math-api | Serverless API to render maths using MathJax for Node | Math library

by chialab JavaScript Version: Current License: MIT

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kandi X-RAY | math-api Summary

math-api is a JavaScript library typically used in Utilities, Math, Latex applications. math-api has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Math API • Render LaTeX and MathML formulas as SVG or PNG. A REST API to do fancy things with formulas, like rendering LaTeX or MathML to SVG or PNG on the server side using MathJax for Node, while leveraging expensive computations on the client.

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math-api has a low active ecosystem.

It has 22 star(s) with 4 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 4 days. There are 7 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of math-api is current.

Quality

math-api has 0 bugs and 0 code smells.

Security

math-api has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

math-api code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

math-api is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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math-api releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

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math-api Key Features

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Community Discussions

Trending Discussions on math-api

What term is rounded in the CUDA __fsqrt round intrinsics?

How to convince CMake to use the CUDA fmax function instead of the std cmath function?

Mathematical Guarantee in Trignometric Functions of CUDA

QUESTION

What term is rounded in the CUDA __fsqrt round intrinsics?

Asked 2021-Jul-14 at 21:59

I need the square root of a float, in CUDA device code. Hard to say whether speed matters more than accuracy in my use case.

__sqrtf CUDA intrinsic is the natural choice

But then I saw the various __fsqrt with rounding CUDA intrinsics;

What is rounded in these intrinsics; the argument "x" or the return value? Or do I misunderstand the meaning of rounding here?

My testing suggests neither is rounded! I wrote a kernel that evaluates:

...

ANSWER

Answered 2021-Jul-14 at 21:59

It's a rounding mode for the result. Input arguments are not "rounded" before they are injected into the arithmetic flow.

the "rn" rounding "direction" is "round-to-nearest"

It means that at whatever precision the interim result is being calculated to, that result will be rounded to the nearest available representation. In the case of a float final result, it will be rounded to the nearest available float representation.

Let's revisit your example. When I put your problem into the windows 10 calculator, the result I get is 42.001190459319126303634970957554 The way we get from that "correct result at arbitrary precision" to a 32-bit floating point "rn" result is to take the two 32-bit floating point numbers, one which is closest but numerically higher, and one which is closest but numerically lower, and of those 2, select the one that is closest. That is apparently 42.0011902.

Source https://stackoverflow.com/questions/68385222

QUESTION

How to convince CMake to use the CUDA fmax function instead of the std cmath function?

Asked 2020-Oct-04 at 04:15

Say I have the following function:

...

ANSWER

Answered 2020-Oct-04 at 04:15

The problem has nothing to do with CMake or linking.

CUDA uses template overloading to provide native math library functions in device code. As per the documentation, exactly two versions of fmax are provided (here and here). They are

Source https://stackoverflow.com/questions/64156448

QUESTION

Mathematical Guarantee in Trignometric Functions of CUDA

Asked 2020-Jan-31 at 10:23

CUDA supports mathematical functions. But do they provide any guarantee like if I compute sin(x) the result would the closet representable value to the mathematical value of sin(x)? If the answer is no, is there any alternative if we want to stay in GPU? Something like it always return an upper bound or always a lower bound (but possibly not the closest possible one).

...

ANSWER

Answered 2020-Jan-31 at 10:23

CUDA Appendix Suggests that the value provided by the API are not accurate. There is a difference of, 1/2 ulps in their value. ~~(when compared between host and device)~~.

However, for all practical purposes, these values are accurate.

Anyway if you want to perform symbolic operations with high-level precision, using float precision makes it inaccurate.

Source https://stackoverflow.com/questions/59999000

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

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