How to reduce runtime of gene data processing program? This program takes a huge data set as input, processes it, calculates and then writes the output to an array.  Most calculations may be quite simple, such as summation. In the input file, there are about 100 million rows and 3 columns.


first column is the name of the gene (total 100 millions)
second column is the specific value
third column is another value of each gene


The problem I face is a long runtime. How can I reduce it?

I need to write all new values (from GenePair to RM_pval with header) I calculated from the new file.

fi = open ('1.txt')
fo = open ('2.txt','w')

import math
def log(x):
    return math.log(x)

from math import sqrt

import sys
sys.path.append('/tools/lib/python2.7/site-packages')
import numpy
import scipy
import numpy as np
from scipy.stats.distributions import norm

for line in fi.xreadlines():
    tmp = line.split('\t')

    GenePair = tmp[0].strip()

    PCC_A = float(tmp[1].strip())
    PCC_B = float(tmp[2].strip())

    ZVAL_A = 0.5 * log((1+PCC_A)/(1-PCC_A))
    ZVAL_B = 0.5 * log((1+PCC_B)/(1-PCC_B))

    ABS_ZVAL_A = abs(ZVAL_A)
    ABS_ZVAL_B = abs(ZVAL_B)

    Var_A = float(1) / float(21-3) #SAMPLESIZE - 3
    Var_B = float(1) / float(18-3) #SAMPLESIZE - 3

    WT_A = 1/Var_A #float
    WT_B = 1/Var_B #float

    ZVAL_A_X_WT_A = ZVAL_A * WT_A #float
    ZVAL_B_X_WT_B = ZVAL_B * WT_B #float

    SumofWT = (WT_A + WT_B) #float
    SumofZVAL_X_WT = (ZVAL_A_X_WT_A + ZVAL_B_X_WT_B) #float

    #FIXED MODEL
    meanES = SumofZVAL_X_WT / SumofWT #float
    Var = float(1) / SumofWT #float
    SE = math.sqrt(float(Var)) #float
    LL = meanES - (1.96 * SE) #float
    UL = meanES - (1.96 * SE) #float
    z_score = meanES / SE #float
    p_val = scipy.stats.norm.sf(z_score)

    #CAL
    ES_POWER_X_WT_A = pow(ZVAL_A,2) * WT_A #float
    ES_POWER_X_WT_B = pow(ZVAL_B,2) * WT_B #float
    WT_POWER_A = pow(WT_A,2)
    WT_POWER_B = pow(WT_B,2)
    SumofES_POWER_X_WT = ES_POWER_X_WT_A + ES_POWER_X_WT_B
    SumofWT_POWER = WT_POWER_A + WT_POWER_B

    #COMPUTE TAU
    tmp_A = ZVAL_A - meanES
    tmp_B = ZVAL_B - meanES
    temp = pow(SumofZVAL_X_WT,2)

    Q = SumofES_POWER_X_WT - (temp /(SumofWT))      
    if PCC_A !=0 or PCC_B !=0:
        df = 0
    else:
        df = 1

    c = SumofWT - ((pow(SumofWT,2))/SumofWT)
    if c == 0:
        tau_square = 0
    else:
        tau_square = (Q - df) / c

    #calculation
    Var_total_A = Var_A + tau_square
    Var_total_B = Var_B + tau_square

    WT_total_A = float(1) / Var_total_A
    WT_total_B = float(1) / Var_total_B

    ZVAL_X_WT_total_A = ZVAL_A * WT_total_A
    ZVAL_X_WT_total_B = ZVAL_B * WT_total_B

    Sumoftotal_WT = WT_total_A + WT_total_B
    Sumoftotal_ZVAL_X_WT= ZVAL_X_WT_total_A + ZVAL_X_WT_total_B


    #RANDOM MODEL
    RM_meanES = Sumoftotal_ZVAL_X_WT / Sumoftotal_WT
    RM_Var = float(1) / Sumoftotal_WT
    RM_SE = math.sqrt(float(RM_Var))
    RM_LL = RM_meanES - (1.96 * RM_SE)
    RM_UL = RM_meanES + (1.96 * RM_SE)
    RM_z_score = RM_meanES / RM_Var
    RM_p_val = scipy.stats.norm.sf(RM_z_score)
The first thing for solving a problem like this is to find out what the actual bottleneck is. In your case the two most likely parameters are disk IO or CPU power.

Disk IO


Make sure the data sits on local drives and not on a network share or USB stick or similar.
Make sure your disks are reasonably fast (SSDs might help).
Put your input file and your output file on two different hard drives.
mmap might gain you some speed in reading and/or writing the data. At least in this case it seemed to make a difference.


CPU


There are several calculations you perform for each line in the input file but those numbers seem to be static (unless the script you posted is incomplete)


Var_A = float(1) / float(21-3) #SAMPLESIZE - 3 looks like a constant to me which can be calculated once.
Similar Var_B
From these two a bunch of other variables are calculated which would also be static: WT_A, WT_B, SumofWT, Var, SE, WT_POWER_A, WT_POWER_B, SumofWT_POWER, c
Might not make a huge impact but still seems redundant

As each line seems to be independently calculated this code would be a prime example for parallelization. I have not much experience with python and parallel programming but the ProcessPoolExecutor seems like a good start. Bascially utilize as many processes as you have cores and split your input into chunks to be distributed to the processes. You'd have to collect the results (and presumably make sure you write them out in the correct order) so this will make your code a bit more complicated but has the potential to speed up your calculations close to a factor of N (assuming N is the number of CPU cores) provided disk IO is not killing you.


You could also split the input, distribute it to other computers in the office and get the processing done there and collect the results.