Online or onsite, instructor-led live GPU (Graphics Processing Unit) training courses demonstrate through interactive discussion and hands-on practice the fundamentals of GPU and how to program GPUs.
GPU training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live GPU trainings in Seoul can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Seoul - Gran Seoul
Jongno-gu, Tower 1, Gran Seoul, 33 Jong-ro, Spaces Gran Seoul, 7F, Seoul, korea, 01359
Gran Seoul is located on the 7th floor of 24-storey Gran Seoul Tower. The location is just a stone's throw away from Gwanghwa...
Gran Seoul is located on the 7th floor of 24-storey Gran Seoul Tower. The location is just a stone's throw away from Gwanghwamun, the largest gate of Gyeongbukgung Palace which is one of the most popular tourist attractions and national landmark in the heart of Seoul. Spaces Gran Seoul can be conveniently accessed by public transportation with Kwanghwamoon subway station located just 500 meters away and only a few minutes’ walk to Jonggak subway station. Also located nearby and within 15 minutes ride is Seoul Station where five lines of bullet train, subway, and airport train are started.
The Gran Seoul Tower is a grade-A commercial building combining office space, world class hotel, department store, major companies, finance enterprises, government offices and banks. Surrounded by historical and cultural facilities symbolizing Seoul’s pride, Gran Seoul is an ideal workspace for all businesses from start-ups to large international companies looking for established business location in the heart of Seoul's CBD.
Seoul Center 1
Jung-gu, 26, Eulji-ro 5-gil, 27/F, . West Center, Center1 Building, Seoul, korea, 04539
Center 1 is conveniently located at the core of Seoul's central business district (CBD). Center 1 is one of the most advanced...
Center 1 is conveniently located at the core of Seoul's central business district (CBD). Center 1 is one of the most advanced green building in Korea. Offers smartly landscaped park for rest and for a showcase of media technology as a front yard. It also features top class amenities such as prestigious retail shops and wide range of sophisticated restaurants (Italian, Japanese, Thai, Chinese, Korean, Indian, and Mexican) and breezy cafes. Business, dining, and shopping are under one roof at Center 1. Many financial institutions, investment banks, and insurance companies are located in close proximity, including the headquarter for the Bank of Korea. Seoul's political, public administration, media, cultural and economic venues are all within walking distance of Center 1. Also hotels, department stores and other amenities are within walking distance.
This instructor-led, live training in Seoul (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.
This instructor-led, live training in Seoul (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 Seoul (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 Seoul (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 Seoul (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.
This instructor-led, live training in Seoul (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use CUDA to program NVIDIA GPUs and exploit their parallelism.By the end of this training, participants will be able to:
Set up a development environment that includes CUDA Toolkit, a NVIDIA GPU, and Visual Studio Code.
Create a basic CUDA program that performs vector addition on the GPU and retrieves the results from the GPU memory.
Use CUDA API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
Use CUDA C/C++ language to write kernels that execute on the GPU and manipulate data.
Use CUDA built-in functions, variables, and libraries to perform common tasks and operations.
Use CUDA memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
Use CUDA execution model to control the threads, blocks, and grids that define the 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.
This instructor-led, live training in Seoul (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use OpenCL to program heterogeneous devices and exploit their parallelism.By the end of this training, participants will be able to:
Set up a development environment that includes OpenCL SDK, a device that supports OpenCL, and Visual Studio Code.
Create a basic OpenCL program that performs vector addition on the device and retrieves the results from the device memory.
Use OpenCL API to query device information, create contexts, command queues, buffers, kernels, and events.
Use OpenCL C language to write kernels that execute on the device and manipulate data.
Use OpenCL built-in functions, extensions, and libraries to perform common tasks and operations.
Use OpenCL host and device memory models to optimize data transfers and memory accesses.
Use OpenCL execution model to control the work-items, work-groups, and ND-ranges.
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 Seoul (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.
This course covers how to program GPUs for parallel computing. Some of the applications include deep learning, analytics, and engineering applications.
This instructor-led, live training course in Seoul 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.
This instructor-led, live training in Seoul (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.
By the end of this training, participants will be able to:
Install and configure the necessary development environment, software and libraries to begin developing.
Build, train, and deploy deep learning models to analyze live video feeds.
Identify, track, segment and predict different objects within video frames.
Optimize object detection and tracking models.
Deploy an intelligent video analytics (IVA) application.
This instructor-led, live training in Seoul (online or onsite) is aimed at developers who wish to use CUDA to build Python applications that run in parallel on NVIDIA GPUs.
By the end of this training, participants will be able to:
Use the Numba compiler to accelerate Python applications running on NVIDIA GPUs.
Create, compile and launch custom CUDA kernels.
Manage GPU memory.
Convert a CPU based application into a GPU-accelerated application.
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Testimonials (1)
Very interactive with various examples, with a good progression in complexity between the start and the end of the training.
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