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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

#21

Post by berkut »

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…

Drawing lines is a stupid way of doing it, as:

1. With the mess of overlapping lines they've got, you'd suffer from severe overdraw (which is where raytracing really shines in terms of efficiency) as you can't efficiently cull lines (without clipping them)

2. You wouldn't get the ambient occlusion look where lines close to each other occlude / darken.

As someone who's previously compared Embree and OptiX (and we were given free hardware and support from Nvidia), Embree stacks up really well, and a dual Xeon can match a single top-of-the-line GPU fairly easily for pure ray-intersection performance.

Once you start putting complex shaders and layered materials on top, GPUs start to really suffer: there's a reason a lot of the GPU renders are mostly being used for clean renders like archvis / product design / car renders - they're simple to render. As soon as you stick dirt layers on top, their efficiency really starts to plummet.

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

#22

Post by berkut »

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…

That's not quite fair - some of the research into volumetric medium interaction and scattering is way ahead of the VFX / Gaming fields...

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

#24

Post by doctorpangloss »

berkut wrote: post #21
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…

Drawing lines is a stupid way of doing it, as: 1. With the mess of overlapping lines they've got, you'd suffer from severe overdraw (which is where raytracing really shines in terms of efficiency) as you can't efficiently cull lines (without clipping them) 2. You wouldn't get the ambient occlusion look where lines close to each other occlude / darken. As someone who's previously compared Embree and OptiX (and we were…

> stupid way of doing it

I guess it really depends on what the objective is. I'm not talking speculatively, but concretely it seems like a reasonable way to achieve a few images that they show in the press release. They show two relatively flatly rendered lots-of-tubes images. I know SSAO isn't the same, and I get that there's overdraw, but there are a lot of details in the particular objective they want. In one shot, they show a lot of emissive tubes with depth of field, which is harder to achieve. I suppose if they're happy, they're happy.

> interactive performance for all datasets on a regular Intel Xeon processor, which can render images at 20-25 frames per second (FPS)

There's a big difference between interactive performance and a production-quality render. Something tells me it's not producing 25 frames of noise-free render per second. There isn't enough information here.

> and a dual Xeon can match a single top-of-the-line GPU

At what, like 3x-5x the price? At how many watts? And at what I.T. complexity? A GTX 1080, at better performance than a Titan X, is really a phenomenally good deal. Especially considering I can drop it into an existing workstation with all of my existing software installed on it; especially considering I can rent out computation time on Amazon by the hour.

I guess what I'm reacting to is how forced of an example it seems.

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

#25

Post by profquail »

lqdc13 wrote: post #20
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..

Memory bandwidth is important too. The Knights Landing processors have a 16GB on-chip memory to the cores have significantly higher bandwidth than you'd get with DDR4; the additional memory bandwidth makes more of an impact on the runtime of some algorithms than raw compute performance does.

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

#26

Post by berkut »

doctorpangloss wrote: post #24
berkut wrote: post #21

Earlier quoted context omitted.

Drawing lines is a stupid way of doing it, as: 1. With the mess of overlapping lines they've got, you'd suffer from severe overdraw (which is where raytracing really shines in terms of efficiency) as you can't efficiently cull lines (without clipping them) 2. You wouldn't get the ambient occlusion look where lines close to each other occlude / darken. As someone who's previously compared Embree and OptiX (and we were…

> stupid way of doing it I guess it really depends on what the objective is. I'm not talking speculatively, but concretely it seems like a reasonable way to achieve a few images that they show in the press release. They show two relatively flatly rendered lots-of-tubes images. I know SSAO isn't the same, and I get that there's overdraw, but there are a lot of details in the particular objective they want. In one shot…

I think the objective is rendering a ridiculous amount of stuff - the fact they talk about "lots and lots of RAM" indicates there's no way a GPU is going to be able to render it efficiently without an aggressive culling step: GDDR5 might be very fast, but you've got to get the data onto the GPU first and probably page data as well. This is very often a significant bottleneck for GPUs, and is another reason GPUs aren't used for VFX rendering (at high-end), as 16 GB isn't anywhere near enough.

Production-quality render implies decent lighting and materials - this stuff has neither, so shading is likely to be negligible, and then you're going to be generally constrained by ray / primitive intersection performance.

No, cheaper (for CPU) : two ~$950 CPU cores vs $3,300 GPU. Granted you need a dual-socket system and twice the RAM to balance and it's easier to stick multiple GPUs in a system than make the jump to 4 sockets, but GPUs aren't really that much of a win... Thermal output and power usage is often worse for GPUs as well.

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

#27

Post by trhway »

yolesaber wrote: post #3

"Figure 1: Even first in-silico models show the complexity and beauty of the brain" Man the human brain is such a narcissist

especially given that it is a poorly designed, if any, patchwork of new features developed using "spaghetti on the wall" approach and piled on top of the old ones.

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

#28

Post by colechristensen »

yolesaber wrote: post #3

"Figure 1: Even first in-silico models show the complexity and beauty of the brain" Man the human brain is such a narcissist

I'm imagining a new test for artificial intelligence measuring a system's capability for narcissism – the true metric of real consciousness.

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

#29

Post by WhitneyLand »

This article is too fluffy, sounds like it had help from Intel's PR depr.

I certainly hope Phi has more advantages than the write once run anywhere / portability angle they kept pushing.

Has anyone chosen Phi for a real project that was in no way funded or subsidized by Intel?

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

#30

Post by fulafel »

profquail wrote: post #25
lqdc13 wrote: post #20

Earlier quoted context omitted.

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..

Memory bandwidth is important too. The Knights Landing processors have a 16GB on-chip memory to the cores have significantly higher bandwidth than you'd get with DDR4; the additional memory bandwidth makes more of an impact on the runtime of some algorithms than raw compute performance does.

The optional 16GB L3 is on separate chips, but it's colocated inside the same chip package. This kind of MCMs (multi-chip modules) have been used for a long time in the semiconductor industry since the 70s. Recent examples include AMD Xenos in XBox 360, Wii U CPU, IBM POWER chips.
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