[sampo]

in High Performance Computing, there is (1) 3-dimensional simulations (weather, fluid dynamics, structural mechanics, all kinds of physics simulations, like magnetic storms in space or nuclear reactors etc.) and then there is (2) everything else, like data mining, machine learning, genomics etc.

Some of the sparse matrix computations in structural mechanics and in some machine learning algorithms have some overlap. But mostly, group 2 has little reason to be interested in what group 1 is doing.

Now, group 2 obviously has more modern tools than the 3d-simulation community, because machine learning came to common use much later that numerical fluid mechanics.

But do 3d-simulation people also have much reason to be interested in what the machine learning people are doing?

The "machine learning / big data" people are probably not doing anything that makes a weather prediction model to run faster? Or are they?

[sgt101]

They are doing it (interesting things) for lower capex and lower development costs. On opex, good for operations, bad for power consumption (relatively).

In terms of absolute performance HPC is absolutely faster. In terms of bangs for bucks, Big Data is hands down faster. Also in terms of accessibility Big Data is hugely easier - I can build you a 100 core big data system for $300k

[sampo]

But your Big Data system is only good for Big Data. If I want to run a weather prediction model, it is not going to help.

My point is, the big data and the physics simulation people probably do not have a lot of common interests - besides using large amounts of computing power.

[acadien]

There are big data people running on supercomputers too. I know there are people writing custom asynch job managers to handle big data type problems because the top supercomputers have low memory latency.

Also I think the dichotomy you're looking for is IO bound vs CPU bound problems. Although certainly there are a plethora of different kinds of IO bound problems (asynch vs synch or disk bound vs memory bound vs cache bound).

[markhahn]

that's silly: HPC has been pinching pennies before big data was a thing. and the computer industry is biz: you get what you pay for. if you can live with Gb performance, you can drop around $2k (IB card, cables, switches) off your price. But it's not as if the hardware is any different, faster or more accessible.

[sgt101]

I think it's economics, GPU's are sold by the million, super computers interconnects are sold by the thousands. Commodity kit is mass produced spreading design, vvt and manufacturing tooling costs.

The hardware is different in terms of the layout. Aggregations of small cores on boards (gpus) vs. very high speed large cores with lots of local memory. Highly localised connections vs. an interconnect fabric.

And it is more accessible because it's affordable, and you can get at it in the cloud; this means that skills building is easier for more people and it also means that a wider user base is possible.

[jabl]

Huh, where did GPU's enter the discussion?

Anyway, for a typical HPC cluster, it's bog standard x86 hardware, the only remotely exotic thing is the Infiniband network. Common wisdom says that since Infiniband is a niche technology, it's hugely expensive, but strangely(?) it seems to have (MUCH!) better bang per buck than ethernet. A 36-port FDR IB (56 Gb/s) switch has a list price of around $10k, whereas a quick search seems to suggest a 48-port 10GbE switch has a list price of around $15k. So the per-port price is roughly in the same ballpark, but IB gives you >5 times better bandwidth and 2 orders of magnitude lower MPI latency. Another advantage is that IB supports multipathing, so you can build high bisection bandwidth networks (all the way to fully non-blocking) without needing $$$ uber-switches on the spine.

[sgt101]

That's interesting, things may have changes with IB since I last looked.

The GPU thing seems to have fallen out of my original comment, I meant to write "I can build you a 100,000 core system for $300k" but some how the decimal point jumped left three times! To do that I would definitely have to use GPU's...

I am seriously lusting after such a device, I feel that there is much to be done.