Rajitha Hathurusinghe ratmcu

## referencecollapse.cpp
#include <iostream>

template<typename T>
void fnc(T&& x)
{
    // std::cout << x;
    x++;
}

int main()

## gist:4028067ec3b6e46f20a5f02d6eeac63c
 find ../build/bin/ -type f \( -name \*.so \) -exec nm -A {} + | grep symBoL

## README.md

      
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                ratmcu
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              Last active
              August 29, 2020 14:18
            
              
                virtual memory manager with paging in c++
              
          
    compiling

$g++ --std=c++11  main.cpp -lpthread  -o sim
running (make sure the simulation input file "input.txt" is places at the location with the current name)

./sim

  
## winsock2_UPD_server.cpp
#undef UNICODE

#define WIN32_LEAN_AND_MEAN

#include <windows.h>
#include <winsock2.h>
#include <ws2tcpip.h>
#include <stdlib.h>
#include <stdio.h>
#include <string>

## main.c
#include "stdio.h"
#include <stdlib.h>
#include <math.h>
#include <mpi.h>

int main(int argc, char *argv[])
{
    int process_Rank, size_Of_Comm;
    double distro_Array[] = {1, 2, 3, 4, 5, 6 ,7, 8, 9, 10, 11, 12, 13, 14}; // data to be distributed
    int N = sizeof(distro_Array)/sizeof(distro_Array[0]);

## file_seeker.py
import os
extension = '.csv'
tree = os.walk('./dataset')
paths = sorted([os.path.join(f[0], name) for f in tree if len(f[2])!=0 for name in f[2] if os.path.splitext(name)[-1] == '.csv'])

## multiprocessing_with_joining_back_results.py
import multiprocessing
import os
# split a list into evenly sized chunks
def chunks(l, n):
    return [l[i:i+n] for i in range(0, len(l), n)]


def do_job(job_id, data_slice, queue):
    for item in data_slice:
        print ("job", job_id, item)
	#include <iostream>

	template<typename T>
	void fnc(T&& x)
	{
	// std::cout << x;
	x++;
	}

	int main()
	#undef UNICODE

	#define WIN32_LEAN_AND_MEAN

	#include <windows.h>
	#include <winsock2.h>
	#include <ws2tcpip.h>
	#include <stdlib.h>
	#include <stdio.h>
	#include <string>
	#include "stdio.h"
	#include <stdlib.h>
	#include <math.h>
	#include <mpi.h>

	int main(int argc, char *argv[])
	{
	int process_Rank, size_Of_Comm;
	double distro_Array[] = {1, 2, 3, 4, 5, 6 ,7, 8, 9, 10, 11, 12, 13, 14}; // data to be distributed
	int N = sizeof(distro_Array)/sizeof(distro_Array[0]);
	import os
	extension = '.csv'
	tree = os.walk('./dataset')
	paths = sorted([os.path.join(f[0], name) for f in tree if len(f[2])!=0 for name in f[2] if os.path.splitext(name)[-1] == '.csv'])
	import multiprocessing
	import os
	# split a list into evenly sized chunks
	def chunks(l, n):
	return [l[i:i+n] for i in range(0, len(l), n)]


	def do_job(job_id, data_slice, queue):
	for item in data_slice:
	print ("job", job_id, item)