Delivered either online or onsite, these instructor-led live training programs explore the essentials of GPU (Graphics Processing Unit) technology through interactive dialogue and practical exercises. Participants will gain a solid understanding of GPU fundamentals and learn how to effectively program them.
The curriculum is offered as "online live training" (also known as "remote live training") or "onsite live training." Remote sessions are conducted via an interactive remote desktop, while onsite sessions take place locally at client premises in Athens or at NobleProg corporate training centers in Athens.
Take Metro Line 3 (M3) from Airport toward Dimotiko Theatro.
Get off at Evangelismos.
The address is on Vasilissis Sofias Avenue, very close to the station. Evangelismos station is directly on the avenue.
Allow roughly 40–50 minutes total, depending on waiting/walking time.
From Athens Central Railway Station (Larissa Station)
Take the metro to Syntagma and change to Line 3 toward Doukissis Plakentias/Airport.
Get off at Evangelismos.
Walk to No. 46.
By bus
There are several buses running along Vasilissis Sofias Avenue. Nearby stops include Rigilis Street and Syntagma, while the Evangelismos area is served by multiple bus routes.
The metro is generally the simplest option, especially if you're arriving with luggage.
This instructor-led course, categorized under Athens, empowers intermediate AI engineers to construct and refine neural network models using the Huawei Ascend platform and CANN toolkit. Learners will gain skills in configuring environments, building applications with MindSpore, and executing deployments in edge or cloud contexts.
This instructor-led live session in Athens delves into Huawei's AI ecosystem, covering the spectrum from the CANN SDK to the MindSpore framework. It assists novice and mid-level professionals in grasping how these elements function together on Ascend hardware to streamline lifecycle management and deployment.
This instructor-led, live training in Athens (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 training in Athens guides you through deploying and optimizing CV and NLP models using the CANN SDK on Ascend hardware. Learners will master model conversion, integration into live pipelines, and boosting inference performance for real-time detection and analysis.
This instructor-led, live training in Athens (online or onsite) is designed for beginner to intermediate developers who want to learn the fundamentals of GPU programming, including the main frameworks and tools used for developing GPU applications.
By the end of this training, participants will be able to: Understand the differences between CPU and GPU computing, along with the benefits and challenges of GPU programming.
Choose the most suitable 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.
Utilize specific memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses.
Apply the respective execution models, such as work-items, work-groups, threads, blocks, and grids, to control 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 Athens empowers advanced developers to build, deploy, and optimize custom AI operators. Participants will gain mastery over CANN TIK and Apache TVM integration, facilitating advanced optimization and scheduling on Huawei Ascend hardware to achieve real-world performance goals.
This instructor-led, live training in Athens (online or onsite) is designed for beginner to intermediate developers wishing to utilize different GPU programming frameworks and compare their features, performance, and compatibility.
Upon completion, participants will be able to:
Configure a development environment encompassing the OpenCL SDK, CUDA Toolkit, ROCm Platform, a compatible device (supporting OpenCL, CUDA, or ROCm), and Visual Studio Code.
Develop a fundamental GPU program performing vector addition using OpenCL, CUDA, and ROCm, while comparing the syntax, structure, and execution of each framework.
Utilize the respective APIs to query device information, manage device memory allocation and deallocation, transfer data between host and device, launch kernels, and synchronize threads.
Write device-side kernels using the respective languages to manipulate data.
Leverage respective built-in functions, variables, and libraries for common tasks and operations.
Employ specific memory spaces (global, local, constant, private) to optimize data transfers and memory access.
Use the respective execution models to control threads, blocks, and grids defining parallelism.
Debug and test GPU programs using tools such as CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
Enhance GPU program performance using techniques like coalescing, caching, prefetching, and profiling.
This instructor-led session on Athens presents CloudMatrix for scalable AI inference. Master the deployment, optimization, and monitoring of models using CANN and MindSpore. Practical exercises encompass packaging, conversion, serving, and performance tuning for both real-time and batch workloads.
This instructor-led live training in Athens delves into the core principles and practical fundamentals of deploying AI models on Ascend edge devices via the CANN toolkit, enabling participants to develop essential skills for compiling, optimizing, and managing constrained environments.
This instructor-led, live training in Athens (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 Athens (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 live training in Athens offers an introduction to the CANN toolkit, specifically tailored for AI framework developers. It guides you through setting up the necessary environments, converting models, and deploying applications on Ascend hardware using MindSpore, TensorFlow, or PyTorch, covering the complete workflow from initial training to final inference.
Enhance AI workloads on Ascend, Biren, and Cambricon through this hands-on training in Athens. Master model benchmarking, bottleneck identification, and the application of graph, kernel, and operator-level optimizations. Optimize deployment pipelines to improve throughput and latency across these leading platforms.
Refine the inference performance of neural networks on Ascend AI processors through this advanced, instructor-led course in Athens. Delve into the CANN runtime architecture while utilizing the Graph Engine, TIK, and TVM to master profiling, develop custom operators, and eliminate memory-related bottlenecks.
Migrate CUDA applications to Chinese GPU architectures such as Huawei Ascend and Biren in Athens. This instructor-led course guides advanced programmers through code translation and performance optimization, featuring hands-on labs for porting CUDA codebases to new SDKs.
This instructor-led live training in Athens, offered online or onsite, is designed for developers from beginner to intermediate levels who want to program NVIDIA GPUs using CUDA and leverage their parallelism.
Upon completing this training, participants will be able to:
Configure a development environment that includes the CUDA Toolkit, an NVIDIA GPU, and Visual Studio Code.
Build a basic CUDA program that performs 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, copy data between host and device, launch kernels, and synchronize threads.
Write kernels in CUDA C/C++ that execute on the GPU and manipulate data.
Utilize CUDA built-in functions, variables, and libraries for common tasks and operations.
Leverage CUDA memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory access.
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.
This live training for Athens walks intermediate AI developers through the process of deploying models on Ascend processors using the CANN toolkit. Participants will learn to convert frameworks such as PyTorch and TensorFlow, optimize performance, and resolve issues to ensure efficient edge and cloud inference scenarios.
This live training in Athens empowers developers with the expertise to program and optimize applications on Biren AI accelerators. Participants will explore GPU architecture, configure the SDK, and migrate CUDA code to Biren, with a strong emphasis on performance tuning and advanced debugging methodologies.
This live, instructor-led training in Athens empowers developers to build and deploy AI models using BANGPy and Neuware on Cambricon MLUs. Participants will learn to configure environments, develop optimized models, and integrate MLU acceleration into edge and data center applications.
This instructor-led, live training in Athens (online or onsite) is designed for beginner-level system administrators and IT professionals who want to learn how to install, configure, manage, and troubleshoot CUDA environments.
Upon completion of this training, participants will be able to:
Comprehend the architecture, components, and capabilities of CUDA.
This instructor-led, live training in Athens (online or onsite) targets developers from beginner to intermediate levels who wish to utilize OpenCL for programming heterogeneous devices and exploiting their parallelism.
By the end of this training, participants will be able to:
Set up a development environment that includes the OpenCL SDK, OpenCL-supporting devices, 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.
Employ OpenCL built-in functions, extensions, and libraries for common tasks and operations.
Utilize 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 with tools such as CodeXL, Intel VTune, and NVIDIA Nsight.
Optimize OpenCL programs using techniques like vectorization, loop unrolling, local memory, and profiling.
This instructor-led, live training in Athens (online or onsite) targets 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 Athens (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 Athens (online or onsite) targets 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 Athens explores GPU programming for parallel computing, demonstrating how to leverage various platforms and master the CUDA framework and its features. Participants will learn a range of optimization techniques using CUDA, with applications spanning deep learning, data analytics, image processing, and engineering solutions.
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Testimonials (1)
Trainers energy and humor.
Tadeusz Kaluba - Nokia Solutions and Networks Sp. z o.o.
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