But typically what you are looking for is the "number of operations" per unit time. In the case of R, what you would probably be doing is to monitor the time that it takes to go through a number of scenarios and then divide this number by the CPU time and you will get "number of operations per CPU seconds". In most cases, even if you are running on a system that has multiple jobs being run, where your elapsed time may vary greatly depending on what else is running, the amount of CPU time that you need to perform some sequence of operations (if the data is the same) will remain fairly constant.
So what I tend to look at is the amount of CPU time that it takes to process a set of data of some given size. I am usually doing data analysis on computer systems where we are processing several million transactions a day. The data is a record of each transaction with the amount of response time. R is great for working with this data and breaking it down by transactions and time of day to see what the variations in performance are. This is also correlated with the performance metrics from the operating system (CPU time, I/O, memory usage, network bandwidth, etc.). In these cases the relationship between the CPU time used and the number of transactions processed remains relatively constant. The times per unit of work will increase with the size of the data since there is sorting/merging going on and these are not linear with the size of the data. On 10/29/07, Thomas Lumley <[EMAIL PROTECTED]> wrote: > On Sun, 28 Oct 2007, kevinchang wrote: > > > > > Hi all, > > > > Since proc.time return three different kind of times (user, system and > > elapsed) , I am wondering which one is right for calculating flops. > > Probably none of them. The 'user'+'system' time is the amount of CPU time > that can be blamed on R. For most applications this time is mostly not > used in floating point calculations, so it doesn't really seem meaningful > to use it in a denominator and call the result 'flops'. > > Sometimes even the 'user'+'system' time is an underestimate of R's > resource use -- for example, indexing a large file in RSQLite takes a long > elapsed time with relatively little user or system time, apparently > because the system is waiting for disk seeks [the disk is being treated as > a random-access device]. > > -thomas > > > > Thomas Lumley Assoc. Professor, Biostatistics > [EMAIL PROTECTED] University of Washington, Seattle > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Jim Holtman Cincinnati, OH +1 513 646 9390 What is the problem you are trying to solve? ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
