Leuven

Park Inn by Radisson Leuven, Martelarenlaan 36, Leuven, Belgium, 3010

Leuven

Louvain (in Dutch Leuven, in German Löwen) is a Dutch-speaking city in Belgium located in the Flemish Region, capital of the province of Flemish Brabant and capital of the district that bears its name. It is watered by the Dyle, a tributary of the Rupel. It is a university city where the Katholieke Universiteit Leuven is located, a Dutch-speaking branch born from the split of the oldest university in Belgium. Leuven is also known for hosting the headquarters of AB InBev, the largest brewery in the world. Leuven is the beer capital of Belgium.

  • The nearest airport to the training center

    • 15 kilometers east of Brussels airport.

  • Closest train station to the training center

    • Leuven station

  • The nearest car park to the training center

    • P1 Parking Station Leuven

  • Cafés restaurants bars nearby

    1. Burger Farm Restaurant
    2. Restaurant Cuore Italiano
    3. Café Rocco

    Subcategories (1)

    Image for the CUDA (Compute Unified Device Architecture)

    CUDA (Compute Unified Device Architecture)

    Explore Our Courses

    GPU Programming with OpenACC

     28 Hours
    This instructor-led, live training in Leuven (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.
    Read more...

    Introduction to GPU Programming

     21 Hours
    This instructor-led, live training in Leuven (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.
    Read more...

    GPU Programming - OpenCL vs CUDA vs ROCm

     28 Hours
    This instructor-led, live training in Leuven (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.
    Read more...

    ROCm for Windows

     21 Hours
    This instructor-led, live training in Leuven (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.
    Read more...

    AMD GPU Programming

     28 Hours
    This instructor-led, live training in Leuven (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.
    Read more...

    GPU Programming with CUDA

     28 Hours
    This instructor-led, live training in Leuven (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.
    97% de clients satisfaits. 
    Read more...

    Administration of CUDA

     35 Hours
    This instructor-led, live training in Leuven (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.
    • Install and configure CUDA environments.
    • Manage and optimize CUDA resources.
    • Debug and troubleshoot common CUDA issues.
    Read more...

    GPU Programming with OpenCL

     28 Hours
    This instructor-led, live training in Leuven (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.
    Read more...

    GPU Programming with CUDA and Python

     14 Hours
    This instructor-led, live training in Leuven (online or onsite) is aimed at intermediate-level 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.
    Read more...

    NVIDIA GPU Programming - Extended

     21 Hours
    This instructor-led, live training course in Leuven 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.
    Read more...

    Last Updated:

    Testimonials (2)

    Very interactive with various examples, with a good progression in complexity between the start and the end of the training.

    Jenny - Andheo

    Course - GPU Programming with CUDA and Python

    Trainers energy and humor.

    Tadeusz Kaluba - Nokia Solutions and Networks Sp. z o.o.

    Course - NVIDIA GPU Programming - Extended

    Provisional Upcoming Courses (Contact Us For More Information)

    GPU Programming - OpenCL vs CUDA vs ROCm

    2025-05-19 09:30
    28 hours
    Leuven
    4950 EUR (Online)
    6750 EUR (Classroom)

    GPU Programming with CUDA and Python

    2025-06-02 09:30
    14 hours
    Leuven
    2550 EUR (Online)
    3450 EUR (Classroom)

    ROCm for Windows

    2025-06-16 09:30
    21 hours
    Leuven
    3750 EUR (Online)
    5100 EUR (Classroom)

    GPU Programming - OpenCL vs CUDA vs ROCm

    2025-06-30 09:30
    28 hours
    Leuven
    4950 EUR (Online)
    6750 EUR (Classroom)

    Introduction to GPU Programming

    2025-07-14 09:30
    21 hours
    Leuven
    3750 EUR (Online)
    5100 EUR (Classroom)

    Other regions in Belgium

    Other Countries

    These courses are also available in other countries

    Consulting

    GPU Consulting
    Online GPU training in Leuven, GPU (Graphics Processing Unit) training courses in Leuven, Weekend Graphics Processing Unit (GPU) courses in Leuven, Evening Graphics Processing Unit (GPU) training in Leuven, Graphics Processing Unit (GPU) instructor-led in Leuven, Graphics Processing Unit classes in Leuven, Graphics Processing Unit instructor-led in Leuven, GPU on-site in Leuven, Graphics Processing Unit (GPU) boot camp in Leuven, Weekend Graphics Processing Unit training in Leuven, Graphics Processing Unit (GPU) coaching in Leuven, Graphics Processing Unit trainer in Leuven, Graphics Processing Unit (GPU) private courses in Leuven, Graphics Processing Unit one on one training in Leuven, Evening GPU courses in Leuven, GPU instructor in Leuven, Online GPU (Graphics Processing Unit) training in Leuven