A Look at Altera's OpenCL SDK for FPGAs

Conclusions: Altera's Offerings and Competitive Landscape

FPGA vendors have long preached about the efficiency of reconfigurable hardware over general purpose processors. However, FPGAs have often been rejected as an option by many due to the programming challenges associated with them.  CPUs, and even GPUs, typically offered a much faster time to market and a larger talent pool of programmers. With OpenCL, FPGA vendors can play on an equal footing as far as programming is concerned.   Earlier, the decision to use an FPGA over another accelerator required significant resource commitment.  OpenCL allows FPGAs to be used as just another option lowering the risk and is potentially a game changer.

On a personal note, I am hoping for cheaper OpenCL capable FPGAs to hit the market. Currently, OpenCL capable FPGAs run into thousands of dollars. This is likely not an issue for the enterprise market typically targeted by FPGA vendors. However, OpenCL on FPGAs has not attracted as much mindshare as GPUs. GPU vendors have a huge advantage that anyone with a cheap laptop can start experimenting with and learning about GPUs. The easy and cheap access to GPUs enabled GPU computing to take off.  Whenever computing technology has become cheaper and/or easier to program, it has enabled many creative products around it in fields not thought of by the original technology makers. FPGAs have not yet reached that stage. While there is a community of FPGA enthusiasts, enabling OpenCL on cheaper FPGAs can increase this community many-fold.

Altera's OpenCL offering effectively promises customized hardware for your OpenCL kernels and the claim is that FPGAs will be more efficient than CPUs or GPUs at many tasks.  Applications that are not necessarily floating-point heavy, for example applications relying on custom integer datatypes, heavy bit-manipulation or fixed point calculations, are an area where FPGAs can shine because CPU and GPU hardware is not really tailored for such applications. The high-speed I/O connections available on an FPGA with external bandwidth far outstripping other accelerators is another advantage. I think streaming/filtering type of applications are an obvious niche that FPGAs can fulfill.  On the other hand, accelerators such as Nvidia Tesla and Xeon Phi will likely continue to do well in many double-precision floating-point applications because these accelerators are heavily optimized for such use cases. Applications such as image processing or data visualization that can make use of dedicated graphics related hardware on GPUs are also best done on a GPU. 

Finally, I would say I am cautiously optimistic at the prospect of using OpenCL on FPGAs.  I am impressed by the theoretical potential for OpenCL on FPGAs. However, I would  like to see third party studies comparing OpenCL SDKs for FPGAs and general purpose processors on various tasks to get a better understanding of performance and power consumption of various accelerator options. If you are evaluating GPUs or Xeon Phi for your application, you should definitely also consider evaluating OpenCL on FPGAs and compare their performance against other options for your application. OpenCL on FPGAs looks to be gaining steam and this will be an interesting space to watch in the near future and may very well be a turning point for wider adoption of FPGAs in various high-performance application segments.