title: Use Chapel to handle massive matrix
body: <p>I've recently come across Chapel and I'm very keen to try it out.  I have a two-fold problem I'm hoping it can solve.</p>
  
  <p>I typically work in Python or C++.  Java when backed into a corner.</p>
  
  <p>I have two matrices <code>I</code> and <code>V</code>. Both are sparse and of dimension about 600K x 600K, populated at about 1% density.</p>
  
  <p>First, using SciPy, I can load both from a SQL database into memory at the moment. However, I expect our next iteration will be simply too large for our machines. Perhaps 1.5M^2. In a case like that, RDDs from Spark may work for the load.  I wasn't able to get PyTables to make this happen.  I understand this is described as an "Out-of-core" problem.</p>
  
  <p>Even if they do get loaded, doing <code>I'IV</code> goes OOM in minutes.  (Here <code>I'</code> is transpose), so I'm looking into distributing this multiplication over multiple cores (which SciPy can do) and multiple machines (which it cannot, so far as I know).  Here, Spark falls down but Chapel appears to answer my prayers, so-to-speak.</p>
  
  <p>A serious limitation is budget on machines.  I can't afford a Cray, for instance.  Does the Chapel community have a pattern for this?</p>