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Using Intel’s Xeon Phi for Brain Research Visualization

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Re: Using Intel’s Xeon Phi for Brain Research Visualization

#11

Post by Klinky »

dman wrote: post #7
reitzensteinm wrote: post #4

The lowest price Xeon Phi in this generation is $2,348 (1.3ghz, 64 cores) - I can't help but feel Intel would do well to introduce an enthusiast product in to the lineup. Even 1.0ghz, 48 cores for $1000. They're Tesla priced without an equivalent desktop gamer graphics card, and that means you can't just dip your toe into the water; you've got to buy the canoe up front. Programming on a normal x86 doesn't really coun…

I agree - but I think there appears to be a more significant shift underpinning this. I suspect that we are beginning to see an architectural divergence between server and client. This reverses the last 20 years where intel made inroads into the datacenter and there were few fundamental differences between xeons and their desktop brethren (the i5/i7 etc). Intel will have vastly different ISAs on server and client thi…

It's is rather silly for Phi to be positioned as "it's just like x86, oh wait except for needing to use special SIMD instructions to get max performance". Kind of like Atom being x86 for ultra mobile platforms, just not being able to match the power/performance of ARM. Once you start sacrificing things to maintain x86 compatibility, you really loose its benefits.

Re: Using Intel’s Xeon Phi for Brain Research Visualization

#12

Post by milcron »

ActsJuvenile wrote: post #5
doctorpangloss wrote: post #2

I don't know if using Xeon Phi for rendering makes that much sense. It's sort of the problem it's least competitive to solve on a raw performance, performance per watt or development cost basis. > However, ‘smaller’ is a relative term as current visualizations can occur on a machine that contains less than a terabyte of RAM. Traditional raster-based rendering would have greatly increased the memory consumption as the…

> That sounds like a poor approach to this problem. You could write a shader that renders thick lines for the dendrites, and the rest of the geometry can be conventional meshes. The same shader could have a pass specially designed for lines and depth of field rendering. That's the one unusual shader. It's hard, but not super hard to write. [0] You will be surprised how bad medical research and visualization is compar…

Here is a comparison of the previous generation: https://www.xcelerit.com/computing-benchmarks/libor/intel-xe...

They hold their own against GPGPU, but are probably the inferior choice if your code already runs on a GPU (OpenCL/CUDA).

The real advantage of the Phi is of course combining this nearly-as-good-as-GPGPU parallelism with the x86_64 toolchain and infrastructure. x86 supports more languages with more libraries, and is easier to develop for.

Re: Using Intel’s Xeon Phi for Brain Research Visualization

#13

Post by CyberDildonics »

doctorpangloss wrote: post #2

I don't know if using Xeon Phi for rendering makes that much sense. It's sort of the problem it's least competitive to solve on a raw performance, performance per watt or development cost basis. > However, ‘smaller’ is a relative term as current visualizations can occur on a machine that contains less than a terabyte of RAM. Traditional raster-based rendering would have greatly increased the memory consumption as the…

> It's sort of the problem it's least competitive to solve on a raw performance, performance per watt or development cost basis.

This is not true for anything beyond running compute shaders on large 1D, 2D, or 3D buffers. Just because something is 'graphics' doesn't mean that a GPU is automatically faster.

> Production-ready renderers like Arnold, Octane and mental ray (NVIDIA's renderer) perform best or are exclusive to the CUDA platform.

Arnold is a CPU renderer, Octane is FAR from what I would consider 'production ready' and mental ray is also a software renderer. Renderman does not use any GPU acceleration.

> I sound like I know what I'm talking about, right?

Not even slightly

Re: Using Intel’s Xeon Phi for Brain Research Visualization

#14

Post by doctorpangloss »

CyberDildonics wrote: post #13
doctorpangloss wrote: post #2

I don't know if using Xeon Phi for rendering makes that much sense. It's sort of the problem it's least competitive to solve on a raw performance, performance per watt or development cost basis. > However, ‘smaller’ is a relative term as current visualizations can occur on a machine that contains less than a terabyte of RAM. Traditional raster-based rendering would have greatly increased the memory consumption as the…

> It's sort of the problem it's least competitive to solve on a raw performance, performance per watt or development cost basis. This is not true for anything beyond running compute shaders on large 1D, 2D, or 3D buffers. Just because something is 'graphics' doesn't mean that a GPU is automatically faster. > Production-ready renderers like Arnold, Octane and mental ray (NVIDIA's renderer) perform best or are exclusiv…

My bad, I wrote Arnold instead of Arion, I mix them up when writing it out. iRay is sort of a feature of mental ray, I guess if you're being pedantic. Octane isn't production ready, but I suppose if you're used to building render farms it's not production ready. It's certainly production ready for someone paying for all those licenses.

> This is not true for anything beyond running compute shaders on large 1D, 2D, or 3D buffers.

Yes, but rendering is a shader on a bunch of those buffers right? That's what I wrote. I'm not 100% confident that you can efficiently render with conventional shaders what they showed in that frame. But I think you can. You could at least cull and tesselate tubes on the GPU, if you really don't want to write a shader.

Re: Using Intel’s Xeon Phi for Brain Research Visualization

#15

Post by dibanez »

reitzensteinm wrote: post #10
dibanez wrote: post #9

Earlier quoted context omitted.

As someone who has programmed both Phis and conventional x86 CPUs, I can confirm that the Phi is more sensitive to data traversal order and NUMA effects on which core accesses which memory. Also, the latest generation (Knights Landing) has much better performing cores than the previous generation.

Well, if it weren't the case and you had that core count without compromise, Phis would have come along a lot sooner with a price tag to match :) Did you happen to use the Knight's Corner or the new Knight's Landing variant? I'd be quite interested to know how KL stacks up, as naively from the specs it seems like it should be a lot more tolerant with code (but not poor memory access patterns).

Both, and it agrees with your prediction. KNL's cores are each much faster than KNC's cores. A KNC core was over 10X slower than a mainstream CPU core, and a KNL core seems to only be about 4.5X slower (on my particular code). I also get linear OpenMP scaling from 1 to 64 threads on KNL, so the parallelism is all there.

Re: Using Intel’s Xeon Phi for Brain Research Visualization

#16

Post by dman »

dibanez wrote: post #15
reitzensteinm wrote: post #10

Earlier quoted context omitted.

Well, if it weren't the case and you had that core count without compromise, Phis would have come along a lot sooner with a price tag to match :) Did you happen to use the Knight's Corner or the new Knight's Landing variant? I'd be quite interested to know how KL stacks up, as naively from the specs it seems like it should be a lot more tolerant with code (but not poor memory access patterns).

Both, and it agrees with your prediction. KNL's cores are each much faster than KNC's cores. A KNC core was over 10X slower than a mainstream CPU core, and a KNL core seems to only be about 4.5X slower (on my particular code). I also get linear OpenMP scaling from 1 to 64 threads on KNL, so the parallelism is all there.

Are you using the socketed version of the pcie version?

Re: Using Intel’s Xeon Phi for Brain Research Visualization

#18

Post by segmondy »

reitzensteinm wrote: post #4

The lowest price Xeon Phi in this generation is $2,348 (1.3ghz, 64 cores) - I can't help but feel Intel would do well to introduce an enthusiast product in to the lineup. Even 1.0ghz, 48 cores for $1000. They're Tesla priced without an equivalent desktop gamer graphics card, and that means you can't just dip your toe into the water; you've got to buy the canoe up front. Programming on a normal x86 doesn't really coun…

Last year they sold a bunch of them 60 cores for $200. I got one, the problem is that they run hot and need a server that can support them. I'm yet to acquire a server with bar support, so it's still sitting. :-( Anyways, they are out there for decent price, keep your eyes open and you will find a deal.

Re: Using Intel’s Xeon Phi for Brain Research Visualization

#19

Post by rjtobin »

dibanez wrote: post #15
reitzensteinm wrote: post #10

Earlier quoted context omitted.

Well, if it weren't the case and you had that core count without compromise, Phis would have come along a lot sooner with a price tag to match :) Did you happen to use the Knight's Corner or the new Knight's Landing variant? I'd be quite interested to know how KL stacks up, as naively from the specs it seems like it should be a lot more tolerant with code (but not poor memory access patterns).

Both, and it agrees with your prediction. KNL's cores are each much faster than KNC's cores. A KNC core was over 10X slower than a mainstream CPU core, and a KNL core seems to only be about 4.5X slower (on my particular code). I also get linear OpenMP scaling from 1 to 64 threads on KNL, so the parallelism is all there.

Some questions out of curiosity: Is your application bandwidth-bound / compute-bound or something else? Also what modes have you been operating the KNL chip in?

Re: Using Intel’s Xeon Phi for Brain Research Visualization

#20

Post by lqdc13 »

reitzensteinm wrote: post #4

The lowest price Xeon Phi in this generation is $2,348 (1.3ghz, 64 cores) - I can't help but feel Intel would do well to introduce an enthusiast product in to the lineup. Even 1.0ghz, 48 cores for $1000. They're Tesla priced without an equivalent desktop gamer graphics card, and that means you can't just dip your toe into the water; you've got to buy the canoe up front. Programming on a normal x86 doesn't really coun…

Why not just use Xeon 2697-v2 for the same price as the phi?

It's 12 core so performance in all-core situation would be about the same as this one. But on non-parallelized code it would be ~5x faster..

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