vexcl | VexCL is a C vector expression template library | GPU library

by ddemidov C++ Version: 1.4.2 License: MIT

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kandi X-RAY | vexcl Summary

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

VexCL is a vector expression template library for OpenCL/CUDA. It has been created for ease of GPGPU development with C++. VexCL strives to reduce amount of boilerplate code needed to develop GPGPU applications. The library provides convenient and intuitive notation for vector arithmetic, reduction, sparse matrix-vector products, etc. Multi-device and even multi-platform computations are supported. The source code of the library is distributed under very permissive MIT license. See VexCL documentation at

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vexcl has a low active ecosystem.

It has 632 star(s) with 78 fork(s). There are 64 watchers for this library.

It had no major release in the last 12 months.

There are 26 open issues and 168 have been closed. On average issues are closed in 58 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of vexcl is 1.4.2

Quality

vexcl has 0 bugs and 0 code smells.

Security

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

vexcl code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

vexcl is licensed under the MIT License. This license is Permissive.

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

Reuse

vexcl releases are available to install and integrate.

It has 105 lines of code, 3 functions and 4 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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vexcl Key Features

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vexcl Examples and Code Snippets

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Community Discussions

Trending Discussions on vexcl

Random number generator with Gaussian distribution for PyOpenCl

Boost - Odeint: What means concurrency using VexCL and how to improve it?

QUESTION

Random number generator with Gaussian distribution for PyOpenCl

Asked 2020-Mar-06 at 17:26

Generating Gaussian random numbers using numpy turns out to be the bottleneck in my monte carlo simulation where I make heavy use of PyOpenCl.

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ANSWER

Answered 2020-Mar-06 at 17:26

Seems like pyopencl already includes a random number generator:

https://github.com/inducer/pyopencl/blob/master/pyopencl/clrandom.py

Running a simple test shows, that the mean and standard deviation are comparable to numpy's implementation. Also the histogram corresponds to the normal distribution with negligible mean squared error.

Does anyone know further tests to check the quality of the random number generator?

Edit: According to https://documen.tician.de/pyopencl/array.html

Source https://stackoverflow.com/questions/60380379

QUESTION

Boost - Odeint: What means concurrency using VexCL and how to improve it?

Asked 2020-Feb-12 at 06:19

My question is related to the tutorial which explains how to implement boost::odeint with VexCL in order to achieve concurrency (the complete code can be found here).

The following figure shows how I think of the iterations of ODEINT:

Now I ask myself, what exactly / or which part of it is parallelised in VexCL?

My impression is, the ODE part is one single task, as all equations of ODE are within one block in the given example. Maybe the integration part runs in three parallel tasks. This results in four tasks, where (I think) the ODE task is a bottle neck (because the equations can become very large).

If this is right I would like to know, how to improve this concurrency. I think it make sense to combine ODE and INT horizontally. This results in 3 tasks, each of which cannot be further reduced at this level.

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ANSWER

Answered 2020-Feb-12 at 06:19

The example you linked to is doing a parameter study of the Lorenz system. That is, it solves a big number of the same equations with different parameters. The state type is vex::multivector, which packs together states (3D coordinates) of many Lorenz systems. This is an embarrassingly parallel problem and one can apply the odeint algorithm to the state types in lock-step. That is, operations like x += tau * dt where x and dt are large vectors, are performed on a GPU.

More details about odeint/vexcl implementation may be found in [1]. [2] is an interesting paper about how to extract parallelism in the case of coupled systems.

[1] Ahnert, Karsten, Denis Demidov, and Mario Mulansky. "Solving ordinary differential equations on GPUs." Numerical Computations with GPUs. Springer, Cham, 2014. 125-157. https://doi.org/10.1007/978-3-319-06548-9_7 (pdf)

[2] Mulansky, Mario. "Optimizing Large-Scale ODE Simulations." arXiv preprint arXiv:1412.0544 (2014).

Source https://stackoverflow.com/questions/60178543

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

Vulnerabilities

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Install vexcl

You can download it from GitHub.

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