title: Covariance of large matrix on R with library parallel
body: <p>I want to calculate the covariance matrix of a large matrix Z that has 8 rows and 27001 columns (creating a 27001 X 27001 covariance matrix). Then I want to simulate multivariate noise with this covariance matrix.</p>
  <p>To avoid ram size allocation issues I used the library parallel but my code did not work; please help me to correct it. My problem is working with matrix</p>
  <p>My attempt:</p>
  <p>Step 1</p>
  <pre><code>library(parallel)
  cl &lt;- makeCluster(detectCores())
  system.time(res &lt;- parRapply(cl,  Z, cov))
  # Error in checkForRemoteErrors(val) : 
  #  8 nodes produced errors; first error: supply both 'x' and 'y' or a matrix-like 'x'
  # Timing stopped at: 0.03 0.01 0.08
  </code></pre>
  <p>Step 2</p>
  <pre><code>library(mvtnorm)
   
  noise1&lt;- rmvnorm(nrow(res), mean = rep(0,nrow(res)) , sigma =  res )
  </code></pre>