gputools | gputools R package for gpu computing | GPU library

by nullsatz C++ Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | gputools Summary

gputools is a C++ library typically used in Hardware, GPU applications. gputools has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

gputools R package for gpu computing

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Quality

Security

License

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Support

gputools has a low active ecosystem.

It has 64 star(s) with 31 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 10 open issues and 15 have been closed. On average issues are closed in 236 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of gputools is current.

Quality

gputools has no bugs reported.

Security

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

License

gputools does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

gputools releases are not available. You will need to build from source code and install.

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gputools Key Features

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gputools Examples and Code Snippets

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Community Discussions

Trending Discussions on gputools

foreach doparallel on GPU

Calling CUDA API functions from Rcpp package causes segfault

Crash ERROR_CGDataProvider_BufferIsNotReadable + 12, CGDataProviderRetainBytePtr + 216

How to build Tensorflow 1.4 with CUDNN 5.0?

QUESTION

foreach doparallel on GPU

Asked 2018-Jun-21 at 08:21

I have this code for writing my results in parallel. I am using foreach and doParallel libraries in R.

...

ANSWER

Answered 2018-Jun-21 at 08:21

Parallelization with foreach or similar tools works because you have multiple CPUs (or a CPU with multiple cores), which can process multiple tasks at once. A GPU also has multiple cores, but these are already used to process a single task in parallel. So if you want to parallelize further, you will need multiple GPUs.

However, keep in mind that GPUs are faster than CPUs only for certain types of applications. Matrix operations with large matrices being a prime example! See the performance section here for a recent comparison of one particular example. So it might make sense for you to consider if the GPU is the right tool for you.

In addition: File IO will always go via the CPU.

Source https://stackoverflow.com/questions/50961484

QUESTION

Calling CUDA API functions from Rcpp package causes segfault

Asked 2018-May-04 at 13:17

I am currently trying to build an R-Package which works with CUDA. While the traditional method of creating the package would work, much like the gputools package, I wanted to try Rcpp for the package as it seems more clean and convenient concerning return values.

The package installation works well so far, but the issue is that the first call of a CUDA API function (like cudaMalloc() for example) crashes my RStudio.

I created a minimal example to illustrate my case.

It is as simple as

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ANSWER

Answered 2018-May-04 at 13:17

Thank you @RalfStubner, the mistake generating the error above was indeed just the declaration of a return type that was never returned.

So instead of

Source https://stackoverflow.com/questions/49856810

QUESTION

Crash ERROR_CGDataProvider_BufferIsNotReadable + 12, CGDataProviderRetainBytePtr + 216

Asked 2018-Mar-15 at 06:23

I have a crash once in every 1000 to 2000 sessions in our app and I can't figure out what to do about it.

Is that an Apple iOS crash where I cant do anything about it?

I am using SDWebImage pod in my project, maybe it is that?

here is the crash log:

...

ANSWER

Answered 2018-Jan-31 at 19:29

@chris-garrett pointed me in the right direction and I was able to greatly mitigate the issue by fixing memory leaks.

Users who are now getting this error are people who spend at least 30 minutes on the app, which is pretty uncommon. Also, the fact that it affects mostly big screens like iPhone 7/6s/8 Plus, iPhone X, where more memory is needed to render images would corroborate that theory

What you should do to solve this, is to check the memory while debugging. If it increases gradually when repeating the same actions, there's certainly something for you to fix.

There are many ways to find leaks in your app, I'm not a big fan of Instruments, but you can find many tutorials online if needed. Another option is to use memory graph. I wrote previously an article on how to use memory graph here.

Source https://stackoverflow.com/questions/48217875

QUESTION

How to build Tensorflow 1.4 with CUDNN 5.0?

Asked 2017-Nov-09 at 17:04

I'm trying to install Tensorflow 1.4 from sources with CUDA 8.0 and CUDNN 5.0.5, on Centos 7. It's indicated in the documentation that it should work with CUDNN 3 and higher. I'm working in a virtual env with Python 3.4.5, using Bazel 0.7.0, with GCC 4.9. During the configuration, I've set CUDNN version to 5.0.5 and the library has been found.

Unfortunately, it doesn't work and ends up with an error that seems to indicate that CUDNN v6 is needed (I may be wrong on the cause of the error).

Here is the command I'm using:

...

ANSWER

Answered 2017-Nov-09 at 06:48

Tensorflow r1.4 release notes suggest using cuDNN6. You can find all release info here.

All our prebuilt binaries have been built with CUDA 8 and cuDNN 6. We anticipate releasing TensorFlow 1.5 with CUDA 9 and cuDNN 7.

Prior to r1.4 cuDNN 5 works fine.

Source https://stackoverflow.com/questions/47188012

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

Vulnerabilities

No vulnerabilities reported

Install gputools

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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