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

Answer 1

Programming a GPU in CUDA is definitely easier. If you don't have any experience with programming FPGAs in HDL it will almost surely be too much of a challenge for you, but you can still program them with OpenCL which is kinda similar to CUDA. However, it is harder to implement and probably a lot more expensive than programming GPUs.

Which one is Faster?

GPU runs faster, but FPGA can be more efficient.

GPU has the potential of running at a speed higher than FPGA can ever reach. But only for algorithms that are specially suited for that. If the algorithm is not optimal, the GPU will loose a lot of performance.

FPGA on the other hand runs much slower, but you can implement problem-specific hardware that will be very efficient and get stuff done in less time.

It's kinda like eating your soup with a fork very fast vs. eating it with a spoon more slowly.

Both devices base their performance on parallelization, but each in a slightly different way. If the algorithm can be granulated into a lot of pieces that execute the same operations (keyword: SIMD), the GPU will be faster. If the algorithm can be implemented as a long pipeline, the FPGA will be faster. Also, if you want to use floating point, FPGA will not be very happy with it :)

I have dedicated my whole master's thesis to this topic. Algorithm Acceleration on FPGA with OpenCL

Answer 2

Others have given good answers, just wanted to add a different perspective. Here is my survey paper published in ACM Computing Surveys 2015 (its permalink is here), which compares GPU with FPGA and CPU on energy efficiency metric. Most papers report: FPGA is more energy efficient than GPU, which, in turn, is more energy efficient than CPU. Since power budgets are fixed (depending on cooling capability), energy efficiency of FPGA means one can do more computations within same power budget with FPGA, and thus get better performance with FPGA than with GPU. Of course, also account for FPGA limitations, as mentioned by others.

Answer 3

FPGA will not be favoured by those with a software bias as they need to learn an HDL or at least understand systemC.

For those with a hardware bias FPGA will be the first option considered.

In reality a firm grasp of both is required & then an objective decision can be made.

OpenCL is designed to run on both FPGA & GPU, even CUDA can be ported to FPGA.

FPGA & GPU accelerators can be used together

So it's not a case of what is better one or the other. There is also the debate about CUDA vs OpenCL

Again unless you have optimized & benchmarked both to your specific application you can not know with 100% certainty.

Many will simply go with CUDA because of its commercial nature & resources. Others will go with openCL because of its versatility.

Answer 4

I investigated the same question a while back. After chatting to people who have worked on FPGAs, this is what I get:

• FPGAs are great for realtime systems, where even 1ms of delay might be too long. This does not apply in your case;
• FPGAs can be very fast, espeically for well-defined digital signal processing usages (e.g. radar data) but the good ones are much more expensive and specialised than even professional GPGPUs;
• FPGAs are quite cumbersome to programme. Since there is a hardware configuration component to compiling, it could take hours. It seems to be more suited to electronic engineers (who are generally the ones who work on FPGAs) than software developers.

If you can make CUDA work for you, it's probably the best option at the moment. It will certainly be more flexible than a FPGA.

Other options include Brook from ATI, but until something big happens, it is simply not as well adopted as CUDA. After that, there's still all the traditional HPC options (clusters of x86/PowerPC/Cell), but they are all quite expensive.

Hope that helps.