Hey,
@Toby : I'll also try to take care of runing the auto-tuner/benchmark
script on my HD5850 or Hawaii (It's the Firepro W9100, but you can
advertise a R9 290x for single-precision, as it is much cheaper and has the
same chip for single precision). Which one do you prefer?
@Karl : Yes, an easy fix :) Considering the portability problems we've had
with GEMM for ViennaCL 1.5.2, I want to be careful. There will be a better
default profiles for CPUs/Accelerators, a better default profiles for GPUs,
and a better default profile for each NVidia / AMD / Intel GPUs. (In the
meanwhile, there is one default crappy global profile. Although the 1
work-group thing is a bug anyway...)
Philippe
2014-07-06 13:37 GMT+02:00 Karl Rupp <r...@iue.tuwien.ac.at>:
> Hey,
>
>
> > I made a small mistake when creating these "conservative" profiles. GEMV
>
>> runs with only one work group. I'll fix this, don't worry :)
>>
>
> ah, that's an easy fix then. Thanks!
>
> Best regards,
> Karli
>
>
>
>
>> 2014-07-06 13:31 GMT+02:00 Karl Rupp <r...@iue.tuwien.ac.at
>> <mailto:r...@iue.tuwien.ac.at>>:
>>
>>
>> Hey,
>>
>> > I'm getting on the plane in a couple of hours, so this might be
>> the last
>> > you here from me till the middle of the night Europe time.
>>
>> Have a good flight and enjoy Texas! :-)
>>
>>
>>
>> >> > I suggest we start unifying in a couple of days indeed. I
>> still have a
>> >>> couple of things to merge, essentially having GEMM dynamically
>> generated
>> >>> for some cases and publishing the repo for auto-tuning using
>> pyviennacl.
>> >>> These have to be done soon so that Toby can present some good
>> benchmarks
>> >>> at the talk.
>> >
>> > I'm currently struggling to get decent performance out of
>> viennacl-dev
>> > master, even when not doing GEMM. Consider single-precision dense
>> GEMV
>> > using a square matrix and vector with 4096 rows/cols. On the GTX
>> 470 on
>> > krupp2, one execution takes ~0.100s; on the C2050, ~0.106s.
>> Execution
>> > overhead is about 0.0003s. But NumPy with MKL takes only ~0.004s;
>> I know
>> > that krupp2 has an 8(?)-core i7, so (something like) 512
>> rows/cols per
>> > core, but I still didn't expect the gap to be like that. It's
>> strange,
>> > because my GeForce 610M takes ~0.090s, and my Intel Ivy Bridge M
>> GT2 GPU
>> > takes ~0.001s (at last competitive with MKL, though I'm waiting
>> to test
>> > correctness as I write this). And NumPy with OpenBLAS on my i5
>> takes
>> > ~0.009s. Any hints?
>>
>> So 16M entries with 4 bytes each need to be transferred for the
>> matrix,
>> which amounts to 64 MB of data. At an execution time of 0.1 sec, this
>> is
>> equivalent to 640 MB/sec of memory bandwidth, which is about a factor
>> of
>> 100 off the peak. Way too low. I'll check this today, it can only be a
>> tiny detail in the generator integration.
>>
>>
>> >> These are all valid points. What about cropping this 'offset'
>> and use
>> >> something like the following:
>> >>
>> >> namespace viennacl { namespace linalg {
>> >>
>> >> void some_api_function() { ... }
>> >>
>> >> namespace detail
>> >> {
>> >> void some_implementation_detail() { ... }
>> >> }
>> >>
>> >> }}
>> >>
>> >> This would preserve the benefit of a visual separation of public
>> API and
>> >> private implementations, yet remove the 'global' indent offset
>> from the
>> >> source file.
>> >
>> > I like this; and indeed is what I tend to do when indentation
>> gets out
>> > of hand.
>>
>> Thanks for the feedback :-)
>>
>> Best regards,
>> Karli
>>
>>
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>>
>>
>
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