Thanks in advance to anyone that might be able to help me with this problem. I have not been able to find a reference to it in the documentation on online sources, so I am turning to this group.
I am running R 2.4.1 under Red Hat Enterprise Linux 4, on an x86_64 platform (multi-core Intel Xeon processors, 3.6Ghx, 8GB of RAM). I have some rather complicated code (so I won't attach it here), but it is an iterative algorithm that takes data in the form of an R list, passes the entire list to some compiled C code, converts list items to GSL matrices and vectors, performs its various operations, and sends back the result to R. That result is then sent back to the compiled code until some kind of convergence (I won't bore the group with more details). The problem is that every .Call to the compiled code runs slower (I print the iteration time using Sys.time() and difftime() ). There is no logical reason for this (i.e., it's not a feature of the algorithm itself). I am using about 20% of my machine's available RAM (it's a large dataset and a memory-intensive algorithm), but there does not appear to be any swapping of memory to disk. I am sure that I am UNPROTECTING the SEXP's that I created, and I am freeing all of the GSL objects at the end of each function. The total RAM used does seem to increase, slowly but steadily, but the speed decrease occurs well before I start coming close to running out of RAM. Also, it is not just the compiled call that slows down. EVERYTHING slows down, even those that consist only of standard R functions. The time for each of these function calls is roughly proportional to the time of the .Call to the C function. Another observation is that when I terminate the algorithm, do a rm (list=ls()), and then a gc(), not all of the memory is returned to the OS. It is not until I terminate the R session that I get all of the memory back. In my C code, I am not doing anything to de-allocate the SEXP's I create, relying on the PROTECT/UNPROTECT mechanism instead (is this right?). I spent most of the day thinking I have a memory leak, but that no longer appears to be the case. I tried using Rprof(), but that only gives me the aggregated relative time spent in each function (more than 80% of the time, it's in the .Call). So I'm stuck. Can anyone help? Thanks, Michael -- Michael Braun Assistant Professor of Marketing MIT Sloan School of Management One Amherst St., E40-169 Cambridge, MA 02142 (617) 253-3436 [EMAIL PROTECTED] ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
