GPU Training in Srbija

This instructor-led live training in Srbija (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 Srbija (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:
  Grasp the distinctions between CPU and GPU computing, as well as the advantages and challenges associated with GPU programming.
• Select the appropriate framework and tool for their specific GPU application.
• Develop a fundamental GPU program that executes vector addition using one or more of the covered frameworks.
• Utilize the relevant APIs, languages, and libraries to query device information, allocate and release device memory, transfer data between the host and device, launch kernels, and synchronize threads.
• Leverage specific memory spaces—such as global, local, constant, and private—to enhance data transfer efficiency and memory access patterns.
• Apply corresponding 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.
• Optimize GPU programs employing techniques like coalescing, caching, prefetching, and profiling.

This instructor-led, live training in Srbija (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.

Ova obuka sa instruktorom, koja se održava uživo na <lokalizaciji> (online ili na licu mesta), namenjena je programerima početnog i srednjeg nivoa znanja koji žele da uče kako da instaliraju i koriste ROCm na Windows-u, kako bi programirali AMD GPU-je i iskoristili njihovu paralelnu prirodu.

Ko se tokom ove obuke učesnici će steći znanje kako da:

• Konfigurišu razvojno okruženje koje uključuje ROCm platformu, AMD GPU i Visual Studio Code na Windows-u.
• Naprave osnovni ROCm program koji obavlja vektorsko sabiranje na GPU-u i preuzima rezultate iz memorije GPU-a.
• Koriste ROCm API za upit o informacijama o uređaju, alokaciju i oslobađanje memorije uređaja, kopiranje podataka između domaćina i uređaja, pokretanje kernela i sinhronizaciju niti.
• Koriste HIP jezik za pisanje kernela koji se izvršavaju na GPU-u i manipulišu podacima.
• Koriste ugrađene funkcije, promenljive i biblioteke HIP jezika za obavljanje uobičajenih zadataka i operacija.
• Koriste ROCm i HIP memorijske prostore, kao što su globalni, deljeni, konstantni i lokalni, kako bi optimizovali transfer podataka i pristup memoriji.
• Koriste ROCm i HIP modele izvršavanja za kontrolu niti, blokova i rešetki koji definišu paralelizam.
• Ispravljaju greške i testiraju ROCm i HIP programe koristeći alate kao što su ROCm Debugger i ROCm Profiler.
• Optimizuju ROCm i HIP programe koristeći tehnike kao što su udruživanje (coalescing), keširanje, unapređeno učitavanje (prefetching) i profiliranje.

This instructor-led, live training in Srbija (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 efficiency on Ascend AI processors through this advanced, instructor-led training in Srbija. Dive into CANN's runtime architecture, utilizing the Graph Engine, TIK, and TVM for profiling, developing custom operators, and resolving memory bottlenecks.

This instructor-led, live training in Srbija (available online or onsite) is tailored for beginner to intermediate developers who wish to utilize CUDA for programming NVIDIA GPUs and harnessing their parallel processing power.

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

• Configure a development environment that includes the CUDA Toolkit, an NVIDIA GPU, and Visual Studio Code.
• Create a basic CUDA program to perform vector addition on the GPU and fetch results from GPU memory.
• Use the CUDA API to query device information, allocate and free device memory, transfer data between host and device, launch kernels, and synchronize threads.
• Write kernels in CUDA C/C++ that execute on the GPU and handle data manipulation.
• 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.
• Control the CUDA execution model to manage threads, blocks, and grids that define parallelism.
• Debug and test CUDA programs with tools like CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize CUDA programs through techniques such as coalescing, caching, prefetching, and profiling.

Reports a 97% client satisfaction rate.

This instructor-led, live training in Srbija (online or onsite) is designed for beginner-level system administrators and IT professionals who wish 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.
• Install and configure CUDA environments.
• Manage and optimize CUDA resources.
• Debug and resolve common CUDA issues.

Ova obuka sa trenerom uživo u Srbija (online ili na licu mesta) namenjena je programerima početnika do srednjeg nivoa koji žele da koriste OpenCL za programiranje heterogenih uređaja i iskorišćavanje njihove paralelizacije.

Na kraju ove obuke, učesnici će biti u stanju da:

• podevaju razvojno okruženje koje uključuje OpenCL SDK, uređaj koji podržava OpenCL i Visual Studio Code;
• napišu osnovni OpenCL program koji obavlja vektorsko sabiranje na uređaju i preuzme rezultate iz memorije uređaja;
• koriste OpenCL API za upit informacija o uređaju, kreiranje konteksta, redova komandi, buffer-a, kerneleova i događaja;
• koriste OpenCL C jezik za pisanje kerneleova koji se izvršavaju na uređaju i manipulaciju podacima;
• koriste OpenCL ugrađene funkcije, ekstenzije i biblioteke za obavljanje uobičajenih zadataka i operacija;
• koriste OpenCL modele memorije domaćina i uređaja za optimizaciju transfera podataka i pristupa memoriji;
• koriste OpenCL model izvršavanja za kontrolu radnih stavki, radnih grupa i ND-Opsega;
• odebuguju i testiraju OpenCL programe koristeći alate poput CodeXL, Intel VTune i NVIDIA Nsight;
• optimizuju OpenCL programe koristeći tehnike kao što su vektoralizacija, razvrtavanje petlji, lokalna memorija i profiliranje.

This instructor-led, live training in Srbija (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 Srbija (online or onsite) is designed for C/C++ developers who want to leverage CUDA to accelerate compute-intensive applications, such as data processing, scientific simulations, machine learning workloads, and image processing pipelines.

This guided, live training in Srbija (online or onsite) is designed for 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 Srbija provides comprehensive guidance on programming GPUs for parallel computing. Participants will learn how to utilize various platforms, master the CUDA platform and its capabilities, and apply diverse optimization techniques using CUDA. Key application areas covered include deep learning, analytics, image processing, and engineering solutions.