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Compute Unified Device Architecture (CUDA)

Information Technology > Programming languages

Description

Compute Unified Device Architecture (CUDA) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing, an approach known as GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is designed to work with programming languages like C, C++, and Fortran. This accessibility makes it a popular choice for developers who want to accelerate their applications by harnessing the power of GPUs. With CUDA, developers can dramatically speed up computing applications by harnessing the power of GPUs.

Expected Behaviors

✎
LEVEL 1

Fundamental Awareness

At this level, individuals are expected to have a basic understanding of the concept of parallel computing and the CUDA platform. They should be familiar with the idea of GPU programming but may not yet have practical experience in writing or running CUDA programs.

🌱
LEVEL 2

Novice

Novices should be able to install the CUDA toolkit and SDK, understand the CUDA memory hierarchy, and know the basic CUDA syntax. They should be capable of writing, compiling, and running simple CUDA programs, and have an understanding of thread hierarchy and block dimensions.

🌍
LEVEL 3

Intermediate

Intermediate users should be proficient in using CUDA libraries and optimizing memory usage. They should understand CUDA streams and concurrency, have experience debugging CUDA code, and be able to use shared and constant memory spaces effectively.

⭐
LEVEL 4

Advanced

Advanced users should be proficient in optimizing CUDA code for performance and understand advanced CUDA features like dynamic parallelism. They should be able to use CUDA in combination with other programming languages, have experience with multi-GPU programming, and understand CUDA device query and capabilities.

🏆
LEVEL 5

Expert

Experts should have a deep understanding of GPU architecture and how it affects CUDA programming. They should be able to design and implement complex algorithms in CUDA, use CUDA for machine learning and AI applications, and have experience with advanced CUDA debugging and profiling tools. Experts should also be able to teach and mentor others in CUDA programming.

Micro Skills

✎
LEVEL 1

Fundamental Awareness

Familiarity with the basic principles of parallelism
Understanding the difference between serial and parallel computing
Knowledge of different types of parallelism (data, task, bit-level)
Awareness of the benefits and challenges of parallel computing
Basic knowledge of the structure of a CUDA program
Understanding the role of the host and device in CUDA
Awareness of the CUDA execution model (kernels, threads, blocks, grids)
Familiarity with the hardware components involved in CUDA (GPU, CPU)
Understanding the concept of a GPU and how it differs from a CPU
Awareness of the types of tasks suitable for GPU acceleration
Basic knowledge of how to write code for a GPU
Familiarity with the concept of kernels in GPU programming
🌱
LEVEL 2

Novice

Identifying compatible hardware
Determining software prerequisites
Navigating NVIDIA's website
Downloading the toolkit and SDK
Running the installer
Verifying the installation
Resolving dependency issues
Addressing hardware compatibility problems
🌍
LEVEL 3

Intermediate

Understanding of how to use cuBLAS and cuFFT
Knowledge of how to use Thrust library
Experience with NPP (NVIDIA Performance Primitives) library
Familiarity with CUDPP (CUDA Data Parallel Primitives Library)
Understanding of different memory types in CUDA
Experience with memory coalescing
Knowledge of how to use pinned memory
Ability to manage memory bandwidth
Knowledge of how to create and destroy streams
Understanding of how to synchronize streams
Experience with concurrent kernel execution
Ability to manage data transfer between host and device concurrently
Familiarity with CUDA-GDB
Understanding of how to use Nsight debugger
Experience with racecheck and memcheck tools
Ability to interpret error messages and codes in CUDA
Understanding of the benefits and limitations of shared and constant memory
Experience with declaring and allocating shared and constant memory
Knowledge of how to synchronize threads when using shared memory
Ability to optimize performance using shared and constant memory
⭐
LEVEL 4

Advanced

Understanding of GPU hardware and how it affects performance
Ability to use CUDA profiler tools
Knowledge of optimization techniques like loop unrolling, memory coalescing, and using shared memory
Ability to write nested kernel calls
Understanding of the impact of dynamic parallelism on performance
Experience with managing device and host code execution
Experience with CUDA C/C++ interoperability
Experience with CUDA Python interoperability
Understanding of how to manage memory when using CUDA with other languages
Understanding of multi-GPU architectures
Ability to distribute computation across multiple GPUs
Experience with CUDA multi-GPU libraries
Ability to use CUDA device query functions
Understanding of different CUDA device properties
Experience with selecting the appropriate device for a given task
🏆
LEVEL 5

Expert

Knowledge of different GPU architectures (Fermi, Kepler, Maxwell, Pascal, Volta, Turing, Ampere)
Understanding of how different GPU architectures affect performance
Ability to optimize code based on specific GPU architecture
Understanding of the impact of memory bandwidth and latency on performance
Proficiency in designing parallel algorithms
Experience with implementing complex data structures in CUDA
Ability to handle synchronization and race conditions in CUDA
Experience with using CUDA for graph processing, sorting, searching, etc.
Experience with using CUDA in popular machine learning frameworks like TensorFlow, PyTorch, etc.
Understanding of how to optimize machine learning algorithms for GPUs
Experience with implementing deep learning models in CUDA
Knowledge of AI-specific GPU features like Tensor Cores
Proficiency in using NVIDIA Nsight tools for debugging and profiling
Experience with using cuda-gdb for debugging
Ability to analyze profiler output to identify performance bottlenecks
Understanding of how to use CUDA events for performance measurement
Experience with creating educational content on CUDA programming
Ability to explain complex CUDA concepts in simple terms
Experience with mentoring junior developers in CUDA
Ability to provide constructive feedback on CUDA code

Skill Overview

  • Expert2 years experience
  • Micro-skills75
  • Roles requiring skill2

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