https://github.com/lchsk/ney

A header-only parallel functions library for Intel Xeon/Xeon Phi/GPUs
https://github.com/lchsk/ney

cuda gpu linux parallel phi scientific xeon xeonphi

Last synced: about 1 month ago
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A header-only parallel functions library for Intel Xeon/Xeon Phi/GPUs

• Host: GitHub
• URL: https://github.com/lchsk/ney
• Owner: lchsk
• Created: 2015-07-24T09:03:39.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2015-09-21T10:03:55.000Z (over 9 years ago)
• Last Synced: 2024-11-10T17:41:24.378Z (3 months ago)
• Topics: cuda, gpu, linux, parallel, phi, scientific, xeon, xeonphi
• Language: C++
• Homepage:
• Size: 2.39 MB
• Stars: 2
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        
## Build status

| [Linux][lin-link] | [Coveralls][cov-link] |

| :---------------: | :-------------------: |

| ![lin-badge]      | ![cov-badge]          |

[lin-badge]: https://travis-ci.org/lchsk/ney.png?branch=master "Travis build status"

[lin-link]:  https://travis-ci.org/lchsk/ney "Travis build status"

[cov-badge]: https://coveralls.io/repos/lchsk/ney/badge.png?branch=master

[cov-link]:  https://coveralls.io/r/lchsk/ney?branch=master

## How to Download

1. Download source code with git by using a command:

`git clone https://github.com/lchsk/ney.git --recursive`

(`--recursive` option will download submodules (Google Test, Thrust) into the `thirdparty` directory)

## Compilation

In order to compile the library, a C++ compiler must be present. Intel C++ Compiler is advised (versions 14 and 15 were used) to exploit all features of the library. For testing purposes, GNU C++ Compiler (`g++`) may be used as well (at least version 4.6.3).

For compiling code for GPUs, please see paragraph `Running examples on GPU`

**Note: the library is designed to work on Linux. Its behaviour on other systems is undefined. Testing was done on the following distributions: Ubuntu 14.04, Scientific Linux 6.3.**

## Running examples on CPU

1. Enter downloaded directory

2. `make example`

3. `cd examples`

4. Run a selected executable, e.g., `./saxpy`

## Running examples on GPU

**Note that in order to run anything on the GPU, CUDA toolkit must be installed. It can be downloaded from https://developer.nvidia.com/cuda-toolkit**

1. Enter downloaded directory

2. `cd examples`

3. `cd gpu`

4. `make saxpy`

4. If you run the executable: `./saxpy`, it will run on the GPU

## Running tests

1. Enter downloaded directory

2. `make && make tests`