Exercise 1
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@ -1052,18 +1052,18 @@ $ python Codes/socket_client.py lpqdh82 11279
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** Server for \pi with sockets in Python
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#+NAME:py_server
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#+begin_src python :tangle Codes/pi_server_python.py
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#!/usr/bin/env python
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import sys, os, socket
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from math import sqrt
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HOSTNAME = "localhost"
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PORT = 1666
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error_threshold = 1.e-4 # Stopping criterion
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def main():
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data = []
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s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # Create an INET socket
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s.bind( (HOSTNAME, PORT) ) # Bind the socket to the address and port
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s.bind( (socket.gethostname(), PORT) ) # Bind the socket to the address and port
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while True:
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s.listen(5) # Wait for incoming connections
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conn, addr = s.accept() # Accept connection
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@ -1078,6 +1078,7 @@ def main():
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** Server for \pi with sockets in Python
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#+NAME:py_server2
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#+begin_src python :tangle Codes/pi_server_python.py
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average = sum(data)/N # Compute average
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if N > 2: # Compute variance
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@ -1135,7 +1136,7 @@ def main():
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X = compute_pi()
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s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # Create an INET socket
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try: # Connect the socket to the address and port of the server
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s.connect( (HOSTNAME, PORT) )
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s.connect( (HOSTAME, PORT) )
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except socket.error:
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break
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message = str(X)
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@ -1680,7 +1681,7 @@ if __name__ == '__main__':
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- OpenMP is an extension of programming languages that enable the use of
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multi-threading to parallelize the code using directives.
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- The OpenMP library may be implemented using =pthreads=
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- Extensions in OpenMP5 to offload code execution to GPUs
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- Extensions in OpenMP 5.0 to offload code execution to GPUs
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- The same source code can be executed with/without OpenMP
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#+begin_src fortran
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@ -1693,64 +1694,164 @@ if __name__ == '__main__':
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!$OMP END PARALLEL
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#+end_src
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** OpenMP directives (Fortran)
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- ~!$OMP PARALLEL~ starts a new multi-threaded section.
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Everything inside this block is executed by /all/ the threads
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- ~!$OMP DO~ tells the compiler to split the loop among the different
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threads (by changing the loop boundaries for instance)
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- ~!$OMP END DO~ marks the end of the parallel loop.
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It contains an implicit synchronization.
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After this line, all the threads have finished executing the loop.
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- ~!$OMP END PARALLEL~ marks the end of the parallel section. Contains also an implicit barrier.
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- ~DEFAULT(SHARED)~ : all the variables (A,B,C) are in shared memory by default
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- ~PRIVATE(i)~ : the variable i is private to every thread
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** OpenMP directives (Fortran)
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- ~!$OMP CRITICAL ... !$OMP END CRITICAL~ : all the statements in this block are protected by a lock
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- ~!$OMP TASK ... !$OMP END TASK~ : define a new task to execute
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- ~!$OMP BARRIER~ : synchronization barrier
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- ~!$OMP SINGLE ... !$OMP END SINGLE~ : all the statements in this block are executed by a single thread
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- ~!$OMP MASTER ... !$OMP END MASTER~ : all the statements in this block are executed by the master thread
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** OpenMP
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*** Functions
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- ~omp_get_thread_num()~ : returns the ID of the current running
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thread (like ~MPI_Rank~)
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- ~omp_get_num_threads()~ : returns the total number of running
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threads (like ~MPI_Size~)
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- ~OMP_NUM_THREADS~ : Environment variable (shell) that fixes the
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number of threads to run
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*** Important
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- Multiple threads *can read* at the same memory address
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- Multiple threads **must not write** at the same address
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** Matrix multiplication
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\[ C_{ij} = \sum_{k=1}^N A_{ik} B_{kj} \]
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#+begin_src fortran
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do j=1,N
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do i=1,N
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C(i,j) = 0.d0
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do k=1,N
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C(i,j) = C(i,j) + A(i,k) * B(k,j)
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end do
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end do
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end do
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#+end_src
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** Matrix multiplication with OpenMP
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\[ C_{ij} = \sum_{k=1}^N A_{ik} B_{kj} \]
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#+LATEX: \begin{columns}
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#+LATEX: \begin{column}{0.5\textwidth}
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#+begin_src fortran
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!$OMP PARALLEL DO DEFAULT(SHARED) PRIVATE (i,j,k)
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do j=1,N
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do i=1,N
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C(i,j) = 0.d0
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do k=1,N
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C(i,j) = C(i,j) + A(i,k) * B(k,j)
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end do
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end do
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end do
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!$OMP END PARALLEL DO
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#+end_src
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#+LATEX: \end{column}
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#+LATEX: \begin{column}{0.4\textwidth}
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- Loop is parallelized over ~j~
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- Writing in ~C(i,j)~ is OK
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#+LATEX: \end{column}
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#+LATEX: \end{columns}
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** Matrix multiplication with OpenMP
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\[ C_{ij} = \sum_{k=1}^N A_{ik} B_{kj} \]
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#+LATEX: \begin{columns}
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#+LATEX: \begin{column}{0.5\textwidth}
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#+begin_src fortran
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!$OMP PARALLEL DEFAULT(SHARED) PRIVATE (i,j,k)
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!$OMP DO COLLAPSE(2)
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do j=1,N
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do i=1,N
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C(i,j) = 0.d0
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do k=1,N
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C(i,j) = C(i,j) + A(i,k) * B(k,j)
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end do
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end do
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end do
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!$OMP END DO
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!$OMP END PARALLEL
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#+end_src
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#+LATEX: \end{column}
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#+LATEX: \begin{column}{0.4\textwidth}
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- Loop is parallelized over pairs ~(i,j)~
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- Writing in ~C(i,j)~ is OK
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#+LATEX: \end{column}
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#+LATEX: \end{columns}
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* Exercises
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** Monte Carlo
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** Exercise 1: Monte Carlo
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1. Write a Fortran src_fortran{double precision function compute_pi(M)} that computes
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$\pi$ with the Monte Carlo algorithm using $M$ samples
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2. Call it like this:
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#+begin_src fortran
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2. Call it using this main program:
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#+begin_src fortran :tangle Exercises/Ex1/main.f90
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program pi_mc
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implicit none
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integer :: M
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logical :: iterate
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double precision :: sample
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double precision, external :: compute_pi
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call random_seed() ! Initialize random number generator
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read (*,*) M ! Read number of samples in compute_pi
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open(unit=11, file='fortran_out.fifo', status='old', action='write', &
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access='stream',form='formatted')
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iterate = .True.
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do while (iterate) ! Compute pi over N samples until 'iterate=.False.'
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sample = compute_pi(M)
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write(*,*) sample
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read (*,*) iterate
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sample = compute_pi()
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write(11,*) sample
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end do
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end program pi_mc
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#+end_src
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** Monte Carlo
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** Exercise 1: Monte Carlo
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3. Write a Fortran src_fortran{double precision function compute_pi(M)} that computes
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$\pi$ with the Monte Carlo algorithm using $M$ samples
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#+begin_src fortran
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program pi_mc
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3. Write a Python server =pi_server.py= that receives samples of \pi in a socket
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and compute the running average of \pi. Its address and port
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number are written in a file =server.cfg=.
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4. Write a Python script =pi_bridge.py= that reads samples of \pi in a named pipe
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=fortran_out.fifo= and sends the samples to the server.
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This script can read the the address and port number of the
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server from the file =server.cfg=.
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5. When the convergence criterion is reached, the server informs
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the bridges that they can stop.
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*** Running a simulation on multiple nodes
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- Run a single server
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- Run one bridge per compute node using the =mpiexec= command
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- Run one Fortran process per core using the =mpiexec= command
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** Exercise 2: Divide and conquer for matrix multiplication
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* Solutions :noexport:
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** Exercise 1: Monte Carlo
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*** Compute_pi.f90
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#+begin_src fortran :tangle Exercises/Ex1/compute_pi.f90
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double precision function compute_pi()
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implicit none
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integer :: M
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logical :: iterate
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double precision :: sample
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double precision, external :: compute_pi
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call random_seed() ! Initialize random number generator
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read (*,*) M ! Read number of samples in compute_pi
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iterate = .True.
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do while (iterate) ! Compute pi over N samples until 'iterate=.False.'
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sample = compute_pi(M)
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write(*,*) sample
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read (*,*) iterate
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end do
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end program pi_mc
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#+end_src
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** Monte Carlo (solution)
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#+begin_src fortran
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double precision function compute_pi(M)
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implicit none
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integer, intent(in) :: M
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integer, parameter :: M=100000000
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double precision :: x, y, n_in
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integer :: i
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@ -1762,11 +1863,118 @@ double precision function compute_pi(M)
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n_in = n_in+1.d0
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end if
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end do
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compute_pi = 4.d0*n_in/dble(nmax)
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compute_pi = 4.d0*n_in/dble(M)
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end function compute_pi
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#+end_src
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*** Compilation
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#+begin_src bash
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gfortran compute_pi.f90 main.f90 -o pi.x
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#+end_src
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*** Python bridge
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#+begin_src python :tangle Exercises/Ex1/pi_bridge.py
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#!/usr/bin/env python
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import os, sys, socket
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server_config = "server.cfg" # Configuration file
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pipe_in = "fortran_out.fifo" # Named pipe for input
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def main():
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# Read hostname and port number from config file
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with open(server_config, 'r') as f:
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HOSTNAME = f.readline().strip()
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PORT = int( f.readline() )
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# Open named pipe for reading the output of the Fortran executables
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p_in = open(pipe_in, 'r')
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print("Bridge connects to", HOSTNAME, PORT)
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while True:
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s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # Create an INET socket
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try: # Connect the socket to the address and port of the server
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s.connect( (HOSTNAME, PORT) )
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except socket.error:
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print("Server inactive")
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break
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X = p_in.readline()
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message = str(X)
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s.send(message.encode()) # Send the data
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reply = s.recv(128).decode('utf-8') # Read the reply of the server
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if reply == "STOP": break
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if __name__ == '__main__':
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main()
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#+end_src
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*** Python server
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#+begin_src python :tangle Exercises/Ex1/pi_server.py
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#!/usr/bin/env python
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import sys, os, socket
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from math import sqrt
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error_threshold = 1.e-5 # Stopping criterion
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PORT = 1666 # Port number
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server_config = "server.cfg" # Config file for the server
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def main():
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HOSTNAME = "localhost" #socket.gethostname()
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with open(server_config, 'w') as f: # Write hostname to config file
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f.write(HOSTNAME+"\n")
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f.write(str(PORT)+"\n")
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print("Server is", HOSTNAME, PORT)
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data = []
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s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # Create an INET socket
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s.bind( (HOSTNAME, PORT) ) # Bind the socket to the address and port
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while True:
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s.listen(5) # Wait for incoming connections
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conn, addr = s.accept() # Accept connection
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X = ""
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while True: # Buffered read of the socket
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message = conn.recv(128)
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X += message.decode('utf-8')
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if len(message) < 128: break
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data.append( float(X) )
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N = len(data)
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average = sum(data)/N # Compute average
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if N > 2: # Compute variance
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l = [ (x-average)*(x-average) for x in data ]
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variance = sum(l)/(N-1.)
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else:
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variance = 0.
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error = sqrt(variance)/sqrt(N) # Compute error
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print('%f +/- %f'%(average,error))
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# Stopping condition
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if N > 2 and error < error_threshold:
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conn.send("STOP".encode())
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break
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else:
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conn.send("OK".encode())
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conn.close()
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if __name__ == "__main__":
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main()
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#+end_src
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*** Script
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#+begin_src bash :tangle Exercises/Ex1/run.sh
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#!/bin/bash
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mkfifo fortran_out.fifo
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python pi_server.py &
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mpiexec --pernode python pi_bridge.py &
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mpiexec pi.x
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rm fortran_out.fifo
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#+end_src
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** Divide and conquer for matrix multiplication
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* Figures :noexport:
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#+BEGIN_SRC dot :output file :file merge.png
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