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How much will this help? Neural networks work so well on distributed computing systems because the operations run in parallel. With a neural network, you can le

by jostmey 10y ago

How much will this help? Neural networks work so well on distributed computing systems because the operations run in parallel. With a neural network, you can leverage tens of thousands of separate computing cores. Top intel processors have around a dozen cores or so. What am I missing here?

wmu 10y ago
Xeon Phi have got more cores, 50 or 60, and the cores share memory. Unlike GPUs memory space is uniform, there is no need to do explicit transfers to/from GPU's memory.
- arcanus 10y ago
  > Xeon Phi have got more cores, 50 or 60, and the cores share memory 60 cores. Furthermore, the cores have multiple (4) hardware threads per core. So on the latest Phi (Knights Landing) you can have several hundred threads (240) running concurrently without contention (in principle).
steego 10y ago
> What am I missing here? There may be a market sweet spot between dedicated high-performance machine learning that's ideal for experts who are willing to buy/lease dedicated hardware and regular developers who want to use simpler machine learning toolkits to solve easier problems. Toolsets like Julia, NumPy, R, etc would take advantage of the new instruction and basically give your average a speed boost for simply having an Intel PC.
- eb0la 10y ago
  This reminds me when you had to buy crypto accelerator cards for your webservers (does anyone remember Soekris?)... The market stalled after Intel started adding support for crypto operations on-die. This looks like Intel is protecting his server chip business shooting at nVidia before they start selling Titan-X with other server chip (what chip - I don't know; but I bet there's a business plan on a spreadsheet somewhere). I guess training neural nets will a GPUs / custom chips task for some time; but as soon as you have developed the classifier / predictor you have to run it on whatever hardware you have and that means you might need less parallel computing power and it doesn't make (financial) sense buying GPUs for that.
gok 10y ago
Let's say you want to run a service that does scene classification in images using deep convolutional networks. It takes 100ms on an Intel chip today, and 3ms on a GPU. But you can't run your entire service on a GPU...you're going to need that CPU anyway. So if Intel can close the gap even a little bit (say, down to 25ms), it might be worth it to avoid the headache of running a GPU server and just buy more Intel chips, which can run anything.

by jostmey 10y ago

wmu 10y ago

Xeon Phi have got more cores, 50 or 60, and the cores share memory. Unlike GPUs memory space is uniform, there is no need to do explicit transfers to/from GPU's memory.

arcanus 10y ago
> Xeon Phi have got more cores, 50 or 60, and the cores share memory 60 cores. Furthermore, the cores have multiple (4) hardware threads per core. So on the latest Phi (Knights Landing) you can have several hundred threads (240) running concurrently without contention (in principle).

steego 10y ago

> What am I missing here? There may be a market sweet spot between dedicated high-performance machine learning that's ideal for experts who are willing to buy/lease dedicated hardware and regular developers who want to use simpler machine learning toolkits to solve easier problems. Toolsets like Julia, NumPy, R, etc would take advantage of the new instruction and basically give your average a speed boost for simply having an Intel PC.

eb0la 10y ago
This reminds me when you had to buy crypto accelerator cards for your webservers (does anyone remember Soekris?)... The market stalled after Intel started adding support for crypto operations on-die. This looks like Intel is protecting his server chip business shooting at nVidia before they start selling Titan-X with other server chip (what chip - I don't know; but I bet there's a business plan on a spreadsheet somewhere). I guess training neural nets will a GPUs / custom chips task for some time; but as soon as you have developed the classifier / predictor you have to run it on whatever hardware you have and that means you might need less parallel computing power and it doesn't make (financial) sense buying GPUs for that.

gok 10y ago

Let's say you want to run a service that does scene classification in images using deep convolutional networks. It takes 100ms on an Intel chip today, and 3ms on a GPU. But you can't run your entire service on a GPU...you're going to need that CPU anyway. So if Intel can close the gap even a little bit (say, down to 25ms), it might be worth it to avoid the headache of running a GPU server and just buy more Intel chips, which can run anything.