4u9u5tsong

* command line call
  matlabc -nodesktop -nosplash -r "matlabpool 12;benchname = '$bench'; function_name ; quit"

* normcdf and precision
  p = normcdf([256-56 256+56],256, sqrt(128));vpa(p(2)-p(1))
  for k=50:56; p = normcdf([256-k 256+k],256, sqrt(128));display(vpa(p(2)-p(1))); end

4u9u5tsong

* combine pdfs
  pdfunite *.pdf all.pdf

4u9u5tsong

* timeout command
  kill the command after some specified time, to set a hard time limit for the executed command. 
  man timeout to read more.

4u9u5tsong

    ifstream pbinfile(argv[2]);
    
    vector<unordered_set<int> > numbers;
    while(getline(pbinfile, stemp)){
      istringstream buffer(stemp);
      vector<int> line((istream_iterator<int>(buffer)),
                       istream_iterator<int>());
      unordered_set<int> s(line.begin(), line.end());
      numbers.push_back(s);
    }

4u9u5tsong

#!/usr/bin/env python

import struct
import scipy as sp
import numpy as np


def binary(num):
    return ''.join(bin(ord(c)).replace('0b', '').rjust(8, '0') for c in struct.pack('!d', num))

4u9u5tsong

inpstream = subprocess.check_output(cmdstr).split('\n')
data = sp.genfromtxt(inpstream, dtype=np.uint64)

4u9u5tsong

# Got an answer from the Scipy user group:
# http://stackoverflow.com/questions/8955448/save-load-scipy-sparse-csr-matrix-in-portable-data-format
# A csr_matrix has 3 data attributes that matter: .data, .indices, and .indptr. All are simple ndarrays, so numpy.save will work on them. Save the three arrays with numpy.save or numpy.savez, load them back with numpy.load, and then recreate the sparse matrix object with:

# new_csr = csr_matrix((data, indices, indptr), shape=(M, N))
# So for example:

def save_sparse_csr(filename,array):
    np.savez(filename,data = array.data ,indices=array.indices,
             indptr =array.indptr, shape=array.shape )

4u9u5tsong

def float64Tobinary(num):
    return ''.join(bin(ord(c)).replace('0b', '').rjust(8, '0') for c in
                   struct.pack('!d', num))


def convertTo1DIntArray(d, start, end, patternwidth, f):
    sview = 'S' + str(patternwidth)
    return f(d[start:end]).view(sview)

4u9u5tsong

ssh -f -L 24800:127.0.0.1:24800 128.151.160.230 -N

4u9u5tsong

compile

1. in DRAMSim2 folder: make libdramsim.so
2. in Marss: scons dramsim=/full/path/to/DRAMSim2

flags

DRAMSim2/Makefile: comment -DLOG_OUTPUT for extra logs