From eyeball perf/pricing, it looks roughly like the Pascal is 50% faster for twice the price. Which isn't surprising from the mass-market to specialized transition.
They are using different CPUs for these tests. It seems that the CPU used for Pascal has a higher GHz speed. I hope that isn't confounding on the results.
I would assume that it definitely affects the result if the procedure involves moving data between GPU and main memory, but I do not know the size of the effect.
I am still pretty confident that the difference in performance comes mostly from differences between different GPU architectures.
They're using different CPUs[0], different amounts of RAM, different base OSes, different drivers and even one different product version (Tensorflow RC). As far as benchmarks go this is pretty awful. It does show some useful top-level comparisons, but it's about as un-rigorous as you can get.
Treating big companies as monoliths never works well. Different teams will inevitably choose to use different hardware, but the nVidia Tesla Kxx is popular among a lot of these "big four" types.
You can switch to "grouped" above the charts to change it.
...and you could argue that it does make sense for showing how X fares across categories Y,Z,T... The height of the bar is proportional to the average performance across the different frameworks.
Just bought a GTX 1070 mostly for reasons other than Deep Learning but it will be interesting to see how it stacks up compared to a 1080. Just hard to justify a 55% price increase for a 25% performance gain. Maybe SLI will get better in the future?
SLI is not for compute.
SLI scaling has nothing to do with compute performance, unless you are hitting a PCIe bandwidth bottleneck or have to jump over QPI you can have 1:1 scaling with multiple GPUs.
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[ 0.17 ms ] story [ 56.5 ms ] threadhttp://add-for.com/topics/benchmarks/caffe-vs-neon-vs-nvcaff...
Then make sure to change from stacked to grouped.
honestly, this doesn't seem like a big issue.
I am still pretty confident that the difference in performance comes mostly from differences between different GPU architectures.
Not a great test of GPUs in isolation.
[0] http://ark.intel.com/compare/82930,94188,77780
You need a Tesla card for most enterprise scale compute features.
...and you could argue that it does make sense for showing how X fares across categories Y,Z,T... The height of the bar is proportional to the average performance across the different frameworks.