Figure 2.

Benchmarks and run time. a) Fold increase in speed from parallelization. Ratios of the user wall time of execution of the R code (varSelRFBoot without previous model fit) between a run with a single Rmpi slave and runs with different numbers of Rmpi slaves (the number of simultaneously executing R processes) for five data sets (see [1] for details). In the legend, in parentheses the user wall time of the execution with a single Rmpi slave for each data set. In all cases (except "1", "60(2)", and "90(3)") there were four Rmpi slaves per node. The timings were obtained in an otherwise idle cluster with 30 nodes, each with two dual-core AMD Opteron 2.2 GHz CPUs and 6 GB RAM, running Debian GNU/Linux and a stock 2.6.8 kernel, with version 7.1.2 of LAM/MPI and version 2.1.4 (patched) of R. The values for "60(2)" refer two a configuration with 2 slaves per node (recall that a node with two dual core CPUs is not identical to a node with 4 CPUs), and the value "90(3)" to a configuration with 3 slaves per node. b) Scaling of user wall time. User wall time as a function of number of arrays and number of genes when executing the R function varSelRFBoot without previous model fit. Shown are three replicate runs. In each run, the arrays and genes are selected randomly from the complete original data set. Further details about the Prostate data set from [1]. Hardware and software as above. We used 4 Rmpi slaves per node (and, thus, a total of 120 slaves). c) User wall time of the web-based application. User wall time for complete runs (i.e., including upload of files and return of complete HTML page) for ten different data sets (see details in [1]). Under the name of each data set, the number of arrays and the number of genes are indicated. For each data set, three replicate runs were conducted. Hardware and software configuration as above, with the default settings for the web-based application (4 Rmpi slaves per node, and thus a total of 120 slaves).