Concurrency and parallel programming

• Multi-threaded applications through             .

#include <string>
#include <iostream>
#include <thread>

using namespace std;

//The functions we want to make the thread run.
void task1(string msg)
{
    cout << "task1 says: " << msg;
}

void task2(string msg)
{
    cout << "task1 says: " << msg;
}

//Main loop.
int main()
{
    thread t1(task1, "Task 1 executed");
    thread t2(task2, "Task 1 executed");
    //let main wait for t1 and t2 to finish.
    t1.join();
    t2.join();
}

Rcpp

Dual core

Concurrency and parallel programming

• Multi threaded applications through             .

#include <string>
#include <iostream>
#include <thread>

using namespace std;

//The functions we want to make the thread run.
void task1(string msg)
{
    cout << "task1 says: " << msg;
}

void task2(string msg)
{
    cout << "task1 says: " << msg;
}

//Main loop.
int main()
{
    thread t1(task1, "Task 1 executed");
    thread t2(task2, "Task 1 executed");
    t1.join();
    t2.join();
}

Rcpp

Parallel computing

• Parallel computing is extremely simple to implement from R. 

# install.packages('foreach'); install.packages('doSNOW')
library(foreach)
library(doSNOW)
cl <- makeCluster(2, type = "SOCK")
registerDoSNOW(cl)

getDoParName()


#matrix operators
x <- foreach(i=1:8, .combine='rbind', .packages='wholebrain' ) %:%
   foreach(j=1:2, .combine='c', .packages='wholebrain' ) %dopar% {
     l <- runif(1, i, 100)
     i + j + l  
   }

R package

• Why R?
  • Standard data analysis:
    • load some data
    • estimate the density distribution.
    • plot it

xx <- faithful$eruptions
fit <- density(xx)
plot(fit)

R package

• Why R?

#Line 1: loading
xx <- faithful$eruptions
#Line 2: estimate density
fit1 <- density(xx)
#Line 2: draw 10'000 bootstraps
fit2 <- replicate(10000, {
    x <- sample(xx,replace=TRUE);
    density(x, from=min(fit1$x), to=max(fit1$x))$y
})
#Line 3: compute 95% error "bars"
fit3 <- apply(fit2, 1, quantile,c(0.025,0.975))
#Line 4: plot the estimate
plot(fit1, ylim=range(fit3))
#Line 5: add estimation error as shaded region
polygon(c(fit1$x,rev(fit1$x)), c(fit3[1,], rev(fit3[2,])), col=’grey’, border=F)
#Line 6: add the line again since the polygon overshadows it.
lines(fit1)

What other language can do this in 6 lines of code?