CUDA or FPGA for special purpose 3D graphics computations? [closed]

Answer 1

FPGAs have fallen out of favor in the HPC sector because they're a horrorterror to program. CUDA is in because it is much much nicer to program and will still give you some good performance. I would go with what the HPC community has gone with and do it in CUDA. It's easier, it's cheaper, it's more maintainable.

Answer 2

FPGAs

• What you need:
  • Learn VHDL/Verilog (and trust me you don't want to)
  • Buy hw for testing, licences for synthesis tools
  • If you already have infrastructure and you need to develop only your core
    • Develop design ( and it can take years )
  • If you don't:
    • DMA, hw driver, ultra expensive synthesis tools
    • tons of knowledge about buses, memory mapping, hw synthesis
    • build the hw, buy the ip cores
    • Develop design
    • Not mentioning of board developement
• For example average FPGA pcie card with chip Xilinx ZynqUS+ costs more than 3000$
• FPGA cloud is also costly 2$/h+
• Result:
  • This is something which requires resources of running company at least.

GPGPU (CUDA/OpenCL)

• You already have hw to test on.
• Compare to FPGA stuff:
  • Everything is well documented .
  • Everything is cheap
  • Everything works
  • Everything is well integrated to programming languages
• There is GPU cloud as well.
• Result:
  • You need to just download sdk and you can start.

Answer 3

CUDA has a fairly substantial code base of examples and a SDK, including a BLAS back-end. Try to find some examples similar to what you are doing, perhaps also looking at the GPU Gems series of books, to gauge how well CUDA will fit your applications. I'd say from a logistic point of view, CUDA is easier to work with and much, much cheaper than any professional FPGA development toolkit.

At one point I did look into CUDA for claim reserve simulation modelling. There is quite a good series of lectures linked off the web-site for learning. On Windows, you need to make sure CUDA is running on a card with no displays as the graphics subsystem has a watchdog timer that will nuke any process running for more than 5 seconds. This does not occur on Linux.

Any mahcine with two PCI-e x16 slots should support this. I used a HP XW9300, which you can pick up off ebay quite cheaply. If you do, make sure it has two CPU's (not one dual-core CPU) as the PCI-e slots live on separate Hypertransport buses and you need two CPU's in the machine to have both buses active.

Answer 4

at latest GTC'13 many HPC people agreed that CUDA is here to stay. FGPA's are cumbersome, CUDA is getting quite more mature supporting Python/C/C++/ARM.. either way, that was a dated question

Answer 5

• FPGAs are more parallel than GPUs, by three orders of magnitude. While good GPU features thousands of cores, FPGA may have millions of programmable gates.
• While CUDA cores must do highly similar computations to be productive, FPGA cells are truly independent from each other.
• FPGA can be very fast with some groups of tasks and are often used where a millisecond is already seen as a long duration.
• GPU core is way more powerful than FPGA cell, and much easier to program. It is a core, can divide and multiply no problem when FPGA cell is only capable of rather simple boolean logic.
• As GPU core is a core, it is efficient to program it in C++. Even it it is also possible to program FPGA in C++, it is inefficient (just "productive"). Specialized languages like VDHL or Verilog must be used - they are difficult and challenging to master.
• Most of the true and tried instincts of a software engineer are useless with FPGA. You want a for loop with these gates? Which galaxy are you from? You need to change into the mindset of electronics engineer to understand this world.

Answer 6

I would go with CUDA.
I work in image processing and have been trying hardware add-ons for years. First we had i860, then Transputer, then DSP, then the FPGA and direct-compiliation-to-hardware.
What innevitably happened was that by the time the hardware boards were really debugged and reliable and the code had been ported to them - regular CPUs had advanced to beat them, or the hosting machine architecture changed and we couldn't use the old boards, or the makers of the board went bust.

By sticking to something like CUDA you aren't tied to one small specialist maker of FPGA boards. The performence of GPUs is improving faster then CPUs and is funded by the gamers. It's a mainstream technology and so will probably merge with multi-core CPUs in the future and so protect your investment.