[Bioc-devel] C++ code performance issues

On 03/21/2013 11:30 AM, Peter Glaus wrote:

Hi,
I am working on BitSeq package, which has both command line C++ 
version and
Bioconductor version in which R calls the same C++ code with .C 
function. While
testing the development version of package on R 3.0.0 I noticed that 
the "R
version" runs much slower: 2-3 TIMES slower than the pure C++ 
implementation.
Interestingly, the stable release of the "R version" seems to be as 
fast as C++
version. (The underlying code has changed slightly but there 
shouldn't be much
difference)
Is there any reason for such behavior? Has anyone encountered similar 
issue? Is
there a way to make the C++ code called from R faster?

More details:
I compiled the C++ code with same g++ flags (... -O3 -pipe -fpic 
-g... ) and
removed OpenMP support from both.
The functions take exactly the same input (input is read from a 
file), and
produce exactly same output (using same seed). A specific computation 
that took
the C++ version 12minutes, took the R(C++) version 47minutes. There 
is no IO
during that part of the code and there was just one 
R_CheckUserInterrupt() call
during this time (I changed the code, so that there would not be many 
of these
calls.).
There are just few differences in the last stable release and that 
seems to run
even faster than current C++ (10m). (The stable release uses -O2 
while compiling
the c++ code.)

Can you narrow this down to something more reproducible, e.g., a 
particular call that causes problems, including the platform(s) on 
which you are seeing issues?

Maybe you're running out of memory (because R is holding memory that 
the command line does not access)?

Probably you spend most of your time 'in C' or 'in R', rather than 
moving between them?

You could try, on linux / mac, a cheap C-level guesstimate of where 
time is spent by running under gdb

  R -d gdb
  (gdb) run

and then periodically breaking with cntrl-C and looking where you are

  (gdb) backtrace
   ## stack trace
   (gdb) continue

and comparing the same under the commandline

  > gdb ./bitseq

or doing some more serious profiling as outlines in section 3.4 of 
'Writing R Extensions"; probably you would start by getting a short 
reproducible example.

Martin

Thanks,
Peter.