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How 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
by sophacles 13y ago
How 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?