the OpenCL execution model.
• Debug and test OpenCL programs using tools such as CodeXL, Intel VTune, and NVIDIA Nsight.
• Optimize OpenCL programs using techniques like vectorization, loop unrolling, local memory management, and profiling.

This live, instructor-led training in Copenhagen (delivered online or on-site) is tailored for 3D design and printing enthusiasts, from novices to experts, who intend to use Fusion 360 for designing, simulating, and preparing models for 3D printing.

Upon completing this training, participants will be equipped to:

• Install and configure Fusion 360 to ensure peak performance.
• Design, model, and simulate 3D objects within a cohesive environment.
• Refine and prepare designs specifically for the 3D printing workflow.
• Collaborate and share designs by utilizing Fusion 360's cloud-based features.

This instructor-led, live training, available in Copenhagen (online or onsite), is tailored for C++ developers seeking to accelerate applications with CUDA. It focuses on developing high-performance GPU kernels and utilizing parallel algorithm libraries for tasks in scientific computing, data processing, and machine learning.

This live, instructor-led training in Copenhagen, offered online or onsite, is designed for C/C++ developers intending to use CUDA to boost the performance of compute-intensive applications, ranging from data processing and scientific simulations to machine learning workloads and image processing pipelines.

This live, instructor-led training in Copenhagen (delivered online or on-site) is tailored for software developers, data analysts, and technical professionals aiming to leverage 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 Copenhagen guides participants through GPU programming for parallel computing. It covers the utilization of various platforms, in-depth work with the CUDA platform and its features, and the application of optimization techniques using CUDA. Key application areas discussed include deep learning, analytics, image processing, and engineering solutions.