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NAMD APOA1 benchmark on i7-6900K CPU @ 3.20GHz +2080Ti + 2x1080Ti
----------------------------------------------------------------

# source: https://www.ks.uiuc.edu/Research/namd/utilities/
---

# delete FFTW_NMAD_*** before start (calculates specific FFT, for number of GPUs)

namd2 +p16 +devices 0,1,2 stmv.namd WallClock: 87.422867  CPUTime: 86.867996  Memory: 3174.035156 MB
namd2 +p8 +devices 0,1,2 stmv.namd  WallClock: 72.966972  CPUTime: 72.678879  Memory: 3104.535156 MB

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# Update clean 16 to 18 LTS
# VM install OpenBox

# sudo adduser [your-user-name] vboxsf
# shared on media/sf_share
sudo adduser ubuntu16 vboxsf

# remove old mono
sudo apt-get purge libmono* cli-common mono-runtime mono-devel
sudo apt-get autoremove

tobigithub

 # requires input file HUMAN.fasta (or others) writes it sorted by sequence length to HUMAN-sort.fasta
 # source https://www.biostars.org/p/153999/
 awk '/^>/ {printf("%s%s\t",(N>0?"\n":""),$0);N++;next;} {printf("%s",$0);} END {printf("\n");}'  HUMAN.fasta  |\
    awk -F '\t' '{printf("%d\t%s\n",length($2),$0);}' |\
    sort -k1,1n | cut -f 2- | tr "\t" "\n" > HUMAN-sort.fasta
    
    

tobigithub

**************************************************************
* Using a Ramdisk under Ubuntu
* Increases throughput  1:12  (151 Mbyte/sec to 1900 Mbyte/sec)
* Increases latency     1:300 (0.15 Mbyte/sec to 126 Mbyte/sec) 
***************************************************************

# create 10 Gbyte ramdisk ubuntu
sudo mkdir -p /media/ramdisk
sudo mount -t tmpfs -o size=10G tmpfs /media/ramdisk/

tobigithub

################################### Keras2DML: Parallely training neural network with SystemML####################################### 
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Input, Dense, Conv1D, Conv2D, MaxPooling2D, Dropout,Flatten
from keras import backend as K
from keras.models import Model
import numpy as np
import matplotlib.pyplot as plt

tobigithub

# -*- coding: utf-8 -*-
"""
@author: Tobias Kind // Fiehnlab (2018)
Program to convert MONA MSP files to MGF files
mona.fiehnlab.ucdavis.edu
"""


# parser reads MSP and converts to MGF
# needs to check for adducts including "[M+H]+" "[M-H]-" "[M]+"

tobigithub

import matplotlib
import numpy as np
import matplotlib.pyplot as plt

np.random.seed(19680801)

# example data
mu = 100  # mean of distribution
sigma = 15  # standard deviation of distribution
x = mu + sigma * np.random.randn(437)

tobigithub

"""
Created on Wed Aug 15 18:44:28 2018
Simple regression example for Keras (v2.2.2) with Boston housing data
@author: tobigithub
"""
from tensorflow import set_random_seed
from keras.datasets import boston_housing
from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.callbacks import EarlyStopping

tobigithub

# -*- coding: utf-8 -*-
"""
Created on Sat Aug 11 22:09:53 2018

@author: Keras doc
https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py
"""

'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs

tobigithub

# https://github.com/Microsoft/LightGBM/blob/ca30b8cb16e535ca5482c127986842bd4d5a99b8/docs/Parameters.md
# https://github.com/Microsoft/LightGBM/issues/127#issuecomment-267755141

library(data.table)
library(lightgbm)

data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label, free_raw_data = FALSE)