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The Parallella Gen-1 has single precision throughput of 90 GFLOPS, power draw of 5W, and unit cost of $99. This yields 0.91 FLOPS/s per dollar and 18 FLOPS per
by n00b101 13y ago
The Parallella Gen-1 has single precision throughput of 90 GFLOPS, power draw of 5W, and unit cost of $99. This yields 0.91 FLOPS/s per dollar and 18 FLOPS per Watt.
By comparison, the NVIDIA GTX 780 Ti GPU has single precision throughput of 5046 GFLOPS, maximum TDP of 250W, and unit cost of $699. This yields 7.22 FLOPS per dollar and 20 FLOPS per Watt.
This makes the GPU board 8 times cheaper and at least 1.1 times more power efficient than the Parallella board.
Note that the average cost of electrical power in the US is about 10.5 cents per kilowatt-hour (kWh), which means a single GPU board running 24 hours at peak utilization would cost about 63 cents per day - so it would take over 3 years of 24/7 peak operation for the nominal power cost to equal the upfront unit cost. So while power efficiency is very important, that doesn't mean the unit cost can be ignored.
In addition to FLOPS/$ and FLOPS/W, one must also consider the processing power density, which essentially measures how many "FLOPS per cubic foot" are yielded by each device. This is important because these machines take up physical space and physical space (i.e. real estate) is costly. A single GPU board has 56 times the throughput of a single Parallella board, and I would say that a GPU board is only slightly larger than the Parallella board (mainly because GPU boards include massive cooling components which Parallella lacks). A machine that requires 56 times more real estate to achieve
the same performance is clearly not competitive.
So far we've only looked at floating-point throughput. One must also consider memory capacity and bandwidth of each device. The GTX 780 Ti has 3GB GDDR5 memory, and I understand that Parallella has 1GB memory. I don't know how the memory bandwidth compares between the two.
It's clear that the Parallella board has a long way to go before it could be competitive for supercomputing applications. And as Parallella tries to catch up, the industry will keep moving - GPUs (and other accelerators, like Xeon Phi) we continue to improve along all these dimensions and I imagine that NVIDIA/Intel/AMD have vastly larger R&D budgets than Parallella/Adapteva. So it is difficult to see how this could ever be a viable supercomputing platform and not just an interesting hobbyist board.
- puller 13y agoHow exactly do you run the NVIDIA GTX 780 Ti GPU by itself? Are its capabilities the same? Aren't you forced to use OpenCL or something?
- gamegoblin 13y agoSince it's NVIDIA, I imagine you'd use CUDA (since CUDA is a bit more mature than OpenCL, and tailored specifically to NVIDIA cards).
- deleted 13y ago[deleted]
- dkhenry 13y agoEverything you said is correct. However this was not a unit to go head to head with GPU's for industrial purposes. This is a way of battle testing their parallel processing technology which is significantly different from GPGPU. On their web site you can see that today apparently you can buy a solution that can scale to 4K cores at 5.6 TFLOPS and 70 GFLOPS/W If thats true and if it turns out their programming model is better then GPGPU programming then they could disrupt the parallel processing space. However they need to actually ship something, and the more time they take doing that the better Intel gets with MIC and AMD get's with HSA and at that point they would lose any advantage their architecture might have.
- sophacles 13y agoHow does this compare when you include the cost of running the computer driving the GPU? Also your analysis only considers the "use in cluster" case, there may be cases where the Parallella provides the power and size budget needed for a single case. For example, using this in endpoint devices in control systems, sensor arrays, etc, may make a lot of sense.
- n00b101 13y agoI think that's a fair point. NVIDIA GPUs are co-processors and need a host system, which adds significantly to the cost, power draw and size. You can try to minimize this (e.g. by maximizing GPU density in a single server, for example you can apparently get 16 GPUs in a blade server format) but it's definitely always a significant factor. My guess is that NVIDIA sees this problem and will move away from the co-processor model and move towards a System-on-a-Chip design with GPU, ARM CPU, NIC and unified memory in a single package. If Parallella saw large usage in the areas you mention (endpoint devices in control systems, sensor arrays, etc) then the competition could be good for the market and force NVIDIA's hand. Of course Intel is working in a similar direction with Knight's Landing and AMD as well with their APUs (Accelerated Processing Units).
- lambda 13y agoYou mean like the NVIDIA Tegra?
- georgeecollins 13y agoOne advantage Parellella has is that I can put it inside a small robot (backyardrobots.com) for say, image processing. I just can't do that with a NVIDIA card, particularly once you include the desktop that it connects to. There are probably other applications where a little card with that kind of processing is helpful. But you make a good point, if I were mining bitcoins, I would take the NVIDIA hands down.
- hosh 13y ago... or use ASICs and skip the general-purpose supercomputer all together.
- CraigJPerry 13y agoFor $99?
- hosh 13y agoWe were talking about card-based GPUs, but sure: http://www.amazon.com/ASICMiner-Block-Erupter-336MH-Sapphire/dp/B00F7CWKYU http://www.amazon.com/ASICMiner-Block-Erupter-336MH-Sapphire... There are far better things to do with a Parallela than mining, and far better things that work better for mining. I'd make a miner out of a Parallela system because I am curious about the tech and want practice at writing software for it not because I want a competitive miner.
- wmf 13y agoI don't even see any advantage for Parallela in embedded devices compared to something like an AMD Temash.
- hosh 13y agoFair enough, that's an interesting device. How does it compare with a Parallela? Are there anything as available as say, the Raspberry Pi? Or compared to the Parallela after coming into general availability? Would this chip necessarily run things like computer vision or deep learning better in the embedded space? How about if you were a hobbyist or a maker? That is, what if you weren't looking at industrial embedded application and instead, you're looking more at DIY?
- hosh 13y agoIf you're going to look at creating a large supercomputer cluster, sure, based on those numbers, GPU is the way to go. However, it would be more challenging to put the GPU board on a quadcopter drone than it would be the Parallela.