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 being a grown up

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 seamless life in many ways

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https://github.com/sephiroce/

cuda benchmark by using NVIDIA sample - 컴퓨터

ref url: https://www.pugetsystems.com/labs/hpc/TitanXp-vs-GTX1080Ti-for-Machine-Learning-937/

nbody -benchmark -numbodies=256000

(1) GTX 1070 4137 GFLOP/s
(1) GTX 1080Ti 7514 GFLOP/s
(1) Titan X Pascal 7524 GFLOP/s
(1) Titan Xp 7904 GFLOP/s
--------------------------------------
In my case,
> Windowed mode
> Simulation data stored in video memory
> Single precision floating point simulation
> 1 Devices used for simulation
gpuDeviceInit() CUDA Device [0]: "Graphics Device
> Compute 6.1 CUDA device: [Graphics Device]
number of bodies = 256000
256000 bodies, total time for 10 iterations: 1611.591 ms
= 406.654 billion interactions per second
= 8133.082 single-precision GFLOP/s at 20 flops per interaction
(The range of GLOP/s was from 8066.468 to 8133.082)

Nvidia driver: 375.26
cuda: 8.0
g/c: titan xp
cpu: 8700k
m/b: ga-z370-hd3
ram: dominator 3466kHz(XMP applied)

* GTX 680 showed "1606.946 single-precision GFLOP/s at 20 flops per interaction"

written time : 2018-01-02 12:50:30.0

ubuntu network driver for ga-z370-hd3 - 컴퓨터

If ubuntu(ubuntu14.01) can not find your onboard nic(gigabyte z370 hd3), then download n/w driver and install it as follows

1. Download from driver "https://downloadcenter.intel.com/ko/download/15817?_ga=2.112617741.80207377.1514690229-1690502750.1514689335"

2. uncompress the driver then
  cd DRIVER_FOLDER/src;sudo make install

3. modprobe e1000e

written time : 2017-12-31 12:21:10.0

조금 단순 하더라도 진행하다보면 - 생각

적어도 쭈뼜거리는 것보다는 낫지 않을까 싶은데,
균형을 잡기가 쉽지는 않다.

written time : 2017-12-02 21:58:58.0
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