Dear community, In follow-up of the e-mail below, we have made public our repository that contains our framework called Fletcher: A framework to integrate FPGA accelerators with Apache Arrow.
https://github.com/johanpel/fletcher With this framework you are able to provide an Arrow schema from which an easy-to-use hardware interface for FPGAs is generated, reaping all the benefits that Arrow already offers. On top of that it increases the programmability of any acceleration project you'd want to build on top of Arrow. During run-time, you simply pass your Arrow table to the run-time part of the framework and your hardware will be able to read from it by using row index ranges, receiving streams of data in the form of the type you've defined through the schema. Currently there is an example project that does regular expression matching on an Arrow table with strings, running on the Amazon EC2 F1 platform. We are not sponsored by Amazon, but as anyone can launch an instance with an FPGA there, we thought it would be a good starting point to hopefully gain some interest, even if you don't have an FPGA card yourself. FPGA accelerators can be so fast that more often than not serialization kills a relatively large part of the performance. Our measurements in this (relatively simple) example show that by using Arrow to prevent serialization, we sometimes get up to 6X improvement in performance over not using Arrow, especially if we start in languages that run on JVMs, for example. (Thanks everyone!) We are looking forward for people with a little bit of FPGA experience to try it out and receive their thoughts, comments, etc. Please drop me an e-mail. With kind regards, Johan Peltenburg Computer Engineering Lab Delft University of Technology ________________________________________ From: Johan Peltenburg [j.w.peltenb...@tudelft.nl] Sent: Tuesday, November 28, 2017 16:29 To: firstname.lastname@example.org Subject: Development of an FPGA Accelerator framework around Apache Arrow Dear community, Over the last year we have been looking into integration of FPGA accelerators with big data frameworks such as Spark. Before Arrow took off, we experienced many issues like serialization overhead but also garbage collection issues, as well as language interoperability issues with our low-level stack. These are all problems that Arrow is now already solving for us in a very nice manner. We see a growing amount of support for infrastructure providers such as Amazon that offer instances with FPGA resources already. Also, we see very rapid advancements from the hardware technology side, where soon enough accelerators can (cache-coherently) be attached to host memory (for example in OpenCAPI), allowing accelerators to work in the same virtual address space as the host process. We believe that a somewhat standardized format for data in-memory like Arrow can help us generalize big data processing in FPGAs tremendously. At the same time, it is known to us that FPGAs are notorious for their high development time and low programmability. Therefore, to alleviate some of these burdens put upon an accelerator developer, we are building a generalized framework around Arrow that abstracts away a very cumbersome aspect of FPGA design; interfacing with the data. The framework takes Arrow Schemas as input, and generates a layer that on the one side interfaces with whatever the host platform provides to access host memory (our initial framework will target support for AXI and OpenCAPI), and on the other side will interface with the user kernel. The user can express request for access to the data in terms of row index ranges. The generated layer will then provide data streams to the user, which the user may read using some kernel that they designed using high-level synthesis (for example they could write the kernel in OpenCL). Thus, they do not need to go into the specifics of the Arrow in-memory format, bother with creating hardware constructs to deal with index buffers and validity buffers, interfacing with the host-side bus, implementing FIFO's, etc... anymore. Hopefully this will be beneficial to faster deployment of FPGA accelerated applications based on data represented in the Arrow format. Currently the framework supports schemas of primitive data types, (nested) lists and structs. The major challenge here was to be able to generate hardware structures from the many forms of schemas that users may provide, but these challenges have been solved. We are in the process of testing the framework in simulation, and will soon move to a test on real FPGA systems. With a bit of luck we hope to initially release our framework in January. We will fully open-source this framework and will attempt to make it as vendor independent as possible. Initially we hope to provide some example applications that demonstrate some of the benefits of using our framework in terms of productivity and the benefits of using FPGAs for specific problems in big data in general. We are reaching out for your comments, questions, suggestions, etc... Please give us your thoughts about this. Thank you in advance. With kind regards, Johan Peltenburg Computer Engineering Lab Delft University of Technology