mtensor | a c++/cuda template library for tensor lazy evaluation | GPU library

by matazure C++ Version: v2.0 License: Non-SPDX

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kandi X-RAY | mtensor Summary

mtensor is a C++ library typically used in Hardware, GPU, Pytorch, Numpy applications. mtensor has no bugs, it has no vulnerabilities and it has low support. However mtensor has a Non-SPDX License. You can download it from GitHub.

延迟计算具有避免额外内存读写的优点, 该技术应用于数值计算，图像处理等领域. 目前绝大部分支持延迟计算的库都没支持cuda, 而对于gpu这种计算能里远强于内存带宽的设备来说, 延迟计算尤为重要, cuda 9版本以来, cuda c++逐渐完善了对c++11和c++14的支持, 使得cuda的延迟计算可以得到简洁的实现.

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Support

mtensor has a low active ecosystem.

It has 155 star(s) with 39 fork(s). There are 16 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 27 have been closed. On average issues are closed in 254 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of mtensor is v2.0

Quality

mtensor has no bugs reported.

Security

mtensor has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

mtensor has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

mtensor releases are available to install and integrate.

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

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mtensor Key Features

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mtensor Examples and Code Snippets

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Community Discussions

Trending Discussions on mtensor

How to take advantage of the Move Semantics for a better performance in C++11?

QUESTION

How to take advantage of the Move Semantics for a better performance in C++11?

Asked 2017-Feb-27 at 18:35

After many trials I still do not understand how to properly take advantage of the move semantics in order to not copy the result of the operation and just use the pointer, or std::move, to "exchange" the data pointed to. This will be very usefull to speed-up more complicated functions like f(g(),h(i(l,m),n(),p(q())) The objective is to have:

...

ANSWER

Answered 2017-Feb-27 at 16:56

You need to move t1 when calling scalarProduct1D, otherwise you'll make a copy:

Source https://stackoverflow.com/questions/42490875

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

Vulnerabilities

No vulnerabilities reported

Install mtensor

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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