A distributed supercomputer using Parallella boards | Hacker News Reader

A distributed supercomputer using Parallella boards

supercomputer.io

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I've enjoyed reading on Adapteva's work not only for their processor specs but the sheer impressiveness of them iterating so many designs with so little money vs most ASIC vendors. This is definitely not a supercomputer (buzzword alert!) but is a good distributed computing idea. I could see their stuff getting crammed into compute nodes in clusters, too.

Basically, a narrow set of applications that leverage these sorts of chips best and are easily distributed can benefit from this setup. It will probably have more raw performance per watt than most @home setups. Yet, I'd guess lower potential in number of nodes given fewer Parallella users on the market. I hope they benchmark it with many, realistic software that can be compared to other parallel computing methods. This would give us useful information on whether to use something similar for a local site or another @home, non-profit effort.

Thanks!

Clearly we don't have the volume to make this as big as @home, but let's not forget how expensive it is to run a big computer. The folding@home project claims 36,000 TFLOPS of performance. If we generously estimate 1 GFLOPS/W for most home computers, then we are talking about a cost of ~$36M/year for the people donating cycles. This would be a smaller system, and if we get all 10,000 Parallella boards in the field hooked up, the electrical cost would only be ~$50K/year.

I am not going to get into the whole "what is a supercomputer thing":-) I am never going to beat Nvidia at that game. http://nvidianews.nvidia.com/news/nvidia-launches-tegra-x1-m... What I do know is that 180,000 CPU cores working on one problem is one insane distributed computer.

The electrical cost savings is a sound argument. On the side, that might also be a good reason for your marketing team to target customers in areas with high energy prices. Just a thought.

I'm still waiting for Adapteva to combine their work with SGI- or NUMAscale-style tech to have a UV-style system full of CPU's, your accelerators for general use, and optionally FPGA's for special purpose stuff. The CC memory + bandwith + ton of Adapteva IC's per node would = incredible performance per dollar and watt. Maybe. ;) Any plans for integrating with ccNUMA architectures?

I was pretty enthusiastic about Adapteva's kickstarter for their Parallella board, even though I felt like some of the wording they had was a bit deceptive. I began to sign on for the 64 core version but I backed out because I got burned by another Kickstarter project around the same time it began to become clear they weren't really going to make it to the funding level they needed for the 64 core version.

I forget how long that's been but I have to admit to be being really disappointed that there hasn't been much demonstrable progress. There's still no 64-core version of the Parallella board. There was some talk about an update to 16-core Parallella board but I don't think it's shipped. As far as I know there's still no way to connect multiple Parallella boards using their "e-Link" ports. And lastly no way to assemble a small Adapteva cluster with a more favorable ratio between the Adapteva and Zynq (as far as power, heat and money go) than a stack of the 16-core Parallella boards.

Obviously I expected too much and I guess I'm being a tad unfair. That I've seen the dev team have always been decent, reasonable, and polite; so I really hate to write anything negative at all. I just thought they'd be much further along by now and that I might have a mini many-core cluster.

At some stage Kickstarter was more about suggesting dreams rather than contracts.

Yeah, that's probably true but I really got the feeling that when they first published that they sort of masked the fact that your average Raspberry Pi enthusiast wasn't going to be able to buy the board and run existing application code on the 16-core epiphany chip. They did eventually edit the page a bit to clarify this a bit.

As it turned out there were a few folks that appeared on the forums who had these kinds of misconceptions. After all this time there weren't that many; so I probably overestimated the number of people who signed on based on those misunderstandings.

Having said all that, I guess I should point out that while I do have a very skeptical view of kickstarter as a whole, I don't view Adapteva's kickstarter as being very unethical or as complete failure. They eventually were able to deliver on what I see a bare minimum of what they said they were going to do. Though I do feel we have to be honest and acknowledge that they weren't able to fully live up to all the claims and promises that were made... like you said "more about suggesting dreams than making contracts".

Have you used the Parallella?

https://www.kickstarter.com/projects/adapteva/parallella-a-s...

Kickstarter goal (first sentence from day 1): Making parallel computing easy to use has been described as "a problem as hard as any that computer science has faced". With such a big challenge ahead, we need to make sure that every programmer has access to cheap and open parallel hardware and development tools. Inspired by great hardware communities like Raspberry Pi and Arduino, we see a critical need for a truly open, high-performance computing platform that will close the knowledge gap in parallel programing. The goal of the Parallella project is to democratize access to parallel computing. If we can pull this off, who knows what kind of breakthrough applications could arise?"

Curious, besides being woefully late, what major promises did the Parallella campaign NOT live up to?

Since you're counting a streaming coprocessor as multicore, how is your offering "democratizing access to parallel computing" when the cheapest OpenCL capable GPU is half the price, and the programmer likely already owns one?

Part of democratizing is access to information, access to different choices, access to drivers.

Can GPUs do this. In my view, not a bad result considering 2014 general availability? How long (and how many $billions did it take for CUDA to catch on...)

https://www.parallella.org/2015/05/25/how-the-do-i-program-t...

In terms of cost, not really sure what you are referring to... supercomputer.io is free to researchers, which last time I checked is less than not zero.

As I mentioned previously, I intended to sign on the kickstarter campaign for the mini-cluster of 4 64-core boards. I didn't change my mind until very late into the campaign when it became clear to me that the 64-core version wasn't likely to happen. Since that time I've never seen any 64-core boards or eLink interconnected 16-core clusters for sale.

As far as major promises did the Parallella campaign NOT live up to (excluding the 64-core version), as far as I know it's still possible to connect multiple Parallella boards together using the Epiphany eLink interfaces. That novel interconnect was to me a key feature of the Epiphany architecture. Without it interfacing multiple Parallella boards together is forced to go through the Ethernet provided by the Zynq... which (assuming the eLink architecture performs as well as is claimed) is impediment & disappointment.

Without fully exploiting the eLink interconnect I think it's really difficult to get a full and accurate understanding of the capabilities and weaknesses of the Epiphany architecture.

Please don't take this in a personal or highly negative way. You guys have accomplished a lot and I really, really don't want to diminish that. You've also dealt with the difficulties that the project faced with a professional, straight forward, and positive demeanour, which I feel is both lacking in many other kickstarter campaigns and which speaks volumes for the quality of character of the developers working at Adaptiva.

I really want to see you guys succeed. I am still interested in buying / building a mini-cluster but I'm holding off until they can be properly interfaced through the eLink fabric. Also, I very much would like to see a next generation Epiphany chip and subsequent nextgen Parallella board, beyond the v2.0 Epiphany III Parallella board that was discussed in the forums.

There was a guy at the Makerfaire who had a Parallela based super computer and a Jetson [1] based one. He felt that by the time the Parallela stuff was running he had already surpassed it with the nVidia offering, faster and less money per Teraflop. It is an interesting space to be sure.

But how does trying to send fragments over the internet really work?

[1] http://www.nvidia.com/object/jetson-tk1-embedded-dev-kit.htm...

Yes, I know Brian's work well. His computers are awesome! Counting TFLOPS is important for some, but for others it's the number of CPU cores that count. Better to compare real workloads. In the case of supercomputer.io the key will be software packages for machine learning and smartly distributing data across the network. There is a long history for this kind of work with folding@home and BOINC.

This is the same problem as with other systems: You NEED to have an embarrassingly parallel problem to solve.

Sequential part and Amdahl’s law will catch you quickly.

Yes, agreed. Note that the cool part of distributed computing is that everything is duplicated.(IO / disk / DRAM / CPU)

From my reading of the architecture, it's not just parallelism, but _streaming_ parallelism. The individual cores really don't have memory or cache to do "real work" on their own. In many cases, they're all competing for memory bandwidth.

The target usage model seems to be where the actual algorithm employed can be pipelined, streaming in-flight results from one core to another. That has even more limited applicability than embarrassingly parallel architectures, and is incredibly difficult to map general problems to keeping the cores busy.

Signed up and donated my unused Parallella kickstarter board. Cool project!

Looks like the image download is hosted on S3, is it possible to get a direct S3 link to it? Then one could use the "?torrent" query string trick[1] to get a bittorrent download, I'd be happy to seed it for a while.

[1]: http://docs.aws.amazon.com/AmazonS3/latest/dev/S3TorrentRetr...

Hey there, resin.io founder here (we're working on this with adapteva) this should be possible, let me ask around internally and get back with more details

Oh, cool, just checking your site as well, it's a very real issue you solve, have some spare Raspberry Pi to try it out.

It feels like a big change in dev thinking that I cannot ssh into the board anymore, but also very interesting.

By the way, I see that you'll be supporting the SabreLite soon. Would it mean to be able to support other i.MX6-based boards like the VIA VAB-820 [1] or the UDOO?

[1]: http://www.viaembedded.com/en/boards/pico-itx/vab-820/

the idea is to make deployments repeatable, so while we may allow sshing in the future, it will be for diagnostic/experimentation reasons, not for altering the device state, so I think you understand the problem we're solving pretty well.

We keep adding devices and will soon release a guide on how users can add their own devices to the mix. That said, the primary determinant on whether we can support a device is whether a yocto/openembedded BSP exists and is relatively modern (uses a kernel above 3.8). If that exists, it's almost certain that resin support will be relatively easy. Happy to chat more, email in profile.

Yeah, i wasn't really missing SSH, as "I never thought to take away SSH" and actually it makes sense to have a different deployment and management mode. :)

And yeah, as a lucky chance, VAB-820 just had a yocto layer released with 3.10.17 [1]. Looking forward to see where this is headed!

[1]: https://github.com/viaembedded/meta-via-vab820-bsp

Any questions/comments? (AMA)

The efficiency of these board (Tflops per watt) is pretty amazing, when I first saw this board I imagined it would be used as powerful embedded servers in situations where battery power is required. However, it seems they have a much broader use cases as well.

What application will be deployed on this for the live test on May 30th?

It will be an image processing demo. Here's a hint... http://arxiv.org/abs/1312.5851

As a scientist used to running simulations on parallell computers, my immediate response is "show me one use case where this is faster than four sandy bridge core i7 desktops connected with Gb ethernet using OpenMX to get low latency". Even though you can boast a decent core count, those cores are connected through a really crappy high latency network (the internet).

Basically, there is no scientist in the world today who doesn't have the funding needed to scrape together four used core i7s and network them. Unless you can significantly beat that performance level, you're not "democratizing access to supercomputing for the scientists that need it" in any meaningful way. If you are significantly faster, even for just one particular application, that should be up front and center on you FAQ.

Are saying that folding@home, BOINC and all the rest is all useless and that all scientists need in to do research is 4 old i7 desktops? As far as the benchmark is concerned, check out this presentation from Ericsson on page 9 concerning FFTs on Xeon and on Epiphany. http://wiki.portal.chalmers.se/cse/uploads/FP/EngdalSlides.p...

No, the @home stuff is leveraging thousands and millions of computers. You have 18. From the slides you linked, the Epiphany board is 27x slower than a Xeon, so your distributed supercomputer is about as fast as a single desktop with a core i7, assuming you have perfect scaling.

This is exactly the same concept as @Home, just with the Parallella board. He will have thousands of those boards in the computing grid.

The slide stated that one Epiphany core is 27 times slower than one Xeon core. clearly the goal here is to ramp..the 324 cores all came online today. If we don't get to >10,000 cores, then the effort has failed.

792 so far now :)

Will you send out any test load before May 30? Would be better to see those cores to work if they are online, otherwise it feels kinda wasted. Wouldn't be surprised if people go offline after a while if there's no work done.

So, I'm confused. That slide says 64 cores, but the Parallella board website says 18. Which is it? You really need a proper benchmark.

Another question: how are you planning to implement fault tolerance? If you're running across hundreds of nodes via the internet, the probability of one failing while my job is running is high. Are you going to run a fault-tolerant scheduler?

And: how are you going to do file I/O? Does the user have to run the master MPI process on his/her own machine and do I/O there?

I think the 18 is counting 16 Epiphany cores plus 2 cores on the Zynq. The 64 is referring to a 64 core Epiphany chip that has only been made in prototype quantities, so far as I know.

When will we get a demonstration with a real interconnect? If the FLOPS/Watt is as good as claimed, why is nobody packaging this as an actual supercomputer?

Epiphany is being considered as the technology that comes "after the next". CPU is the default, then GPU, then "the others". Software is king and shared resource supercomputers have an incredibly long list of legacy software requirements. This is why we are focusing this effort on an application that is pretty dynamic at the moment, machine learning.

because small, slow computers are easy to make power-efficient. the truth is that the processor isn't that interesting, if you're taking a lots-of-wimpy-nodes approach (like BGQ).

Do you have some ideas/opinions who the industry will contain power consumption with big cores? Current super computers running big brawny cores and GPU accelerators are running at 5 GFLOPS/W at best. (consensus that we need to get to 50 GFLOPS/W).

Do you plan to record/stream the Parallella Technical Conference?

Why this over say spinning up a cluster on AWS? You're paying for HW and watts either way, and AWS already has the hardware sharing built in. For just $10, you can get ~100 GPU spot instances for an hour, which is a ridiculous number of cores/flops.

Not familiar with the price point, reference? Pricing at AWS seems to be more like $0.65/hour for one GPU?http://aws.amazon.com/ec2/pricing/

Either way, the point of supercomputer.io is that it would be free.(thanks to the contribution of everyone donating cycles). Think BOINC, not AWS. This is not for commercial use.

Spot pricing is more like $0.065/hr:

https://aws.amazon.com/ec2/purchasing-options/spot-instances...

Mind boggling pricing for sure, but $0.065/hr is still greater than $0/hour:-)

I'm impressed with how efficient the CPUs are. The 64-core Epiphany-IV is 50 GFLOPs/watt (single precision). That beats pretty much every GPU, as far as I know.

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Next!

Direct links if you still want to participate. Just dd card, powerup, and forget.

http://parallellocalypse.s3-website-us-east-1.amazonaws.com/...

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