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CuPy VS Scilab

Compare CuPy VS Scilab and see what are their differences

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  2. » Videos
  3. » Reviews
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CuPy logo CuPy

CuPy is an open-source array library for GPU-accelerated computing with Python. CuPy utilizes CUDA Toolkit libraries including cuBLAS, cuRAND, cuSOLVER, cuSPARSE, cuFFT, cuDNN and NCCL to make full use of the GPU architecture.

Scilab logo Scilab

Scilab Official Website. Enter your search in the box aboveAbout ScilabScilab is free and open source software for numerical . Thanks for downloading Scilab!
Not present
  • Scilab Landing page
    Landing page //
    2023-02-10

CuPy features and specs

No features have been listed yet.

Scilab features and specs

  • Open Source
    Scilab is free and open-source software, allowing users to access the source code and modify it to suit their needs without any cost.
  • Extensive Mathematical Functionality
    Scilab provides a wide range of mathematical functions and capabilities for numerical computation, making it suitable for a variety of scientific and engineering applications.
  • Toolboxes and Modules
    It offers various built-in toolboxes and modules for specialized tasks, such as signal processing, control systems, and optimization, expanding its functionality.
  • Cross-Platform Support
    Scilab runs on different operating systems, including Windows, macOS, and Linux, providing flexibility for users working in diverse environments.
  • Strong Community Support
    A large and active user community means that users can find plenty of support, tutorials, and third-party contributions, easing the learning curve.
  • Integration Capabilities
    Scilab can be easily integrated with other software and tools, such as Modelica for modeling and simulation, enhancing its versatility in different workflows.

Possible disadvantages of Scilab

  • Performance
    Scilab may not be as performance-optimized as some other numerical computation software, like MATLAB, especially for very large datasets or highly complex calculations.
  • Learning Curve
    While Scilab is powerful, it can be challenging for beginners to master due to its extensive functionality and the need to learn its scripting language.
  • Less Commercial Support
    As open-source software, Scilab does not offer the same level of commercial support or extensive professional resources that are available for some paid alternatives like MATLAB.
  • Documentation Quality
    Although Scilab has a lot of documentation, some users find that it lacks depth or clarity compared to other software, making it harder to find thorough explanations or examples.
  • Graphical User Interface
    The graphical user interface (GUI) of Scilab is not as polished or user-friendly as that of some competitor tools, which can impact user experience.
  • Compatibility Issues
    Interoperability with MATLAB can be limited, potentially causing issues when porting code or collaborating with MATLAB users.

Analysis of Scilab

Overall verdict

  • Overall, Scilab is a robust and cost-effective alternative to other commercial numerical computation software. Its strengths lie in its flexibility and the support of a large community of users and contributors.

Why this product is good

  • Scilab is considered good by many due to its open-source nature, comprehensive capabilities for numerical computations, and its extensive community support. It offers a wide range of mathematical functions for engineering and scientific applications and is particularly favored for its ability to handle complex data analysis and simulations. Additionally, its compatibility with MATLAB code and its powerful graphical capabilities make it a versatile tool for developers and researchers.

Recommended for

    Scilab is recommended for engineers, scientists, and educators who require a powerful computational tool without the associated costs of commercial software. It is also suitable for students and researchers who are looking to perform complex mathematical modeling and simulations.

CuPy videos

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Scilab videos

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Scilab IPCV 1.2

More videos:

  • Review - Raspberry Pi for Computer Vision with Scilab
  • Review - Tone Recognition with Scilab and LabVIEW to Scilab Gateway

Category Popularity

0-100% (relative to CuPy and Scilab)

CuPy

Scilab

Numerical Computation
3 3%
Numerical Computation
97% 97
Technical Computing
2 2%
Technical Computing
98% 98
3D
7 7%
3D
93% 93
Architecture
100 100%
Architecture
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare CuPy and Scilab

CuPy Reviews

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Scilab Reviews

25 Best Statistical Analysis Software
Scilab is a powerful, free, and open-source software widely used by researchers, students, and professionals in various fields such as engineering, mathematics, physics, and more.
Source: www.scijournal.org
7 Best MATLAB alternatives for Linux
The syntax of Scilab is similar to MATLAB it also provides a source code translator to convert MATLAB code to Scilab.
Source: cyanogenmods.org
Matlab Alternatives
Scilab is an open-source similar to the implementation of Matlab. The approximation techniques known as Scientific Computing is used to solve numerical problems. To achieve this, the team of Scilab developers made use of Solvers and algorithms to build the algebraic libraries. Scilab is one of the major alternatives to Matlab along with GNU Octave.
Source: www.educba.com
10 Best MATLAB Alternatives [For Beginners and Professionals]
Scilab has 1700 mathematical functions for engineering applications and data analysis. You can also use Scilab to solve various constrained and unconstrained problems such as shape and topology optimizations etc.
Source: thetechtop10.com
4 open source alternatives to MATLAB
Scilab is another open source option for numerical computing that runs across all the major platforms: Windows, Mac, and Linux included. Scilab is perhaps the best known alternative outside of Octave, and (like Octave) it is very similar to MATLAB in its implementation, although exact compatibility is not a goal of the project's developers.
Source: opensource.com

What are some alternatives?

When comparing CuPy and Scilab, you can also consider the following products

MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming

SciPy - SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering. 

GNU Octave - GNU Octave is a programming language for scientific computing.

Julia - Julia is a sophisticated programming language designed especially for numerical computing with specializations in analysis and computational science. It is also efficient for web use, general programming, and can be used as a specification language.

Wolfram Mathematica - Mathematica has characterized the cutting edge in specialized processing—and gave the chief calculation environment to a large number of pioneers, instructors, understudies, and others around the globe.

R Lang - R is a free software environment for statistical computing and graphics.

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