GPU Training in Bangkok

This live, instructor-led training in Bangkok (online or onsite) is designed for developers with beginner to intermediate experience who wish to use OpenACC to program heterogeneous devices and maximize their parallel processing capabilities.

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

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

This instructor-led, live training conducted in Bangkok (available online or onsite) is tailored for beginner to intermediate developers looking to master the basics of GPU programming and the essential frameworks for developing GPU applications.

• By the end of this training, participants will be able to:
  Differentiate between CPU and GPU computing, recognizing the specific advantages and complexities of GPU workflows.
• Select the optimal framework and tooling for their GPU application.
• Build a basic GPU program that executes vector addition using one or more of the available frameworks.
• Utilize specific APIs, languages, and libraries to query device details, manage memory allocation, handle host-to-device data copies, launch kernels, and synchronize threads.
• Apply memory spaces such as global, local, constant, and private to enhance data transfer efficiency and memory access.
• Leverage execution models, including work-items, work-groups, threads, blocks, and grids, to control parallel execution.
• Debug and test GPU code using tools like CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize GPU programs using techniques like coalescing, caching, prefetching, and profiling.

This instructor-led live training, conducted in Bangkok (online or onsite), is designed for developers from beginner to intermediate levels who intend to employ different GPU programming frameworks and assess their features, performance, and compatibility.

Upon completing this training, participants will be able to:

• Establish a development environment that includes the OpenCL SDK, CUDA Toolkit, and ROCm Platform, along with a supporting device and Visual Studio Code.
• Construct a basic GPU program for vector addition using OpenCL, CUDA, and ROCm, comparing the syntax, structure, and execution of each framework.
• Utilize the respective APIs to query device information, manage memory allocation and deallocation, transfer data between host and device, launch kernels, and synchronize threads.
• Write kernels using the respective languages to execute on the device and manipulate data.
• Employ the respective built-in functions, variables, and libraries to perform common tasks and operations.
• Utilize the respective memory spaces, including global, local, constant, and private, to optimize data transfers and memory accesses.
• Apply the respective execution models to control threads, blocks, and grids that define parallelism.
• Debug and test GPU programs using tools such as CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize GPU programs using techniques like coalescing, caching, prefetching, and profiling.

This instructor-led live training in Bangkok (available online or on-site) is designed for developers from beginner to intermediate levels who want to install and use ROCm on Windows to program AMD GPUs and leverage their parallelism.

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

• Establish a development environment on Windows featuring the ROCm Platform, an AMD GPU, and Visual Studio Code.
• Create a basic ROCm program that performs vector addition on the GPU and retrieves results from GPU memory.
• Use the ROCm API to query device details, manage device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use the HIP language to write kernels that execute on the GPU and manipulate data.
• Employ HIP built-in functions, variables, and libraries to perform standard tasks and operations.
• Utilize ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory access.
• Apply ROCm and HIP execution models to manage the threads, blocks, and grids that define parallelism.
• Debug and test ROCm and HIP programs using tools like the ROCm Debugger and ROCm Profiler.
• Optimize ROCm and HIP programs using techniques including coalescing, caching, prefetching, and profiling.

This instructor-led, live training in Bangkok (available online or onsite) is designed for beginner to intermediate developers looking to program AMD GPUs using ROCm and HIP to maximize parallel processing capabilities.

Upon completion, participants will be able to:

• Establish a development environment comprising the ROCm Platform, an AMD GPU, and Visual Studio Code.
• Build a fundamental ROCm program that performs vector addition on the GPU and extracts results from GPU memory.
• Use the ROCm API to query device details, manage memory allocation, transfer host-device data, launch kernels, and synchronize threads.
• Write GPU-executed kernels using the HIP language to manipulate data.
• Employ HIP built-in functions, variables, and libraries for standard tasks and operations.
• Leverage ROCm and HIP memory spaces, such as global, shared, constant, and local, to streamline data transfers and memory access.
• Utilize ROCm and HIP execution models to control threads, blocks, and grids that structure the parallelism.
• Debug and test ROCm and HIP applications using tools like the ROCm Debugger and ROCm Profiler.
• Optimize ROCm and HIP programs through techniques including coalescing, caching, prefetching, and profiling.

Elevate neural network inference performance on Ascend AI processors with this advanced, instructor-led course in Bangkok. Delve into CANN's runtime architecture, utilizing the Graph Engine, TIK, and TVM for comprehensive profiling, custom operator creation, and resolving memory constraints.

This instructor-led live training in Bangkok (available online or on-site) targets developers from beginner to intermediate levels who wish to leverage CUDA to program NVIDIA GPUs and utilize their parallelism.

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.
• Build a basic CUDA program that executes vector addition on the GPU and retrieves results from GPU memory.
• Use the CUDA API to query device information, manage device memory allocation and deallocation, transfer data between host and device, launch kernels, and synchronize threads.
• Use CUDA C/C++ to write kernels that execute on the GPU and manipulate data.
• Employ 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 like CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize CUDA programs using techniques such as coalescing, caching, prefetching, and profiling.

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This instructor-led live training in Bangkok (delivered online or on-site) is tailored for beginner-level system administrators and IT professionals looking to install, configure, manage, and troubleshoot CUDA environments.

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

• Comprehend the architecture, components, and capabilities of CUDA.
• Install and configure CUDA environments efficiently.
• Manage and optimize CUDA resources effectively.
• Debug and resolve common CUDA issues.

This instructor-led live training in Bangkok, offered online or onsite, is tailored for developers with beginner to intermediate experience who intend to use OpenCL to program heterogeneous devices and harness their parallel capabilities.

Upon completing this training, participants will be equipped to:

• Set up a development environment incorporating the OpenCL SDK, compatible OpenCL devices, and Visual Studio Code.
• Build a basic OpenCL program that performs vector addition on the device and retrieves results from device memory.
• Utilize the OpenCL API to query device information and create contexts, command queues, buffers, kernels, and events.
• Employ the OpenCL C language to write kernels for device execution and data manipulation.
• Apply OpenCL built-in functions, extensions, and libraries to handle 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 via 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 management, and profiling.

This instructor-led live training in Bangkok (offered online or on-site) is tailored for C++ developers who intend to use CUDA to accelerate applications, develop high-performance GPU kernels, and apply parallel algorithm libraries to scientific computing, data processing, and machine learning workflows.

This live, instructor-led training in Bangkok (available online or onsite) is designed for C/C++ developers seeking to leverage CUDA to boost the performance of compute-intensive applications, covering areas such as data processing, scientific simulations, machine learning operations, and image processing pipelines.

This live training in Bangkok, available online or onsite, targets software developers, data analysts, and technical professionals aiming to utilize TensorFlow 2.x and Keras to build, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.

Join this Bangkok instructor-led live session to delve into GPU programming for parallel computing. You will learn to utilize diverse platforms, harness the capabilities of the CUDA platform, and implement effective optimization strategies. The course highlights practical applications in deep learning, analytics, image processing, and engineering.