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I think it gets squeezed even more with general purpose GPU computing.

by payne92 10y ago

astrodust 10y ago
Intel's trying to hedge their bets here by having strong FPGA capability. GPGPU compute is not the answer to every problem.
exelius 10y ago
There's no reason you can't have general-purpose SoCs that combine traditional pipeline-driven CPU cores, GPGPU cores and FPGA cores with memory, bus chips, storage, etc. We long ago reached the point of diminishing returns for cramming more raw computing power on a chip; so chips of the future will likely consolidate specialized features for various workloads in the name of power savings / speed increases for certain workloads.
- rjsw 10y ago
  Plus Intel owns the IP to all the pieces and is happy to provide documentation for them. Xilinx have Mali GPUs on their latest ARM+FPGA hybrids but that isn't much use to me if I can't find out how to program it.
ska 10y ago
The main problem with GPGPU is the "general" comes with a lot of caveats and costs. It's great for some problems, no so much for others.
- ZenoArrow 10y ago
  I may be wrong on this, but isn't one of the main drawbacks of the GPGPU approach the high power draw? High end GPUs do seem to be quite thirsty. I've no idea if any FPGAs offer better performance per watt, but I wouldn't be surprised if they did. EDIT: This article seems to suggest that FPGAs currently have an edge over GPUs when it comes to energy efficiency: http://app.scientificcomputing.com/articles/2015/03/optimizing-application-energy-efficiency-using-cpus-gpus-and-fpgas http://app.scientificcomputing.com/articles/2015/03/optimizi...
  - Stubb 10y ago
    FPGAs win in latency and I/O performance. Colleagues are running FPGA systems that handle nearly 100 Gbps of data. FPGAs will also get you latencies down in the microsecond range.
  - gh02t 10y ago
    Power draw with GPGPU is better per flop than normal CPUs, but not competitive with an FPGA tuned to a specific problem with the same equivalent throughout. I think FPGAs also win out in flexibility. GPUs don't integrate so well into e.g. the signal processing hardware for digital oscilloscopes (a common use for high end FPGAs) mostly because of latency and timing constraints.

I think it gets squeezed even more with general purpose GPU computing.

by payne92 10y ago

I think it gets squeezed even more with general purpose GPU computing.

Intel's trying to hedge their bets here by having strong FPGA capability. GPGPU compute is not the answer to every problem.

exelius 10y ago

There's no reason you can't have general-purpose SoCs that combine traditional pipeline-driven CPU cores, GPGPU cores and FPGA cores with memory, bus chips, storage, etc. We long ago reached the point of diminishing returns for cramming more raw computing power on a chip; so chips of the future will likely consolidate specialized features for various workloads in the name of power savings / speed increases for certain workloads.

rjsw 10y ago
Plus Intel owns the IP to all the pieces and is happy to provide documentation for them. Xilinx have Mali GPUs on their latest ARM+FPGA hybrids but that isn't much use to me if I can't find out how to program it.

ska 10y ago

The main problem with GPGPU is the "general" comes with a lot of caveats and costs. It's great for some problems, no so much for others.

ZenoArrow 10y ago
I may be wrong on this, but isn't one of the main drawbacks of the GPGPU approach the high power draw? High end GPUs do seem to be quite thirsty. I've no idea if any FPGAs offer better performance per watt, but I wouldn't be surprised if they did. EDIT: This article seems to suggest that FPGAs currently have an edge over GPUs when it comes to energy efficiency: http://app.scientificcomputing.com/articles/2015/03/optimizing-application-energy-efficiency-using-cpus-gpus-and-fpgas http://app.scientificcomputing.com/articles/2015/03/optimizi...
- Stubb 10y ago
  FPGAs win in latency and I/O performance. Colleagues are running FPGA systems that handle nearly 100 Gbps of data. FPGAs will also get you latencies down in the microsecond range.
- gh02t 10y ago
  Power draw with GPGPU is better per flop than normal CPUs, but not competitive with an FPGA tuned to a specific problem with the same equivalent throughout. I think FPGAs also win out in flexibility. GPUs don't integrate so well into e.g. the signal processing hardware for digital oscilloscopes (a common use for high end FPGAs) mostly because of latency and timing constraints.