On Tue, 2004-06-01 at 18:05, Havoc Pennington wrote: > Something like: > > 5M sum of per-app icon theme caching > 5M sum of per-app base gtk_init() overhead > 10M sum of per-email data in Evolution > 7M base evo overhead with no mail loaded > 30M sum of all executable pages (libraries and binaries) > ... I don't think people agree with me, but in my opinion it is important to measure the working set. A program can malloc() 500 MB and then just sit in poll() never touching that memory. An approach that could give you something close to what you are after is to LD_PRELOAD a new malloc() for the entire desktop and have that new malloc() report a stack trace to another application that could then process the data: - Calculate the total amount of memory used by applications: sum of all mmap()ed pages in physical RAM + sum of all anonymous pages in physical RAM where "sum of all anonymous pages in physical RAM" is calculated by for each application subtracting the number of mapped pages in RAM from the RSS. - Report like memprof does now, the amount of memory allocated by a function and its children. Divide all numbers by the total amount of memory used. The amount of memory used by mmap()ed files is easy to measure: - scan /proc and build a list of mmap()ed files - mmap() of those files - use mincore() to find out how many pages of those files are actually in RAM. (I have a program that does this somewhere). > i.e. try to get an idea of where focused optimization could have the > most impact on the desktop overall - what percentage of TOTAL memory > usage for the whole desktop can be blamed on each optimizable item, with > sufficient granularity to be useful. The above might give you something like what you are after. It would be possible to report at a filename granularity instead of function granularity, which might be interesting as Alex suggested. Soeren
