Dear all, I am managing a departmental purchase of an Opteron based workstation/server for scientific computing on which we will be running R. The environment will probably be either Unix/Linux or Solaris and the amount of RAM will be 8-16Gb, depending on the number of processors. My main concerns are the following: 1. How much does R benefit from passing from one processor to two/four processor machines? Consider that the typical intensive use of the server will be represented by simulation studies with many repeated loops. 2. How does R cope with parallelization and/or parallelized compiled code ? I would be very grateful if someone could give suggestions and/or point me to information on the above mentioned issues. Regards, Simone Giannerini -- ______________________________________________________ Simone Giannerini Dipartimento di Scienze Statistiche "Paolo Fortunati" Universita' di Bologna Via delle belle arti 41 - 40126 Bologna, ITALY Tel: +39 051 2098248 Fax: +39 051 232153 E-mail: giannerini at stat.unibo.it
On 3/6/06 11:50 AM, "Simone Giannerini" <sgiannerini at gmail.com> wrote:> Dear all, > > I am managing a departmental purchase of an Opteron based > workstation/server for scientific computing on which we will be > running R. > The environment will probably be either Unix/Linux or Solaris and the > amount of RAM will be 8-16Gb, depending on the number of processors. > My main concerns are the following: > > 1. How much does R benefit from passing from one processor to > two/four processor machines? Consider that the typical intensive use > of the server > will be represented by simulation studies with many repeated loops.You will have to implement some parallelization code yourself in order to take full advantage of the multiple processors. See below.> 2. How does R cope with parallelization and/or parallelized compiled code ?You might look at the Rmpi and snow packages for parallelization from within R. We use Rmpi and snow for analyses like simulation and have found these applications quite easy to implement in parallel from within R.
On Mon, 6 Mar 2006, Simone Giannerini wrote:> The environment will probably be either Unix/Linux or Solaris and the > amount of RAM will be 8-16Gb, depending on the number of processors. > My main concerns are the following: > > 1. How much does R benefit from passing from one processor to > two/four processor machines? Consider that the typical intensive use > of the server > will be represented by simulation studies with many repeated loops.The typical way that R is used on multiprocessor systems is running more than one program, rather than parallel processing. If four people are using the computer or if one person splits 10,000 iterations of a simulation into 4 sets of 2,500 you will be using all four processors.> 2. How does R cope with parallelization and/or parallelized compiled code ? >It doesn't really. There are interfaces to MPI and PVM and there is the possibility of using a parallel BLAS to speed up linear algebra. These won't help much unless the server is under fairly low load so that a single program can use more than 100% of a single processor. Our multiprocessor Opteron servers are rarely that underutilized. -thomas Thomas Lumley Assoc. Professor, Biostatistics tlumley at u.washington.edu University of Washington, Seattle