OpenCL and the reconfigurable data centre

Part 3: General purpose data centres

Xilinx's adoption of the Open Computing Language (OpenCL) as part of its SDAccel development tool is important, not just for FPGAs but also for the computational capabilities of the data centre.

The FPGA vendor is promoting its chips as server co-processors to tackle complex processing tasks such as image searches, encryption, and custom computation.   

Search-engine specialists such as Baidu and Microsoft have seen a greater amount of traffic for image and video searches in the last two years, says Loring Wirbel, senior analyst at market research firm, The Linley Group: "All of a sudden they are seeing that these accelerator cards as being necessary for general-purpose data centres."

Xilinx and Altera have been way ahead of the niche FPGA vendors, indeed ahead of a lot of the network processor and graphics processor (GPU) vendors, in recognising the importance of OpenCL 

OpenCL was developed by Apple and is being promoted by the Khronos Group, an industry consortium set up to promote the integration of general purpose microprocessors, graphics processors, and digital signal processing blocks. And it is the FPGA vendors that are playing a pivotal role in OpenCL's adoption.

"Xilinx and Altera have been way ahead of the niche FPGA vendors, indeed ahead of a lot of the network processor and graphics processor (GPU) vendors, in recognising the importance of OpenCL," says Wirbel.

Altera announced the first compiler kit for OpenCL in 2013. "The significant thing Altera did was develop 'channels' for accelerator 'kernels'. Using the channels, kernels - the tasks to be accelerated in hardware - communicate with each other without needing the host processor. "It offers an efficient way for multiple co-processors to talk to each other," says Wirbel. The OpenCL community have since standardised elements of Altera's channels, now referred to as pipes.

"What Xilinx has brought with SDAccel is probably more significant in that it changes the design methodology for bringing together CPUs and GPUs with FPGAs," says Wirbel. Xilinx's approach may be specific to its FPGAs but Wirbel expects other firms to adopt a similar design approach. "Xilinx has created a new way to look at design that will ease the use of parallelism in general, and OpenCL," says Wirbel. (see SDAccel design approach, below.)

"Altera and Xilinx should be saluted in that they have encouraged people to start looking at OpenCL as a move beyond C for programming everything," says Wirbel. This broadening includes programming multi-core x86 and ARM processors, where a good parallel language is desirable. "You get better performance moving from C to C++, but OpenCL is a big jump," he says.

The future says that every data centre is going to become an algorithmically-rich one that can suddenly be reallocated to do other tasks

Wirbel does not have hard figures as to how many of a data centre's servers will have accelerator cards but he believes that every data centre is going to have specialised acceleration for tasks such as imaging and encryption as a regular feature within the next year or two. His educated guess is that it will be one accelerator card per eight host CPUs and possibly one in four.

Longer terms, such acceleration will change the computational nature of the data centre. "The future says that every data centre is going to become an algorithmically-rich one that can suddenly be reallocated to do other tasks," he says. It could mean that institutions such as national research labs that tackle huge-scale simulation work may no longer require specialist supercomputer resources.

"That is a little bit exaggerated because what will really happen is you will have to have whole clusters of data centres around the country allocated to ad-hoc virtual multiprocessing on very difficult problems," says Wirbel. "But the very notion that there needs to be assigned computers in data centres to one set of problems will be a thing of the past."

How does that relate to Xilinx's SDAccel and OpenCL?

"Some of this will happen because of tools like OpenCL as the language and tools like SDAccel for improving FPGAs," says Wirbel.

The SDAccel design approach

Xilinx has adopted the concept of co-simulation at an early stage of an FPGA-based co-processor design, alongside a server's x86 processor.    

Wirbel says that despite all the talk about co-simulation over the last decade, little has been done in practice. With co-simulation, an x86 processor or a graphics processor is simulated with a designer's IP logic that makes up an ASIC or an FPGA design.

Making FPGAs with very tightly-packed processors and with a very low power dissipation is critical; it is a big deal

"What Xilinx did is they said: the biggest problem is designers having to redo an FPGA, even placing and routing elements and going back to using back-end EDA [electronic design automation] tools," says Wirbel. "Maybe the best way of doing this is recognising we have to do some early co-simulation on a target x86 CPU board."

This is where OpenCL plays a role.

"The power of OpenCL is that it lets you define an acceleration task as a kernel," says Wirbel. It is these acceleration kernels that are sent to the hardware emulator with the x86 on board. The kernels can then be viewed in the co-simulation environment working alongside the x86 such that  any problems encountered can be tackled, and the two optimised. "Then, and only then, do you send it to a compiler for a particular FPGA architecture."  

The challenge for Xilinx is keeping a lid on the FPGA accelerator card's power consumption given the huge number of servers in a data centre.

"The large internet players have got to be able to add these new features for almost zero extra power," says Wirbel. "Making FPGAs with very tightly-packed processors and with a very low power dissipation is critical; it is a big deal."

For Part 1, click here

For Part 2, click here