Intel Announces Knights Mill: A Xeon Phi for Deep Learning

http://www.anandtech.com/show/10575/intel-announces-knights-mill-a-xeon-phi-for-deep-learning

by scaz • 10 years ago

94 56 10 years ago

cs7023• 10 years ago

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 lot of software infrastructure being built atop these frameworks, and switching costs are getting higher by the day. No one wants to use some kind of 'non-standard' fork of [name your DL framework of choice] customized for Intel hardware, because such a fork can quickly get stale in comparison to the upstream project.

Intel needs to be both better/faster and drop-in compatible with the popular frameworks.

argonaut3• 10 years ago

But being drop-in compatible means supporting CUDA or the CPU interface.

cs7023• 10 years ago

They could also contribute code to the most popular frameworks, instead of releasing forks like "Intel Caffe."[1]

[1] https://github.com/intelcaffe/caffe

joe_the_user3• 10 years ago

Is there a problem with a system that's CUDA compatible?

CUDA seems to me like the only simple SIMD-type computing system that's fairly straightforward to program and understand at this point.

Drop-in CUDA compatibility seems like a good thing.

kartD3• 10 years ago

I agree, AMD is working on making it easy to port CUDA[1], which still doesn't provide a strict alternative, but it's something

http://wccftech.com/amd-cuda-compilercompatibility-layer-ann...

DannyBee• 10 years ago

Google also released GPUCC (http://research.google.com/pubs/pub45226.html), and got CUDA support into upstream clang.

LLVM also has an AMD GPU backend, and says this thing is built on clang/llvm.

So i suspect it's based on that support :)

duaneb2• 10 years ago

This is also on upstream projects not to lock themselves into CUDA. Yes, it's great, but everyone suffers when there's only one supported API. Even more so when it's closed and locked to a specific vendor, as CUDA is.

joe_the_user3• 10 years ago

I'd love an alternative to CUDA.

The problem is that as far as I can see, OpenCL is in no way that. Basically, OpenCL gives me the impression that the oceans of boiler plate required both make development hard and effectively locks you into a specific vendor also since the boiler-plate is going to be setting things up for one's specific vendor.

cma• 10 years ago

Can companies that don't have agreements/court rulings with Intel implement Xeon Phi ISAs?

agibsonccc• 10 years ago

How many people are actually deploying to production though? It seems like its mostly research papers and enthusiasts out there in the wild yet.

Are we talking startups? A lot of startups know python so that would make sense...I'd love to see some actual stories though.

jcoffland• 10 years ago

Since Intel's product is just a bunch of CPUs it should work with OpenCL out of the box.

AIMunchkin• 10 years ago

It does... Badly... And you used to have to pay for it...

jlebar• 10 years ago

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 memory hierarchy for one thing -- I kind of doubt that "just run your CUDA code in the Phi" is going to work well for nontrivial examples.

(Disclaimer, I work at Google on CUDA support in clang. Which is awesome, you should try it out. :) Google "cuda clang" for instructions.)

jlouis• 10 years ago

To elaborate: Tensorflow is clever because it takes your flow graph and cuts it into pieces which it then executes on computational units. Any computational unit will do, really as long as you have a backend for it. This choice makes adaptation to new system much easier.

I believe Caffe and Theano uses the same model, but I didn't study it. There are also some similarity in the model to what OCaml does with the incremental library, though it is not for machine learning.

cs7023• 10 years ago

"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.

scottlegrand• 10 years ago

Except that up to now at least, CUDA IMO remains the best abstraction for programming multi-core: subsuming away multiple threads, SIMD width, and multiple cores into the language definition. AMBER (http://www.ambermd.org) literally "recompiled and ran" with each succeeding GPU generation since GTX 280 in 2009. 3-5 days of subsequent refactoring then unlocked 80% of the attainable performance gains of each of the subsequent GPUs. DSSTNE (https://github.com/amznlabs/amazon-dsstne) just ran as well, but it's only targeting Kepler and up because the code relies heavily on the __shfl instruction.

So I honestly don't get the Google clang CUDA compiler right now. It's really really cool work, but I don't get why they didn't just lobby NVDA heavily to improve nvcc. With the number of GPUs they buy, I suspect they could have anything they want from the CUDA software teams.

However, if it could compile CUDA for other architectures, sign me up, you'd be my heroes.

For I'd love to see CUDA on Xeon Phi and on AMD GPUs (I know, they're trying). And if Intel poured the same amount of passion and budgeting into building that as they are pouring into fake^H^H^H^Hdeceptive benchmark data and magical powerpoint processors we won't see for at least a year or two (and which IMO will probably disappoint just like the first two), they'd be quite the competitor to NVIDIA, no?

That said, the Intel marketing machine seems to have succeeded in punching NVDA stock in the nose the past few days and in grabbing coverage in Forbes (http://www.forbes.com/sites/aarontilley/2016/08/17/intel-tak...) so maybe they know a thing or two I don't.

filereaper• 10 years ago

This earlier thread on HN might be of interest:

Why didn't Larrabee fail? https://news.ycombinator.com/item?id=12293308

kartD3• 10 years ago

Not a very good one, I feel it covers up too much of Larabee's past. I think it was well established that it would be a GPU, but suffered from Intel getting confused about what it should be.

If anyone would like to know more about Intel, I think this AMA is much better

https://www.reddit.com/r/IAmA/comments/15iaet/iama_cpu_archi...

kartD3• 10 years ago

Nice, but how is this going to fit with Nervana?

Also more than hardware how do Intel's libraries compare with CuDNN?

At the end of the day ease of use and software support matter along with the hardware.

zump2• 10 years ago

Dude, they bought Nervana like yesterday.

Nervana has its own silicon, but I doubt they will tape out.

ThinkBeat• 10 years ago

I wish they had a range that was affordable for a hobbyist. You can buy a cheap Nvidia card to "get your feet wet".

I would like to play with these things.

dogma1138• 10 years ago

Intel was selling the Xeon Phi 31S1P for under 200$ (it's back to 500$ now) for a limited time. They will likely to have cheap version and promotions this time around too.

imaginenore• 10 years ago

Why not use Google's platform, which runs on their new custom chips (Tensor Processing Unit)?

vonnik• 10 years ago

Why use it, if it locks you in?

visarga3• 10 years ago

Because it's not made to accelerate training, just inference. The TPU is an 8-bit fixed point processor less power hungry than GPUs, so it won't help research, only deployment for large projects, running in the cloud.

rch• 10 years ago

Keep an eye on Colfax - they've had some nice deals in the past:

http://www.colfax-intl.com/nd/

marmaduke5• 10 years ago

A recent multicore i7 (e.g. 4 core Haswell, 8 way SIMD for single precision = 32 threads) is enough to prototype OpenCL code which you can then run on larger CPUs or GPUs.

zump2• 10 years ago

How much?

cordite• 10 years ago

Will there ever be an ARM coprocessor with several hundred nodes available?

frozenport• 10 years ago

Intel has thousands of employees and a compiler. Get 200 of them in a room and implement CUDA.

visarga3• 10 years ago

They don't even have to implement the latest flavor of "Inception module", they only need to implement matrix vector operations and some math primitives like exponential, log, tangent and such. Why is it so hard to port to Intel? I would have liked to make use of my Macbook's Intel Iris GPU for deep learning, but it's not supported by anything.

nl• 10 years ago

OpenCL supports Iris.

Intel Announces Knights Mill: A Xeon Phi for Deep Learning

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