title: H2O using too much RAM with Sparse Matrices
body: <p>I'm using H2O with a SVMLight sparse matrix of dimensions ~700,000 x ~800,000.  The file size is approximately ~800MB on disk.  But importing it into H2O takes up over 300GB of RAM? The process also takes too long (~15 minutes) to finish. </p>
  
  <p>I can create and store the sparse matrix in RAM using the Matrix package rather quickly in comparison.  The Sparse Matrix in that case takes ~1.2GB of RAM.</p>
  
  <p>Below is my code:</p>
  
  <pre><code>library(h2o)
  h2o.init(nthreads=-1,max_mem_size = "512g")
  
  x &lt;- h2o.importFile('test2.svmlight', parse = TRUE)
  </code></pre>
  
  <p>Here is my system:</p>
  
  <pre><code>openjdk version "1.8.0_121"
  OpenJDK Runtime Environment (build 1.8.0_121-b13)
  OpenJDK 64-Bit Server VM (build 25.121-b13, mixed mode)
  
  Starting H2O JVM and connecting: .. Connection successful!
  
  R is connected to the H2O cluster: 
  H2O cluster uptime:         2 seconds 76 milliseconds 
  H2O cluster version:        3.14.0.3 
  H2O cluster version age:    1 month and 8 days  
  H2O cluster name:           H2O_started_from_R_ra2816_fhv677 
  H2O cluster total nodes:    1 
  H2O cluster total memory:   455.11 GB 
  H2O cluster total cores:    24 
  H2O cluster allowed cores:  24 
  H2O cluster healthy:        TRUE 
  H2O Connection ip:          localhost 
  H2O Connection port:        54321 
  H2O Connection proxy:       NA 
  H2O Internal Security:      FALSE 
  H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4 
  R Version:                  R version 3.4.1 (2017-06-30) 
  </code></pre>
  
  <p>I would appreciate any advice because I really enjoy H2O and would like to use it for this project.</p>