C / C++ / MFC

#include<graphics.h>
void drawrect(int l,int t,int r,int d,int col){
  if(col==0)setcolor(RED);
  else setcolor(BLUE);
  rectangle(l,t,r,d);
}
void drawpiece(int l,int t,int r,int d,int col){
  for(int i=1;i<5;i++)
    drawrect(i*16+l,t,i*16+r,d,col);
}
DWORD WINAPI Thread2(void* lparam){
  int px2=161,py2=17;
  for(int i=0;i<10;i++,py2+=16,delay(100))
    drawpiece(px2,py2,px2+16,py2+14,1);
}
int main()
{   
    initwindow(600,600,"TEST");   
    CreateThread(0,0,&Thread2,NULL,0,0);
    int px1=17,py1=17;
    for(int i=0;i<10;i++,py1+=16,delay(100))
      drawpiece(px1,py1,px1+14,py1+14,0);    
    getch();
    return EXIT_SUCCESS;
}

I am working on a project about data mining. my company has given me 6 million dummy customer info of twitter. I was assigned to find out the similarity between any two users. can anyone could give me some ideas how to deal with the large community data? Thanks in advance

Problem : I use the tweets & hashtag info(hashtags are those words highlighted by user) as the two criteria to measure the similarity between two different users. Since the large number of users, and especially there may be millions of hastags & tweets of each user. Can anyone tell me a good way to fast calculate the similarity between two users? I have tried to use FT-IDF to calculate the similarity between two different users, but it seems infeasible. can anyone have a very super algorithm or good ideas which could make me fast find all the similarities between users?

For example:
user A's hashtag = {cat, bull, cow, chicken, duck}
user B's hashtag ={cat, chicken, cloth}
user C's hashtag = {lenovo, Hp, Sony}

clearly, C has no relation with A, so it is not necessary to calculate the similarity to waste time, we may filter out all those unrelated user first before calculate the similarity. in fact, more than 90% of the total users are unrelated with a particular user. How to use hashtag as criteria to fast find those potential similar user group of A? is this a good idea? or we just directly calculate the relative similarity between A and all other users? what algorithm would be the fastest and customized algorithm for the problem?