5 ms·
...but I can buy an off the shelf $200 GPU (e.g. Nvidia 660) with stable tools that will work pretty good with highly parallel tasks, and get ~2000 GFLOPS accor
by osivertsson 13y ago
...but I can buy an off the shelf $200 GPU (e.g. Nvidia 660) with stable tools that will work pretty good with highly parallel tasks, and get ~2000 GFLOPS according to [1].
[1] https://en.wikipedia.org/wiki/GeForce_600_Series https://en.wikipedia.org/wiki/GeForce_600_Series
Or is there workloads where the parallella actually would make sense?
Power requirements are low, but development environment is probably rough, and development costs (regardless of stack) are going to be order of magnitudes higher than your electricity bill anyway for most projects.
- akiselev 13y agoConsidering the Z7000 series FPGA, yes there are many use cases where this is much better than any Cpu or GPU. it's a really uncommon use case though, and the best use for this board is probably as a developer board for ASICs with ARM IP cores. The benefit of the FPGA, though is that you can use it to add whatever peripherals or even coprocessors you want for your specialized application. It would be awesome to see a 10gbit fiber optic Phy on it.
- zhemao 13y agoI think he was asking about the 16-core co-processor, not the FPGA.
- akiselev 13y agoThe reply above explained it better. Intel CPUs are the equivalent to the ARM CPUs in the GPU solution and the big benefit of the FPGA is feeding data into the 64 core chip without being constrained to PCIe. (The FPGA has DSP cores too so it'll be able to feed processed data from sensors faster than an equiv Intel would).
- vidarh 13y agoA GPU will always beat this for tasks with many data streams and few instruction streams. This is for workloads with many instruction streams, which GPU's do really badly on. And it doesn't much for electricity costs to be important - electricity already makes up a substantial percentage of hosting costs for colocation.