GPU Training in Gaia

This instructor-led, live training in Gaia (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 Gaia (online or onsite) is designed for beginner to intermediate developers wishing to learn the essentials of GPU programming and the main frameworks and tools for developing GPU applications.

• By the conclusion of this training, participants will be able to:
  Grasp the distinctions between CPU and GPU computing, as well as the advantages and challenges associated with GPU programming.
• Select the most appropriate framework and tool for their specific GPU application.
• Develop a fundamental GPU program that executes vector addition using one or more of the discussed frameworks and tools.
• Utilise the relevant APIs, languages, and libraries to query device details, allocate and deallocate device memory, transfer data between host and device, launch kernels, and synchronise threads.
• Leverage the corresponding memory spaces—such as global, local, constant, and private—to optimise data transfers and memory access patterns.
• Apply the respective execution models, including work-items, work-groups, threads, blocks, and grids, to manage parallelism effectively.
• Debug and test GPU programs using tools such as CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimise GPU programs employing techniques like coalescing, caching, prefetching, and profiling.

This instructor-led, live training in Gaia (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 Gaia (online or onsite) is designed for beginner to intermediate 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, an 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 Gaia (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use ROCm and HIP 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.
• 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.

Elevate neural network inference performance on Ascend AI processors through this advanced, instructor-led training in Gaia. Delve into the CANN runtime architecture, utilising the Graph Engine, TIK, and TVM for profiling, custom operator creation, and resolving memory bottlenecks.

This instructor-led live training, delivered in Gaia (either online or onsite), is tailored for developers from beginner to intermediate levels who wish to leverage CUDA for programming NVIDIA GPUs and utilizing their parallel capabilities.

Upon completion, participants will be able to:

• Establish a development environment featuring the CUDA Toolkit, an NVIDIA GPU, and Visual Studio Code.
• Create a fundamental CUDA program that executes vector addition on the GPU and retrieves the outcomes from GPU memory.
• Employ the CUDA API to query device details, manage memory allocation and deallocation, transfer data between host and device, launch kernels, and synchronize threads.
• Develop kernels that run on the GPU and manipulate data using CUDA C/C++.
• Utilize CUDA built-in functions, variables, and libraries to carry out standard tasks and operations.
• Apply CUDA memory spaces, including global, shared, constant, and local, to enhance data transfer and memory access efficiency.
• Control threads, blocks, and grids via the CUDA execution model to define parallelism.
• Debug and test CUDA programs using utilities such as CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Enhance CUDA program performance using techniques like coalescing, caching, prefetching, and profiling.

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This instructor-led live training in Gaia (online or onsite) is designed for 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:

• Comprehend 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, instructor-led training in Gaia, available online or onsite, is designed for beginner to intermediate-level developers who want to utilize OpenCL for programming heterogeneous devices and leveraging their parallel capabilities.

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

• Set up a development environment that includes the OpenCL SDK, a supported OpenCL device, and Visual Studio Code.
• Create a basic OpenCL program that performs vector addition on the device and retrieves results from device memory.
• Use the OpenCL API to query device information and create contexts, command queues, buffers, kernels, and events.
• Write kernels in OpenCL C that execute on the device and manipulate data.
• Use OpenCL built-in functions, extensions, and libraries to perform common tasks and operations.
• Optimize data transfers and memory accesses using OpenCL host and device memory models.
• 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 such as vectorization, loop unrolling, local memory, and profiling.

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

This instructor-led, live training in Gaia (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 Gaia (online or on-site) 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 Gaia explains how to program GPUs for parallel computing, utilize diverse platforms, navigate the CUDA platform and its capabilities, and execute various optimization techniques with CUDA. Key application areas include deep learning, analytics, image processing, and engineering solutions.