[digitailor]

As you talk about your background more I'm getting where you're coming from. GPUs are obviously more powerful and capable for high-end graphics disregarding power efficiency. And if you're doing Hadoop big data your perspective makes more sense to me, because storage-centric applications really have no use for FPGAs. Maybe they'll be useful for querying one day, but they sure can't store large datasets. I get your point that FPGAs are not a universal panacea, but in the case of GPUs for supercomputing, and not graphics tasks, I think they are.

[cmccabe]

In the past, GPUs weren't that practical for big data, since they offered a lot of CPU power, but not much in the way of I/O. It's possible that PCI-e 3.0 and 4.0 will change that, since they supposedly offer up to 15 GB/s and 31 GB/s bandwidth.

Big data is more than just querying. There are people running fancy machine learning algorithms. Those are the people most likely to use the flexibility of having a Java (or other programming language) interface.

I'm curious why you think FPGAs will win over GPUs in HPC. I struggle to imagine academics writing Verilog or VHDL. I can just barely see them sending in legions of grad students to try to write CUDA or OpenCL, but RTL design seems a bridge too far. I also haven't heard of any of the big FPGA companies trying to make a splash in HPC, whereas NVIDIA has been very active with Tesla and its other high-end offerings.