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Intel Announces Knights Mill: A Xeon Phi for Deep Learning

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Re: Intel Announces Knights Mill: A Xeon Phi for Deep Learning

#51

Post by stonogo »

rbanffy wrote: post #41
stonogo wrote: post #40

Earlier quoted context omitted.

I wish this were true, but it's not. Multi-million-dollar contracts have been scuttled because a given customer runs codes that they receive as binaries from a vendor, closed-source, and Company A bid Processor X, but the codes only run on Processor Y...

That's interesting. I'd assume a market so performance driven would compete such vendors out of existence.

The market is performance-driven because it is task-driven. Performance as a requirement, in other words, is a byproduct of the mission. If your task requires software that someone else holds the keys to, you have no choice but to mold the rest of your environment to fit that software.

Thankfully this sort of situation is becoming less common over time, but we're not all the way done yet.

Re: Intel Announces Knights Mill: A Xeon Phi for Deep Learning

#52

Post by rbanffy »

stonogo wrote: post #51
rbanffy wrote: post #41

Earlier quoted context omitted.

That's interesting. I'd assume a market so performance driven would compete such vendors out of existence.

The market is performance-driven because it is task-driven. Performance as a requirement, in other words, is a byproduct of the mission. If your task requires software that someone else holds the keys to, you have no choice but to mold the rest of your environment to fit that software. Thankfully this sort of situation is becoming less common over time, but we're not all the way done yet.

It's a big relief to know my assumptions will, eventually, be true... :-)

Re: Intel Announces Knights Mill: A Xeon Phi for Deep Learning

#53

Post by cs702 »

jlebar wrote: post #38
cs702 wrote: post #2

We badly need an alternative to Nvidia/CUDA for deep learning... but realistically, if Intel wants to make headway in the deep learning market, it must offer hardware that can not only compete on performance with Nvidia, but also work out-of-the-box (that is, without requiring lots of one-off tinkering and tweaking) with popular deep/machine learning frameworks like TensorFlow, Caffe, Torch, and Theano. There is a lo…

Tensorflow has a mode that lets it run on CPUs. I'm sure other frameworks are the same. Isn't the whole point of Xeon Phi that it looks basically like an x86 CPU with a ton of cores? If so there is almost nothing to port, just the kernel launching. Granted, you do have to bother to make a fast x86 / AVX-512 port of your code. But because the shape of GPUs is so different than CPUs -- GPUs have a more complicated memo…

"Granted, you do have to bother to make a fast x86 / AVX-512 port of your code."

That is exactly the problem: no one has bothered to make a fast x86 / AVX-512 port for any of the most popular frameworks (at the upstream level, not in some fork), and no one has an incentive to bother, other than Intel. For example, as far as I know, none of the popular frameworks take advantage of Intel's MKL out of the box.

Right now, if you want out-of-the-box high performance, Nvidia hardware is your only practical choice.

Re: Intel Announces Knights Mill: A Xeon Phi for Deep Learning

#54

Post by marmaduke »

dagss wrote: post #46
marmaduke wrote: post #31

Earlier quoted context omitted.

Nope. I have some substantial simulation code written against OpenCL that runs on Intel OpenCL and NVIDIA without modifications, and rather performant on both of them. The only part of the code specific to vendor is the platform selection, which is one line of code. OpenCL falls down in terms of standard libraries such as cu{dnn,sparse,blas} but if you're writing everything from scratch it's fine.

Interesting...so do you use the vector subset designed for CPUs or the wavefront subset designed for GPUs?

I write code which maximizes coalesced memory access and plain old 32 bit floats.. nothing special. The respective drivers do a good job of mapping that onto the hardware given sufficient work group sizes.

Re: Intel Announces Knights Mill: A Xeon Phi for Deep Learning

#55

Post by marmaduke »

joe_the_user wrote: post #48
marmaduke wrote: post #31

Earlier quoted context omitted.

Nope. I have some substantial simulation code written against OpenCL that runs on Intel OpenCL and NVIDIA without modifications, and rather performant on both of them. The only part of the code specific to vendor is the platform selection, which is one line of code. OpenCL falls down in terms of standard libraries such as cu{dnn,sparse,blas} but if you're writing everything from scratch it's fine.

I'm an independent developer in the process of choosing a GPGPU library. I can see simple, comprehensible 20-50 sample code for cuda that does most simple tasks. With OpenCL, I get references to version, boiler-plate, mode with nothing that boils down to simple code. If you have a simple sample, you should post it here or blog about it.

Look at Pyopencl; it makes it quite easy to prototype OpenCL apps in tens of lines of code.

Later, it's straightforward to port to c or c++ if that's your thing, though I find having numpy et al handy even in production code

Re: Intel Announces Knights Mill: A Xeon Phi for Deep Learning

#56

Post by marmaduke »

duaneb wrote: post #50
joe_the_user wrote: post #48

Earlier quoted context omitted.

I'm an independent developer in the process of choosing a GPGPU library. I can see simple, comprehensible 20-50 sample code for cuda that does most simple tasks. With OpenCL, I get references to version, boiler-plate, mode with nothing that boils down to simple code. If you have a simple sample, you should post it here or blog about it.

Boilerplate is one thing—this is what transpilers are for, among other solutions—but can OpenCL match CUDA on a performance level?

Cl is comparable to Cuda where the cuda code doesn't employ nvidia specific primitives.

Re: Intel Announces Knights Mill: A Xeon Phi for Deep Learning

#57

Post by flamedoge »

willvarfar wrote: post #29
flamedoge wrote: post #20

Earlier quoted context omitted.

Because you don't have control over what Google does. They may kill TPU altogether leaving your work irrelevant.

You don't have control over what nvidia, intel or the rest of them either. If you want to get your 'feet wet', then why bother?

Because they have client facing products and backward compatibility is important to clients.
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