Hi Martin, Here is a run that illustrates the problem. Am I doing something wrong?
[EMAIL PROTECTED]:~/m4ri-20080514/testsuite> time ./ bench_multiplication 5000 2048 n: 5000, cutoff: 2048, cpu cycles: 2670143764 real 0m2.768s user 0m2.760s sys 0m0.008s [EMAIL PROTECTED]:~/m4ri-20080514/testsuite> time ./ bench_multiplication 5000 3000 n: 5000, cutoff: 3000, cpu cycles: 746344039 real 0m3.277s user 0m3.252s sys 0m0.024s Bill. On 15 May, 18:42, Martin Albrecht <[EMAIL PROTECTED]> wrote: > On Thursday 15 May 2008, Bill Hart wrote: > > > > > Hi Martin, > > > The cycle counter in your bench code seems to give random values on > > the 2.4GHz Opteron with SUSE 9 linux that I have access to, which has > > Magma 2-14.10 on it. > > > Anyhow, here are the Magma times: > > > A := RandomMatrix(GF(2),10^4,10^4); > > > B := RandomMatrix(GF(2),10^4,10^4); > > > t := Cputime(); C := A*B; Cputime(t); > > > 2.940 > > > > A := RandomMatrix(GF(2),2*10^4,2*10^4); > > > B := RandomMatrix(GF(2),2*10^4,2*10^4); > > > t := Cputime(); C := A*B; Cputime(t); > > > 16.570 > > > > A := RandomMatrix(GF(2),2^14,2^14); > > > B := RandomMatrix(GF(2),2^14,2^14); > > > t := Cputime(); C := A*B; Cputime(t); > > > 9.250 > > > The corresponding times for your code are approximately (hand timed; > > adjusted for generation of random matrices): > > > 10^4: 8s > > 2*10^4: 59s > > 2^14: 43s > > > I think it is polynomials over GF2 which Magma 2-14.x is much faster > > at rather than linear algebra. > > > Bill. > > Hi Bill, > > I cannot explain the random behaviour of the cpucycles counter. The cpucycles > library is by Dan Bernstein so I'd suspect it is carefully coded. Some > potential sources of variance are: dynticks (i.e. the kernel doesn't have a > fixed tick rate, this is done to safe power), CPU frequency scaling and other > applications. On the other hand, I'd suspect I don't need to tell you since > you probably benchmarked more code than I did. To account for the variance I > have the small run_bench.py script in the testsuite directory. Run it like > this: > > ./run_bench.py -t mul -n 10000 -c 3200 > > It'll run the experiment 17 times (17 for no particular reason) and return the > median runtime. -n is the size and -c the cutoff. > > Cheers, > Martin > > -- > name: Martin Albrecht > _pgp:http://pgp.mit.edu:11371/pks/lookup?op=get&search=0x8EF0DC99 > _www:http://www.informatik.uni-bremen.de/~malb > _jab: [EMAIL PROTECTED] --~--~---------~--~----~------------~-------~--~----~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to [EMAIL PROTECTED] For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~----------~----~----~----~------~----~------~--~---
