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CUDA or Compute Unified Device Architecture is an extension of the C programming language. It was developed by Nvidia. CUDA enables a programmer to take benefit of the parallel computing power of the graphics card to perform general-purpose computation. It is used in research and science applications in fields such as financial modeling, medical imaging, and energy exploration. If you need the best CUDA assignment help, you can contact the experts of BookMyEssay.

What is CUDA?

CUDA is a programming and computing platform model, which makes using GPU simple and elegant. It is the GPU architecture of NVIDIA. It positions itself as a way for computing along with GPU.

It allows developers for increasing the speed of computer-intensive applications by using the power of GPU. While there are proposed APIs related to GPUs including OpenCL, you will find competitive GPUs from companies like AMD.  When Nvidia GPUs are CUDA are combined they dominate many application areas such as deep learning. It is the foundation for the fastest computers all across the globe. With BookMyEssay assistance, besides understanding the topic properly, you can secure top grades when you buy assignment case study help on CUDA topics.


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CUDA Application Areas

Nvidia GPUs and CUDA have been applied in several areas, which require top computing performances. Some of the application areas are highlighted in our Compute Unified Device Architecture assignment help as follows:

  • Computational finance
  • Data Analytics and Science
  • Weather, climate, and ocean modeling
  • Machine learning and deep learning
  • Intelligence and Defense
  • Medical Imaging
  • Manufacturing/Architecture, Engineering, and Construction: CAE and CAD (including fluid dynamics, computational fluid dynamics, electronic design automation, and design and visualization)
  • Oil and gas
  • Media and Entertainment (modeling, animation, rendering, compositing, color correction, finishing and effects, on-set, on-air graphics, stereo tools, review, and weather graphics)
  • Safety and security
  • Research
  • Tools and management

CUDA Toolkit

The toolkit of CUDA includes debugging, libraries, and optimization tools, documentation, a compiler, and a runtime library for deploying the applications. CUDA has components, which support linear algebra, deep learning, parallel algorithms, and signal processing.

CUDA libraries support the Nvidia GPU families, however, perform best on the newest generation including V100 that is 3x quicker compared to P100. Using one or more than one library is the quickest way for taking the benefits of GPUs until the time you need algorithms that are implemented in an accurate way. We have hired eminent scholars who can offer you trustworthy CUDA assignment writing help.

Advantages of CUDA

There are many companies that look for CUDA developers. You should learn CUDA because it offers the following benefits that are discussed in our CUDA assignment help online as follows:

  • Software shall not see automatic performance upgradations, which they use for obtaining quicker CPU architecture every two years.
  • CUDA is a parallel model. You will find many others such as OpenCL, TBB, MPI, OpenMP, Cilk, MPI, etc. If you know how to combine them, you will increase the understanding of a parallel programming field.
  • CUDA is a specific model and has different architectures in comparison to traditional CPUs. When you learn CUDA, you will understand the architecture better.

CUDA and Deep Learning

Deep learning has the need for computing speed. For training the models of Google Translate, the teams of Google Translate and the Google Brain did a week of TensorFlow using GPUs that they had purchased fro Nvidia. Without GPUs, the training would have taken a few months instead of a week. Besides TensorFlow, several other DL frameworks depend on CUDA for GPU support such as CNTK, Cafee2, H2O, CNTK, and Torch. In many cases, the cuDNN library is used for neural network computations.


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