On Thu, May 15, 2008 at 10:42 AM, 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.
Median? Shouldn't you take the minimum? Are there any good papers
on benchmarking?
By the way -- present for you:
http://m4ri.sagemath.org/
To update that page you have to ssh to sagemath.org.
William
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