A *research* library. The purpose of this repository is to support an eventual paper. The immediate goal is not to produce a library that would be used in production systems. Of course, the end game is, indeed, to have an impact on production system.
> 44% of the time is in the tree construction. Of the remaining 56%, pretty much half is in the code to discover structural characters and half is in the naive code to flatten that out into a vector of u32 offsets (the 'iterate over set bits' problem).
> I'm focusing on the tree construction at the moment. I think we can abstract the structural characters to 3 operations during that stage (UP, DOWN, SIDEWAYS), batch them, and build out tree structure in bulk with data-driven SIMD operations rather than messing around with branches. It's probably OK to have a table with 3^^5 or even 3^^6 entries, and it's still probably OK to have some hard cases eliminated and handled on a slow path (e.g. someone hits you with 6 scope closes in a row, forcing you to pop 24 bytes into your tree construction stack). (...) I spend some time with SIMD and found myself going in circles. There's an utterly fantastic solution in there somewhere involving turning the tree building code into a transformation over multiple abstracted symbols (e.g. UP/UP/SIDEWAYS/DOWN). It looked great until I smacked up against the fact that the oh-so-elegant solution I had sketched out had, on its critical path, an unaligned store followed by load partially overlapping with that store, to maintain the stack of 'up' pointers. Ugh. (...) So I worked through about 6 alternate solutions of various levels of pretentiousness none of which made me particularly happy.
> The structure building is too slow for my taste. I'm not sure I want too much richer functionality in it. Many of the transformations which I would like to do seem better done on the tree (i.e. pruning out every second " character). But this code takes 44% of our time; it's outrageous.
> A future goal for Stage 3 will also be to thread together vectors or lists of similar structural elements (e.g. strings, objects, numbers, etc). A putative 'stage 4' will then be able to iterate in parallel fashion over these vectors (branchlessly, or at least without a pipeline-killing "Giant What I Am Doing Anyway" switch at the top) and transform them into more usable values. Some of this code is also inherently interesting (de-escaping strings, high speed atoi - an old favorite).
- Lu, Yifan, et al. "Tree structured data processing on GPUs." Cloud Computing, Data Science & Engineering-Confluence, 2017 7th International Conference on. IEEE, 2017.
- Sidhu, Reetinder. "High throughput, tree automata based XML processing using FPGAs." Field-Programmable Technology (FPT), 2013 International Conference on. IEEE, 2013.
- Dai, Zefu, Nick Ni, and Jianwen Zhu. "A 1 cycle-per-byte XML parsing accelerator." Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays. ACM, 2010.
- Lin, Dan, et al. "Parabix: Boosting the efficiency of text processing on commodity processors." High Performance Computer Architecture (HPCA), 2012 IEEE 18th International Symposium on. IEEE, 2012. http://parabix.costar.sfu.ca/export/1783/docs/HPCA2012/final_ieee/final.pdf
> Using this parallel bit stream approach, the vast majority of conditional branches used to identify key positions and/or syntax errors at each parsing position are mostly eliminated, which, as Section 6.2 shows, minimizes branch misprediction penalties. Accurate parsing and parallel lexical analysis is done through processor-friendly equations that require neither speculation nor multithreading.
- Deshmukh, V. M., and G. R. Bamnote. "An empirical evaluation of optimization parameters in XML parsing for performance enhancement." Computer, Communication and Control (IC4), 2015 International Conference on. IEEE, 2015.
APA
- Moussalli, Roger, et al. "Efficient XML Path Filtering Using GPUs." ADMS@ VLDB. 2011.
- Jianliang, Ma, et al. "Parallel speculative dom-based XML parser." High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on. IEEE, 2012.
- Li, Y., Katsipoulakis, N.R., Chandramouli, B., Goldstein, J. and Kossmann, D., 2017. Mison: a fast JSON parser for data analytics. Proceedings of the VLDB Endowment, 10(10), pp.1118-1129. http://www.vldb.org/pvldb/vol10/p1118-li.pdf
> They build the index and parse at the same time. The index allows them to parse faster... so they don't materialize the index. The speculative part is hastily described but they do hint that this is where much of the benefit comes from...
- Cameron, Robert D., et al. "Parallel scanning with bitstream addition: An xml case study." European Conference on Parallel Processing. Springer, Berlin, Heidelberg, 2011.
- Cameron, Robert D., Kenneth S. Herdy, and Dan Lin. "High performance XML parsing using parallel bit stream technology." Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds. ACM, 2008.
- Shah, Bhavik, et al. "A data parallel algorithm for XML DOM parsing." International XML Database Symposium. Springer, Berlin, Heidelberg, 2009.
- Cameron, Robert D., and Dan Lin. "Architectural support for SWAR text processing with parallel bit streams: the inductive doubling principle." ACM Sigplan Notices. Vol. 44. No. 3. ACM, 2009.
- Amagasa, Toshiyuki, Mana Seino, and Hiroyuki Kitagawa. "Energy-Efficient XML Stream Processing through Element-Skipping Parsing." Database and Expert Systems Applications (DEXA), 2013 24th International Workshop on. IEEE, 2013.
- Medforth, Nigel Woodland. "icXML: Accelerating Xerces-C 3.1. 1 using the Parabix Framework." (2013).
- Zhang, Qiang Scott. Embedding Parallel Bit Stream Technology Into Expat. Diss. Simon Fraser University, 2010.
- Cameron, Robert D., et al. "Fast Regular Expression Matching with Bit-parallel Data Streams."
- Lin, Dan. Bits filter: a high-performance multiple string pattern matching algorithm for malware detection. Diss. School of Computing Science-Simon Fraser University, 2010.
- Yang, Shiyang. Validation of XML Document Based on Parallel Bit Stream Technology. Diss. Applied Sciences: School of Computing Science, 2013.
- N. Nakasato, "Implementation of a parallel tree method on a GPU", Journal of Computational Science, vol. 3, no. 3, pp. 132-141, 2012.
> A JSON parser transforms a JSON text into another representation. A JSON parser MUST accept all texts that conform to the JSON grammar. A JSON parser MAY accept non-JSON forms or extensions. An implementation may set limits on the size of texts that it accepts. An implementation may set limits on the maximum depth of nesting. An implementation may set limits on the range and precision of numbers. An implementation may set limits on the length and character contents of strings."
- The structural characters are:
begin-array = [ left square bracket
begin-object = { left curly bracket
end-array = ] right square bracket
end-object = } right curly bracket
name-separator = : colon
value-separator = , comma
- It might be useful/important to prune "insignificant" white space characters. Maybe.
- Parsing and validating numbers fast could be potentially interesting, but the spec allows many things. (Geoff wrote: "Parsing numbers (especially floating point ones!) and other atoms is fiddly but doable.")
- Error handling is a nice problem. Of course, we have to define what we mean by an error and which error types the parser should be responsible for. Here are a few things that could/should probably trigger an error:
- An array structure is represented as square brackets surrounding zero or more values (or elements). Elements are separated by commas.
- An object structure is represented as a pair of curly brackets surrounding zero or more name/value pairs (or members). A name is a string. A single colon comes after each name, separating the name from the value. A single comma separates a value from a following name.
- Values must be one of false / null / true / object / array / number / string
- A string begins and ends with quotation marks. All Unicode characters may be placed within the quotation marks, except for the characters that must be escaped: quotation mark, reverse solidus, and the control characters (U+0000 through U+001F). [Decoding UTF-8 is fun](https://github.com/skeeto/branchless-utf8/blob/master/utf8.h).
> For example, in Stage 2 ("string detection") we could validate that the only place we saw backslashes was in places we consider "inside strings".
- Geoff's remark regarding the structure:
> next structural element
prev structural element
next structural element at the same level (i.e. skip over complex structures)
prev structural element at the same level
containing structural element ("up").
## Remarks on the code
- It seems that the use of ``bzero`` is discouraged.
- Per input byte, multiple bytes are allocated which could potentially be a problem when processing a very large document, hence one might want to be more incremental in practice maybe to minimize memory usage. For really large documents, there might be caching issues as well.
- The ``clmul`` thing is tricky but nice. (Geoff's remark: find the spaces between quotes, is actually a ponderous way of doing parallel prefix over XOR, which a mathematically adept person would have realized could be done with clmul by -1. Not me, I had to look it up: http://bitmath.blogspot.com.au/2016/11/parallel-prefixsuffix-operations.html.)
- It is possible, though maybe unlikely, that parallelizing the bitset decoding could be useful (https://lemire.me/blog/2018/03/08/iterating-over-set-bits-quickly-simd-edition/), and there is VCOMPRESSB (AVX-512)
Long term we should keep in mind the idea that what would be cool is a method to extract something like this code from an abstract description of something closer to a grammar.
