Yes you are correct, only cpu icd is supported for intel xeon.
The gpu icd needs a purchase from intel. But I think money should
be spent on purchasing an amd or nvidia card instead.

Пн, 28 сен 2015, jprogramming написал(а):
> My amd apu has integrated GPU.  The cpu part supports f64.  the gpu part does 
> not.
> 
> If your i7 has integrated graphics, then its gpu may not support opencl at 
> all (though intel gpu's above hd4000 should though)
> 
> https://en.wikipedia.org/wiki/List_of_Intel_graphics_processing_units
> 
> 
> to count opencl cores, take the triplet of config (for earlier hd4000 128 16 
> 2)
> 
> 64 4 1&(<./@:%~) 128 16 2(2 opencl cores)
> 
> opencl 2 features are not used by arrayfire (yet)
> 
> I'd expect that the afcpu driver would always outperform the opencl cpu 
> driver due to full featured assembly, but this is a guess.
> 
> 
> ----- Original Message -----
> From: bill lam <[email protected]>
> To: 'Pascal Jasmin' via Programming <[email protected]>
> Cc: 
> Sent: Monday, September 28, 2015 11:28 AM
> Subject: Re: [Jprogramming] Arrayfire bindings
> 
> 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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> > > 
> > > 
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> > >  
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