Does your GPU support fp64? OpenCL should already have support for
fp64, on my intel i7 which has only one device. it shows extensions
with cl_khr_fp64.
Пн, 28 сен 2015, jprogramming написал(а):
> I am cheating on the benchmark,actually. I'm doing double precision in J,
> and float in arrayfire for the 250x speed claim on GPU. It does verify that
> they are tolerantly equal to J's results, and gets a 1 on my system.
>
> The functions are matmulsFonly (for floats), and multimatmulsF (for array
> size(s) parameter)
>
> With doubles, there is also a test included for that you can use to test CPU
> (still speed boost) and opencl on CPU. Its called matmuls and multimatmuls.
> Running matmuls on device that doesn't support double, crashes gracefully.
>
> As a testing scaffold, assuming 2 opencl devices with 1 (on cpu/apu)
> supporting doubles
>
> C =. P =. ('afcpu';0) conew 'afdevice'
> O =. ('afopencl';1) conew 'afdevice'
> C =. G =. ('afopencl';0) conew 'afdevice'
> 256 128 100 multimatmuls P, setme__O O (relevant code is matmuls)
>
>
> for my system, 100 array size is faster in J, while 128 is slower for both
> CPU (openblas library) and opencl(cpu). I think power of 2 sizes provide
> special optimizations. What is superfast in all libraries is returning
> (async/lazy) from calls. Accessing a result transparently syncs (invokes
> wait until calculation complete) it. 128 size has about even blas and
> opencl(apu ) performance.
>
> for 256 size blas-cpu is 2x faster than opencl-apu. Another advantage blas
> has is that running a specific array size the first time is about the same
> speed as the 2nd time. opencl has a JIT delay/or just high variation for
> each new size.
>
> at 512 on my system, openblas is 4x+ faster than J, but opencl apu is 2x
> slower. I can't explain why the trend from 256 breaks down.
>
> > OpenCL is already cross platforms, what is the advantage of
> using ArrayFire instead of OpenCL?
>
> On specific hardware, cuda might be faster. The double precision issue above
> means you may not be able to use an available GPU to get the exact result you
> want. You can use the cpu and gpu at the same time, with the same code, and
> it provides async/lazy execution.
> It is/was relatively easy to bind to a single array creation/dereferencing
> api, to use as backend all of the possible supported functions (on cpu come
> from different integrated libraries).
> Its also straightforward to create an afdevice compatible class (see coclass
> 'afJ' in arrayfire.ijs) that has mostly noops for array
> creating/dereferencing/memory management, and is useful for 32 bit systems,
> those who don't want to install arrayfire, and the many situations where J is
> actually faster than using arrayfire, that you either find out through
> benchmarking the same code, or dynamically switching implementation based on
> input size.
>
>
>
>
>
>
>
> ----- Original Message -----
> From: bill lam <[email protected]>
> To: 'Pascal Jasmin' via Programming <[email protected]>
> Cc:
> Sent: Monday, September 28, 2015 1:22 AM
> Subject: Re: [Jprogramming] Arrayfire bindings
>
> The speed improvement is impressive, btw did you also verfiy the
> calculation results argeed with each other?
>
> OpenCL is already cross platforms, what is the advantage of
> using ArrayFire instead of OpenCL?
>
> Пн, 28 сен 2015, jprogramming написал(а):
> > it provides a unified api to opencl, cuda (gpu centered computing) and
> > various paralell (multithreaded) opensource cpu libraries for the same
> > functions.
> >
> > the function categories it covers:
> > http://www.arrayfire.com/docs/group__func__categories.htm
> >
> > something not in those categories is a nearest neighbour algorithm.
> >
> >
> > ----- Original Message -----
> > From: 'Bo Jacoby' via Programming <[email protected]>
> > To: "[email protected]" <[email protected]>
> > Cc:
> > Sent: Monday, September 28, 2015 12:52 AM
> > Subject: Re: [Jprogramming] Arrayfire bindings
> >
> > What is Arrayfire is all about?-- Bo
> >
> >
> >
> > Den 6:29 mandag den 28. september 2015 skrev 'Pascal Jasmin' via
> > Programming <[email protected]>:
> >
> >
> >
> > updated repo (bug fixes), and included a wiki page describing
> > advantages/disadvantages of arrayfire. https://github.com/Pascal-J/Jfire
> >
> > On my low end GPU, I get a 250x speed improvement over J for floating point
> > matrix multiplication on array size of 1024x1024 (including setup and
> > results going back to J).
> >
> >
> >
> >
> > ----- Original Message -----
> > From: Alex Shroyer <[email protected]>
> > To: [email protected]
> > Cc:
> > Sent: Saturday, September 26, 2015 10:07 PM
> > Subject: Re: [Jprogramming] Arrayfire bindings
> >
> > Very cool. I did an audio processing thing in J a while back which I think
> > would have benefited from something like this. Perhaps now I'll
> > re-implement it using Jfire.
> > Thanks for sharing!
> >
> > Alex
> >
> > -----Original Message-----
> > From: "'Pascal Jasmin' via Programming" <[email protected]>
> > Sent: 9/26/2015 7:03 PM
> > To: "Programming Forum" <[email protected]>
> > Subject: [Jprogramming] Arrayfire bindings
> >
> > https://github.com/Pascal-J/Jfire
> >
> > There are probably some helpful applications for it. Mostly with floating
> > point. (A lot of work for something I don't use much :P)
> >
> > Hope you like the interface for it. Its fairly J like.
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