title: Parallel biocomputing.
abstract: BACKGROUND
  With the advent of high throughput genomics and high-resolution imaging techniques, there is a growing necessity in biology and medicine for parallel computing, and with the low cost of computing, it is now cost-effective for even small labs or individuals to build their own personal computation cluster.
  
  
  METHODS
  Here we briefly describe how to use commodity hardware to build a low-cost, high-performance compute cluster, and provide an in-depth example and sample code for parallel execution of R jobs using MOSIX, a mature extension of the Linux kernel for parallel computing. A similar process can be used with other cluster platform software.
  
  
  RESULTS
  As a statistical genetics example, we use our cluster to run a simulated eQTL experiment. Because eQTL is computationally intensive, and is conceptually easy to parallelize, like many statistics/genetics applications, parallel execution with MOSIX gives a linear speedup in analysis time with little additional effort.
  
  
  CONCLUSIONS
  We have used MOSIX to run a wide variety of software programs in parallel with good results. The limitations and benefits of using MOSIX are discussed and compared to other platforms.
authors.affiliation: Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Kenneth S
authors.initials: KS
authors.lastname: Kompass
authors.affiliation: Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Hoffmann
authors.affiliation: Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.
authors.forename: John S
authors.initials: JS
authors.lastname: Witte