curand | R bindings for cuRAND | GPU library

by cur-dev R Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets(1)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | curand Summary

curand is a R library typically used in Hardware, GPU, Deep Learning applications. curand has no bugs, it has no vulnerabilities and it has low support. However curand has a Non-SPDX License. You can download it from GitHub.

Fast random number generators on gpu's via NVIDIA CUDA. Not officially affiliated with or endorsed by NVIDIA in any way, but we like their work.

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Support

curand has a low active ecosystem.

It has 5 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

curand has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of curand is current.

Quality

curand has no bugs reported.

Security

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

License

curand 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

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

Installation instructions, examples and code snippets are available.

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curand Key Features

No Key Features are available at this moment for curand.

curand Examples and Code Snippets

Find the curand configuration .

python

Lines of Code : 28

License : Non-SPDX (Apache License 2.0)

Copy

def _find_curand_config(base_paths, required_version, cuda_version):

  if _at_least_version(cuda_version, "11.0"):

    def get_header_version(path):
      version = (
          _get_header_version(path, name)
          for name in ("CURAND_VER_MAJO

Community Discussions

Trending Discussions on curand

Difficulty using atomicMin to find minimum value in a matrix

RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. GPU not detected by pytorch

How to find and link CUDA libraries using CMake 3.15?

PULSE on github (link provided) RuntimeError: CUDA out of memory.... preventing the program "run.py" from executing

How to build pytorch from source using numpy

CUDA catkin/cmake - undefined reference to `curandCreateGenerator`

Docker GPU enabled version (>19.03) does not load tensorflow successfully

GCC version 4.9 has no installation candidate

curand_uniform not deterministic?

cuRAND performs much worse than thrust when generating random numbers inside CUDA kernels

QUESTION

Difficulty using atomicMin to find minimum value in a matrix

Asked 2021-Apr-20 at 21:13

I'm having trouble using atomicMin to find the minimum value in a matrix in cuda. I'm sure it has something to do with the parameters I'm passing into the atomicMin function. The findMin function is the function to focus on, the popmatrix function is just to populate the matrix.

...

ANSWER

Answered 2021-Apr-20 at 21:13

harr is not allocated. You should allocated it on the host side using for example malloc before calling cudaMemcpy. As a result, the printed values you look are garbage. This is quite surprising that the program did not segfault on your machine.

Moreover, when you call the kernel findMin at the end, its parameter is harr (which is supposed to be on the host side regarding its name) should be on the device to perform the atomic operation correctly. As a result, the current kernel call is invalid.

As pointed out by @RobertCrovella, a cudaDeviceSynchronize() call is missing at the end. Moreover, you need to free your memory using cudaFree.

Source https://stackoverflow.com/questions/67185681

QUESTION

RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. GPU not detected by pytorch

Asked 2021-Mar-21 at 16:24

Having trouble with CUDA + Pytorch this is the error. I reinstalled CUDA and cudnn multiple times.

Conda env is detecting GPU but its giving errors with pytorch and certain cuda libraries. I tried with Cuda 10.1 and 10.0, and cudnn version 8 and 7.6.5, Added cuda to path and everything.

However anaconda is showing cuda tool kit 9.0 is installed, whilst I clearly installed 10.0, so I am not entirely sure what's the deal with that.

...

ANSWER

Answered 2021-Mar-20 at 10:44

From the list of libraries, it looks like you've installed CPU only version of the Pytorch.

Source https://stackoverflow.com/questions/66711799

QUESTION

How to find and link CUDA libraries using CMake 3.15?

Asked 2021-Feb-23 at 21:08

I'm using CMake 3.15-rc3 on my Unix-like system.

I need to link a program I'm building with several of the CUDA libraries, including cublas, cufft, cusolver, curand, nppicc, nppial, nppist, nppidei, nppig, nppitc, npps.

Based on what I found online, I need to do something like this:

...

ANSWER

Answered 2021-Feb-23 at 07:18

The documentation you linked says that you need to add CUDA to the list of languages in your project() command. And to find CUDA libraries it says to use FindCUDAToolkit module, not that CUDALibs.

Source https://stackoverflow.com/questions/66327073

QUESTION

PULSE on github (link provided) RuntimeError: CUDA out of memory.... preventing the program "run.py" from executing

Asked 2021-Jan-15 at 02:58

(As a student I am kind of new to this but did quite a bit of research and I got pretty far, I'm super into learning something new through this!)

This issue is for the project pulse -> https://github.com/adamian98/pulse

the readme if you scroll down a bit on the page, gives a much better explanation than I could. It will also give a direct "correct" path to judge my actions against and make solving the problem a lot easier.

Objective: run program using the run.py file

Issue: I got a "RuntimeError: CUDA out of memory" despite having a compatible gpu and enough vram

Knowledge: when it comes to coding i just started a few days ago and have a dozen hours with anaconda now, comfterable creating environments.

What I did was... (the list below is a summary and the specific details are after it)

install anaconda
use this .yml file -> https://github.com/leihuayi/pulse/blob/feature/docker/pulse.yml (it changes dependencies to work for windows which is why I needed to grab a different one than the one supplied on the master github page) to create a new environment and install the required packages. It worked fantastically! I only got an error trying to install dlib, it didn't seem compatible with A LOT of the packages and my python version.
I installed the cuda toolkit 10.2 , cmake 3.17.2, and tried to install dlib into the environment directly. the errors spat out in a blaze of glory. The dlib package seems to be only needed for a different .py file and not run.py though so I think it may be unrelated to this error

logs are below and I explain my process in more detail

START DETAILS AND LOGS: from here until the "DETAILS 2" section should be enough information to solve, the rest past there is in case

error log for runing out of memory--> (after executing the "run.py" file)

...

ANSWER

Answered 2021-Jan-15 at 02:58

based on new log evidence using this script simultaneously alongside the run.py file

Source https://stackoverflow.com/questions/65680194

QUESTION

How to build pytorch from source using numpy

Asked 2020-Nov-03 at 12:38

I am trying to build pytorch v1.4.0 from source because I need it for another module. I have succeeded in building pytorch from source but when I try to run the intended python script I get this error:

...

ANSWER

Answered 2020-Nov-03 at 12:38

Okay so I don't exactly know what the solution was since I did two things:

I installed a lower version of numpy since my torch version is also a bit older.
I cleared all the pytorch install cache with the command: sudo USE_ROCM=1 USE_LMDB=1 USE_OPENCV=1 MAX_JOBS=15 python3 setup.py clean

Downgrading numpy might have been unnecessary, since I can't remember if I cleared all the pytorch installation cache after installing numpy and trying again.

Now my installation log contains USE_NUMPY : ON. Dont know if this fixed all my problems, building pytorch takes forever so I'll just have to wait and see, but at least it fixed this one :)

Source https://stackoverflow.com/questions/64662102

QUESTION

CUDA catkin/cmake - undefined reference to `curandCreateGenerator`

Asked 2020-Oct-30 at 20:23

I've got an issue with what I believe is a linker error when compiling a CUDA project with catkin. A snippet of the source code is below. When I try to run catkin build I get an error:

error

...

ANSWER

Answered 2020-Oct-30 at 20:23

Robert Crovella answered this question in the comments above.

I was able to use target_link_libraries() to set -lcurand and -lcublas which satisfied the dependencies. So I added the following line to my CMakeLists.txt

Source https://stackoverflow.com/questions/64599880

QUESTION

Docker GPU enabled version (>19.03) does not load tensorflow successfully

Asked 2020-Jul-04 at 17:00

I want to use docker 19.03 and above in order to have GPU support. I currently have docker 19.03.12 in my system. I can run this command to check that Nvidia drivers are running:

...

ANSWER

Answered 2020-Jul-04 at 17:00

tldr;

A base Dockerfile which seems to work with docker 19.03+ & cuda 10 is this:

Source https://stackoverflow.com/questions/62679353

QUESTION

GCC version 4.9 has no installation candidate

Asked 2020-Jun-04 at 08:51

I'm trying to install gcc version 4.9 on Ubuntu to replace the current version 7.5 (because Torch is not compatible with version 6 and above). However, even following precise instructions, I can't install it. I did:

...

ANSWER

Answered 2020-Jun-04 at 08:51

In the meantime, I figured out myself. You must add however that strangely, G++ and GCC version 4.9 is still not available, you must go with 4.8. By combining multiple sources, I constructed a way to install G++ and GCC 4.8.5 on your machine and configure them as the default ones:

Source https://stackoverflow.com/questions/62177887

QUESTION

curand_uniform not deterministic?

Asked 2020-May-23 at 13:45

I want to generate pseudo-random numbers on a CUDA device in a deterministic way, saying if I ran the program two times I expect the exact same results, given that the program uses a hardcoded seed. Following the examples provided by nvidia: https://docs.nvidia.com/cuda/curand/device-api-overview.html#device-api-example I would expect exactly the described behavior.

But I do get different results, running the exact same code multiple times. Is there a way to get pseudo-random numbers in a deterministic way, as I described?

Following example code shows my problem:

...

ANSWER

Answered 2020-May-23 at 13:45

Thanks to the comments by Robert Crovella I see now that the error was in relying on the thread execution order. Just not reusing the state would result in the same "random" numbers, when the draw_numbers kernel is called multiple times, which is not an option for me either.

My guess is that the best solution in my case is to only launch 1024 threads (as many as curandState are set up) and generating multiple random numbers in each thread (in my example 16/thread). This way I receive different random numbers on multiple calls within the program, but the same numbers for every program launch.

Updated code:

Source https://stackoverflow.com/questions/61962819

QUESTION

cuRAND performs much worse than thrust when generating random numbers inside CUDA kernels

Asked 2020-Apr-20 at 16:24

I am trying to generate "random" numbers from a uniform distribution inside a CUDA __global__ kernel using two different approaches. The first is using the cuRAND device API, and the second is using thrust. For each approach I have created a different class.

Here is my cuRAND solution:

...

ANSWER

Answered 2020-Apr-20 at 16:24

Perhaps the performance difference happens because cuRAND and Thrust use different PRNG algorithms with different performance profiles and demands on memory. Note that cuRAND supports five different PRNG algorithms, and your code doesn't give which one is in use.

Thrust's default_random_engine is currently minstd_rand, but its documentation notes that this "may change in a future version". (A comment written after I wrote mine also noted that it's minstd_rand.) minstd_rand is a simple linear congruential generator that may be faster than whatever PRNG cuRAND is using.

This was a comment converted to an answer and edited.

Source https://stackoverflow.com/questions/60731867

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