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2%%%%%%%%%%%%%%%%%%%%%%% file typeinst.tex %%%%%%%%%%%%%%%%%%%%%%%%%
4% This is the LaTeX source for the instructions to authors using
5% the LaTeX document class 'llncs.cls' for contributions to
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26\urldef{\mailsa}\path|{alfred.hofmann, ursula.barth, ingrid.haas, frank.holzwarth,|
27\urldef{\mailsb}\path|anna.kramer, leonie.kunz, christine.reiss, nicole.sator,|
28\urldef{\mailsc}\path|erika.siebert-cole, peter.strasser, lncs}|   
34\mainmatter  % start of an individual contribution
39% first the title is needed
40\title{Parallel Scanning with Bitstream Addition: An XML Case Study}
42% a short form should be given in case it is too long for the running head
43\titlerunning{Parallel Scanning with Bitstream Addition}
45% the name(s) of the author(s) follow(s) next
47% NB: Chinese authors should write their first names(s) in front of
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51\author{Robert D. Cameron
53\and Ehsan Amiri \and Kenneth S. Herdy \and Dan Lin \and Thomas C. Shermer \and Fred P. Popowich}
56\authorrunning{Cameron {\em et al}}
58% the affiliations are given next; don't give your e-mail address
59% unless you accept that it will be published
60\institute{Simon Fraser University, Surrey, BC, Canada\\
61\email{\{cameron, eamiri, ksherdy, lindanl, shermer, popowich\}}
68A parallel scanning method using the concept of bitstream addition is
69introduced and studied in application to the problem of XML
70parsing and well-formedness checking.   
71% The method parallelizes
72% finite-state transitions, using carry propagation to achieve up
73% to $W$ transitions with each $W$-bit binary addition operation.
74On processors supporting $W$-bit addition operations,
75the method can perform up to $W$ finite state transitions per instruction.
76The method is based on the concept of parallel bitstream technology,
77in which parallel streams of bits are formed such that each stream
78comprises bits in one-to-one correspondence with the character
79code units of a source data stream.    Parsing routines are initially
80prototyped in Python using its native support for unbounded
81integers to represent arbitrary-length  bitstreams.  A compiler
82then translates the Python code into low-level C-based implementations.
83These low-level implementations take advantage of
84the SIMD (single-instruction multiple-data) capabilities of commodity
85processors to yield a dramatic speed-up over
86traditional alternatives employing byte-at-a-time parsing.
87\keywords{SIMD text processing, parallel bitstream technology, XML, parsing}
95Traditional byte-at-a-time parsing technology is increasingly
96mismatched to the capabilities of modern processors.   Current
97commodity processors generally possess 64-bit general purpose registers
98as well as 128-bit SIMD registers, with 256-bit registers now
99appearing.   General purpose processing on graphics processors
100can make available 512-bit or wider registers.   Parsing models
101based on the traditional loading and processing of 8 bits at a time
102would seem to be greatly underutilizing processor resources.
104Unfortunately, parsing is hard to parallelize.   Indeed, in their seminal
105report outlining the landscape of parallel computing research,
106researchers from Berkeley identified the finite state machine
107methods underlying parsing and lexical processing as the hardest
108of the "13 dwarves" to parallelize, concluding at one point that
109"nothing helps." \cite{Asanovic:EECS-2006-183}   SIMD methods, in particular, would seem to
110be ill-suited to parsing, because textual data streams are seldom organized in
111convenient 16-byte blocks, tending to consist instead of
112variable-length items in generally unpredictable patterns.
113Nevertheless, there have been some notable works such as that of
114Scarpazza in applying the multicore and SIMD capabilities of the
115Cell/BE processor to regular expression matching \cite{Scarpazza:2009}
116Intel has also signalled the importance of accelerated string
117processing to its customers through the introduction of new string processing
118instructions in the SSE 4.2 instruction set extension, demonstrating
119how those features may be used to advantage in activities such as
120XML parsing \cite{XMLSSE42}
122Our research has been exploring a promising alternative approach, however, based on
123the concept of {\em parallel bit streams} \cite{Cameron2009,PPoPP08,CameronHerdyLin2008}.   
124In this approach, byte streams
125are first sliced into eight basis bit streams, one for each
126bit position within the byte.  Bit stream $i$ thus comprises
127the $i$th bit of each byte.   Using 128-bit SIMD registers, then,
128bitwise logic operations on these basis bit streams allows
129byte classification operations to be carried out in parallel
130for 128 bytes at a time.  For example, consider a character class
131bit stream \verb:[<]: using a 1 bit to mark the position of
132opening angle brackets in a byte stream.  This stream may
133computed as logical combination of the basis bit streams using
134only seven bitwise logical operations per 128 bytes.
136Based on this approach, our prior work has shown how parallel
137bit streams may be used to accelerate XML parsing by further
138taking advantage of processor {\em bit scan} instructions, commonly
139found within commodity processors\cite{CameronHerdyLin2008}.
140On current Intel or AMD processors, for example, these instructions
141allow one to determine the position of the first 1 bit in a group of 64
142in a single instruction.   Using these techniques, our Parabix 1
143parser demonstrated offered considerable accelaration of XML
144parsing in statistics gathering \cite{CameronHerdyLin2008} as well
145as GML to SVG conversion \cite{Herdy2008}.
147In this paper, we further increase the parallelism in our methods
148by introducing a new parallel scanning primitive using bitstream
149addition.   In essence, multiple 1 bits in a marker stream
150identify current scanning positions for multiple instances
151of a particular syntactic context within a byte stream.
152These multiple marker positions may each be independently
153advanced in parallel using addition and masking. 
154The net result is a new scanning primitive that
155allows multiple instances
156of syntactic elements to be parsed simultaneously.   For example,
157in dense XML markup, one might find several instances of particular
158types of markup tags within a given 64-byte block of text; parallel
159addition on 64-bit words allows all such instances to be processed at once.
162Other efforts to accelerate XML parsing include the use of custom
163XML chips \cite{Leventhal2009}, FPGAs \cite{DaiNiZhu2010}, and
164multithread/multicore speedups based on fast preparsing \cite{ZhangPanChiu09}.
166The remainder of this paper is organized as follows.
167Section 2 reviews the basics of parallel bitstream technology
168and introduces our new parallel scanning primitive.
169Section 3 illustrates how this primitive may be used
170in the lexical processing of XML references including the
171parallel identification of errors.   Section 4 goes on
172to consider the more complex task of XML tag processing
173that goes beyond mere tokenization.
174Building on these methods, Section 5 describes how to
175construct a
176complete solution to the problem of XML parsing and
177well-formedness checking, in order
178to gauge the applicability and power of the techniques.
179Section \ref{sec:compile} then considers
180the translation of high-level operations on unbounded bitstreams into
181equivalent low-level code using SIMD intrinsics in the C programming
183Performance results are presented in section 7, comparing
184the results of our generated implementations in comparison with
185a number of existing alternatives.
186The paper concludes with
187comments on the current status of the work and directions for
188further research.
190\section{The Parallel Bitstream Method}\label{sec:parabit}
195source data $\vartriangleleft$ & \verb`----173942---654----1----49731----321--`\\
196$B_7$ & \verb`.......................................`\\
197$B_6$ & \verb`.......................................`\\
198$B_5$ & \verb`111111111111111111111111111111111111111`\\
199$B_4$ & \verb`....111111...111....1....11111....111..`\\
200$B_3$ & \verb`1111...1..111...1111.1111.1...1111...11`\\
201$B_2$ & \verb`1111.1..1.1111111111.11111.1..1111...11`\\
202$B_1$ & \verb`.....11..1...1.............11.....11...`\\
203$B_0$ & \verb`11111111..111.1.111111111.111111111.111`\\
204\verb:[0-9]: & \verb`....111111...111....1....11111....111..`\\
207\caption{Basis and Character-Class Bitstreams}
213A bitstream is simply a sequence of $0$s and $1$s, where there is one such bit in the bitstream for each character in a source data stream.
214For parsing, and other text processing tasks, we need to consider multiple properties of characters at different stages during the parsing process. A bitstream can be associated with each of these properties, and hence there will be multiple (parallel) bitstreams associated with a source data stream of characters \cite{Cameron2009,PPoPP08}.
216The starting point for bitstream methods are \emph{basis} bitstreams
217and their use in determining \emph{character-class} bitstreams.
218The $k$th basis bitstream $B_k$ consists of the $k$th bit (0-based, starting at the LSB)
219of each character in the source data stream;
220thus each $B_k$ is dependent on the encoding of the source characters (ASCII, UTF-8, UTF-16, etc.).
221Given these basis bit streams, it is then possible to combine them
222using bitwise logic in order to compute character-class
223bit streams, that is, streams that identify the positions at which characters belonging
224to a particular class occur.  For example, the character class bitstream
225$D=$\verb:[0-9]: marks with $1$s the positions at which decimal digits
226occur.    These bitstreams are illustrated in Figure \ref{fig:inputstreams},
227for an example source data stream consisting of digits and hyphens.
228This figure also illustrates some of our conventions for figures:  the left triangle $\vartriangleleft$ after
229``source data'' indicates that all streams are read from right to left
230(i.e., they are in little-endian notation).  We also use hyphens
231in the input stream represent any character that is not relevant to a character
232class under consideration, so that relevant characters stand out.
233Furthermore, the $0$ bits in the bitstreams are represented by periods,
234so that the $1$ bits stand out.
237Transposition of source data to basis bit streams and calculation
238of character-class streams in this way is an overhead on parallel bit
239stream applications, in general.   However, using the SIMD
240capabilities of current commodity processors, these operations are quite
241fast, with an amortized overhead of about 1 CPU cycle per byte for
242transposition and less than 1 CPU cycle per byte for all the character
243classes needed for XML parsing \cite{CameronHerdyLin2008}.
244Improved instruction sets using parallel extract operations or
245inductive doubling techniques may further reduce this overhead significantly \cite{CameronLin2009,HilewitzLee2006}.
247Beyond the bitwise logic needed for character class determination,
248we also need \emph{upshifting} to deal with sequential combination.
249The upshift $n(S)$ of a bitstream $S$ is obtained by shifting the bits in $S$ one position forward,
250then placing a $0$ bit in the starting position of the bitstream; $n$ is meant to be mnemonic of ``next''.
251In $n(S)$, the last bit of $S$ may be eliminated or retained for error-testing purposes.
253\subsection{A Parallel Scanning Primitive}
255In this section, we introduce the principal new feature of the paper,
256a parallel scanning method based on bitstream addition.   Key to this
257method is the concept of {\em marker} bitstreams. 
258Marker bitstreams are used to represent positions of interest in the
259scanning or parsing of a source data stream.
260The appearance of a 1 at a position in a marker bitstream could, for example, denote
261the starting position of an XML tag in the data stream.   In general, the set of
262bit positions in a marker bitstream may be considered to be the current
263parsing positions of multiple parses taking place in parallel throughout
264the source data stream.
266Figure \ref{fig:scan1} illustrates the basic concept
267underlying parallel parsing with bitstream addition.
268As with the previous figures, all streams are shown in little-endian
269representation, with streams reading from right-to-left.
270The first row shows a source data stream that includes three
271spans of digits, $13840$, $1139845$, and $127$, with other nondigit characters shown
272as hyphens.  The second row specifies the parsing problem
273using a marker bitstream $M_0$ to mark three initial
274marker positions at the start of each span of digits.
275The parallel parsing task is to move each
276of the three markers forward through the corresponding spans of
277digits to the immediately following positions.
282\multicolumn{2}{l}{source data $\vartriangleleft$}
283                          & \verb`--721----5489311-----04831------`\\
284$M_0$ &                   & \verb`....1..........1.........1......`\\
285$D$   & $= \verb`[0..9]`$ & \verb`..111....1111111.....11111......`\\
286$M_1$ & $ = M_0 + D$      & \verb`.1......1...........1...........`
289\caption{Bitstream addition}
293The third row of Figure \ref{fig:scan1}
294shows the derived character-class bitstream $D$ identifying
295positions of all digits in the source stream. 
296The fourth row then illustrates the key concept: marker movement
297is achieved by binary addition of the marker and character
298class bitstreams.  As a marker 1 bit is combined using binary addition to
299a span of 1s, each 1 in the span becomes 0, generating
300a carry to add to the next position to the left. 
301For each span, the process terminates at the left end
302of the span, generating a 1 bit in the immediately
303following position.   In this way, binary addition produces the marker bitstream
304$M_1$, with each of the three markers
305moved independently through their respective spans of digits to the position at the end.
307However, the simple addition technique shown in Figure \ref{fig:scan1}
308does not account for digits in the source stream that do
309not play a role in a particular scanning operation.
310Figure \ref{fig:scan2} shows an example and how this
311may be resolved.   The source data stream is again shown in row 1,
312and the marker bitstream defining the initial marker positions
313for the the parallel parsing tasks shown in row 2.   
314Row 3 again contains the character class bitstream for digits $D$.
315Row 4 shows the result of bitstream addition, in which
316marker bits are advanced, but additional bits not
317involved in the scan operation are included in the result.
318However, these are easily removed in row 5, by masking
319off any bits from the digit bitstream; these can never
320be marker positions resulting from a scan.
325\multicolumn{2}{l}{source data $\vartriangleleft$}     
326                                                        & \verb`--134--31--59127---3--3474--`\\
327$M_0$ &                                                 & \verb`....1.........1..........1..`\\
328$D$   & $= \verb`[0..9]`$ & \verb`..111..11..11111...1..1111..`\\
329$M_1$ & $= M_0 + D$       & \verb`.1.....11.1....1...1.1......`\\
330$M_2$ & $= (M_0 + D) \wedge \neg D$ & \verb`.1........1..........1......`
333\caption{Parallel Scan Using Addition and Mask}
337The addition and masking technique allows matching of
338the regular expression \verb:[0-9]*: for any reasonable
339(conflict-free) set of initial markers specified in $M_0$.
340A conflict occurs when a span from one marker would run
341into another marker position.   However, such conflicts
342do not occur with the normal methods of marker bitstream
343formation, in which unique syntactic features of
344the input stream are used to specify the initial marker
347In the remainder of this paper, the notation $s(M, C)$
348denotes the operation to scan
349from an initial set of marker positions $M$ through
350the spans of characters belonging to a character class $C$ found at each position.
351\[s(M, C) = (M_0 + C)  \wedge \neg C\]
354\section{Parsing and Error Streams}
357Now consider how the parallel scanning primitive can
358be applied to the following problem: parse all occurrences
359of XML decimal character references according to the
360grammar of Figure \ref{fig:decrefgrmr} and identify any
361errors.  This grammar is simplified to omit other
362forms of XML reference including hexadecimal character
363references as well as general entity references.
364For the time being, we assume that our XML documents contain
365only the decimal references and no other use of the ``\verb:&:''
371DecRef & ::=   &        '\verb:&#:' Digit+ '\verb:;:'  \\
372Digit  & ::=   &         \verb:[0-9]:
375\caption{Grammar of Decimal Character References}
379Figure \ref{fig:decref} shows the parallel parsing of
380decimal references together with error checking.
381The source data includes four instances of potential
382decimal references beginning with the \verb:&: character.
383Of these, only the first one is legal according to
384the decimal reference syntax, the other three instances
385are in error.   These references may be parsed in
386parallel as follows.  The
387starting marker bitstream $M_0$ is formed from the \verb:[&]:
388character-class bitstream as shown in the second row.  The next row shows the
389result of the marker advance operation $n(M_0)$ to
390produce the new marker bitstream $M_1$.  At this point,
391a hash mark is required, so the first error bitstream $E_0$ is
392formed using a bitwise ``and'' operation combined with negation,
393to indicate violations of this condition.
394Marker bitstream $M_2$ is then defined as those positions
395immediately following any $M_1$ positions not in error.
396In the following row, the condition that at least
397one digit is required is checked to produce error bitstream $E_1$.
398A parallel scan operation is then applied through the
399digit sequences as shown in the next row to produce
400marker bitstream $M_3$.  The final error bitstream $E_2$ is
401produced to identify any references without a
402closing semicolon.
403In the penultimate row, the final marker bitstream $M_4$ marks the
404positions of all fully-checked decimal references, while the
405last row defines a unified error bitstream $E$ 
406indicating the positions of all detected errors.
411\multicolumn{2}{l}{source data $\vartriangleright$}     
412                                         & \verb`-&#978;-&9;--&#;--&#13!-`\\
413$M_0$ &                                  & \verb`.1......1....1....1.....`\\
414$M_1$ & $ = n(M_0)$                      & \verb`..1......1....1....1....`\\
415$E_0$ & $ = M_1 \wedge \neg $\verb:[#]:  & \verb`.........1..............`\\
416$M_2$ & $ = n(M_1 \wedge \neg  E_0)$     & \verb`...1...........1....1...`\\
417$E_1$ & $ = M_2 \wedge \neg  D$          & \verb`...............1........`\\
418$M_3$ & $ = s(M_2 \wedge \neg  E_1, D)$  & \verb`......1...............1.`\\
419$E_2$ & $ = M_3 \wedge \neg  $\verb:[;]: & \verb`......................1.`\\
420$M_4$ & $ = M_3 \wedge \neg  E_2$        & \verb`......1.................`\\
421$E $  & $= E_0 \, | \, E_1 \, | \, E_2$  & \verb`.........1.....1......1.`
424\caption{Parsing Decimal References}
429\subsection{Marker Stream Initialization}
431How are marker bitstreams initialized?   In general,
432this is an important problem, and dependent on the task at hand.   
433In the XML parsing context,
434we rely on an important property of well-formed
435XML: after an initial filtering pass to identify
436XML comments, processing instructions and CDATA
437sections, every remaining \verb:<: in the
438file must be the initial character of a start,
439end or empty element tag, and every remaining \verb:&:
440must be the initial character of a general entity
441or character reference. These assumptions permit easy creation of marker bitstreams for XML tags and XML entities.
443\subsection{Parallel Parsing of Sequential Structures}
445The critical and most interesting application of our parallel
446parsing techniques to XML is in parsing of the start, end and empty element
447tags that comprise the core of XML markup.   
448In particular, start tags have an iterative internal structure
449as shown in the grammar of Figure \ref{fig:stag-grmr}.  After an
450opening angle bracket and tag name, a tag may have multiple
451attribute-value pairs with values enclosed in single or double
452quotes.  Using the bitstream addition technique, our method
453is to start with the opening angle bracket of all tags as
454the initial marker bitstream for parsing the tags in parallel,
455advance through the element name and then use an iterative
456process to move through attribute-value pairs.
458Figure \ref{fig:stag-ex}
459illustrates the parallel parsing of three XML start tags.
460The figure omits determination
461of error bitstreams, processing of single-quoted attribute values and handling
462of empty element tags, for simplicity.  In this figure, the first
463four rows show the source data and three character class bitstreams:
464$N$ for characters permitted in XML names,
465$W$ for whitespace characters,
466and $Q$ for characters permitted within a double-quoted attribute value string. 
471STag         &  ::=   &        '\verb:<:' Name (WS  Attribute)* WS? '\verb:>:'  \\
472Attribute & ::=   &        Name WS? '=' WS? AttValue \\
473AttValue  &           ::=   &        `\verb:":' \verb:[^<"]*: `\verb:":' $|$ ``\verb:':'' \verb:[^<']*: ``\verb:':'' \\
474%DQuoted & ::= & \verb:[^<"]*:  \\
475%SQuoted & ::= & \verb:[^<']*:
478\caption{Grammar of XML Start Tags}
487source data $\vartriangleright$ & \verb`--<e a= "137">---<el2 a="17" a2="3379">---<x>--`\\
488$N = $ name chars & \verb`11.1.1...111..111.111.1..11..11..1111..111.1.11`\\
489$W = $ white space & \verb`....1..1.............1......1..................`\\
490$Q = \neg$\verb:[">]: & \verb`11.11111.111.1111.111111.11.1111.1111.1111.1111`\\
492$M_0$ & \verb`..1..............1........................1....`\\
493$M_1 = n(M_0)$ & \verb`...1..............1........................1...`\\
494$M_{0,7} = s(M_1, N)$ & \verb`....1................1......................1..`\\
495$M_{0,8} = s(M_{0,7}, W) \wedge \neg$\verb:[>]: & \verb`.....1................1........................`\\
497$M_{1,1} = s(M_{0,8}$ & \verb`......1................1.......................`\\
498$M_{1,2} = s(M_{1,1}, W) \wedge$\verb:[=]: & \verb`......1................1.......................`\\
499$M_{1,3} = n(M_{1,2})$ & \verb`.......1................1......................`\\
500$M_{1,4} = s({1,3}, W) \wedge$\verb:["]: & \verb`........1...............1......................`\\
501$M_{1,5} = n(M_{1,4})$ & \verb`.........1...............1.....................`\\
502$M_{1,6} = s(M_{1,5}, Q) \wedge$\verb:["]: & \verb`............1..............1...................`\\
503$M_{1,7} = n(M_{1,6})$ & \verb`.............1..............1..................`\\
504$M_{1,8} = s(M_{1,7}, W) \wedge \neg$\verb:[>]: & \verb`.............................1.................`\\
506$M_{2,1} = s(M_{1,8}, N)$ & \verb`...............................1...............`\\
507$M_{2,2} = s(M_{2,1}, W) \wedge$\verb:[=]: & \verb`...............................1...............`\\
508$M_{2,3} = n(M_{2,2})$ & \verb`................................1..............`\\
509$M_{2,4} = s(M_{2,3}, W) \wedge$\verb:["]: & \verb`................................1..............`\\
510$M_{2,5} = n(M_{2,4})$ & \verb`.................................1.............`\\
511$M_{2,6} = s(M_{2,5}, Q) \wedge$\verb:["]: & \verb`.....................................1.........`\\
512$M_{2,7} = n(M_{2,6})$ & \verb`......................................1........`\\
513$M_{2,8} = s(M_{2,7}, W) \wedge \neg$\verb:[>]: & \verb`...............................................`
521\caption{Start Tag Parsing}
526The parsing process is then illustrated in the remaining rows of the
527figure.    Each successive row shows the set of parsing markers as they
528advance in parallel using bitwise logic and addition.
529Overall, the sets of marker transitions can be divided into three groups.
531The first group
532$M_0$ through $M_{0,8}$ shows the initiation of parsing for each of the
533 tags through the
534opening angle brackets and  the element names, up to the first
535attribute name, if present.  Note that the there are no attribute names
536in the final tag shown, so the corresponding marker becomes zeroed
537out at the closing angle bracket.
538Since $M_{0,8}$ is not all $0$s, the parsing continues.
540The second group of marker transitions
541$M_{1,1}$ through $M_{1,8}$ deal with the parallel parsing of the first attribute-value
542pair of the remaining tags.
543After these operations, there are no more attributes
544in the first tag, so its corresponding marker becomes zeroed out.
545However, $M_{1, 8}$ is not all $0$s, as the second tags still has an unparsed attribute-value pair.
546Thus, the parsing continues.
548The third group of marker transitions $M_{2,1}$ through $M_{2,8}$ deal with the parsing of
549the second attribute-value pair of this tag.  The
550final transition to $M_{2,8}$ shows the zeroing out of all remaining markers
551once two iterations of attribute-value processing have taken place.
552Since $M_{2,8}$ is all $0$s, start tag parsing stops.
554The implementation of start tag processing uses a while loop that
555terminates when the set of active markers becomes zero,
556i.e.\  when some $M_{k, 8} = 0$.
558as an iteration over unbounded bitstreams, all start tags in the document
559are processed in parallel, using a number of iterations equal to the maximum
560number of attribute-value pairs in any one tag in the document.   
561However, in block-by-block processing, the cost of iteration is considerably reduced; the iteration for
562each block only requires as many steps as there are attribute-value pairs
563overlapping the block.
568%\subsection{Name Scans}
569%To illustrate the scanning of the name found in an XML start tag,
570%let us consider a sequence that might be found in an HTML file,
571%\verb:<div id="myid">:,
572%which is shown as the source data stream in Figure \ref{fig:stag-scan}.
577%source data & \verb:<div id="myid">:\\
578%$M_0$ & \verb`1..............`\\
579%$C_0$ & \verb`.111.11...1111.`\\
580%$M_1 = n(M_0)$ & \verb`.1.............`\\
581%$M_2 = s(M_1, D_0) \wedge \neg C_0$ & \verb`....1.........`\\
582%lastline & \verb`...............`
585%\caption{Scanning Names}
589%If we set our initial marker bitstream according to the procedure outlined in our discussion of marker bitstream initialization, we %obtain the bitstream $M_0$.
590%According to the grammar in Figure \ref{fig:stag-grmr}, we can then look for a \verb:Name: in an \verb:STag: after we have found a %5\verb:<:.
591%So, $M_1$ is the marker bitstream for the starting position of our name.
592%Although we do not know the length of the name, the $C_0$ bit vector can easily be set to $1$ for the characters that can be contained %in a name.
593%We can then use the scan function in a manner similar to how it was used in Figure \ref{fig:scan2} to scan through the entire name to %identify its end position.
596\subsection{Mask Formation with Bitstream Subtraction}
597%\subsection{Comment, CDATA and Processing Instructions}
600For various purposes in parsing, it may be necessary to
601introduce {\em mask bitstreams}, streams that identify spans
602of positions that are to be selected or excluded from processing
603in some fashion.
605In the case of XML processing, one important use of mask
606bitstreams is to filter out those \verb:&: and \verb:<:
607characters that occur within comments,
608CDATA sections and processing instructions and hence
609do not indicate starting marker positions
610for references or tags, respectively. Each of these
611has a relatively simple structure comprising primarily
612specific opening and closing delimiters: \verb;<!--;
613and \verb:-->: for comments, \verb:<![CDATA[:
614and \verb:]]>: for CDATA sections and \verb:<?:
615and \verb:?>: for processing instructions.   Processing
616instructions also have a small amount of internal structure
617consisting of a name that identifies the target of the
618processing instruction followed by optional parameter text.
620The content of each of these items is relatively unconstrained
621and may contain what appears to be XML markup of other kinds.
622This makes it impossible to reliably parse all instances of these
623types of markup using parallel techniques. 
624However, we can
625still use bitstream addition for the sequential parsing of
626these items from the beginning of the file.  In this case,
627instead of initializing a marker bitstream using specific marker
628symbols found throughout the file, the marker bitstream is
629initialized with a single 1 bit at the file start position.
631Nevertheless, parsing of comments, CDATA sections and
632processing instructions generally proceeds quite quickly in a
633sequential fashion.   Parsing steps generally involve long
634scans to an opening delimiter of one of these constructs,
635followed by further long scans through the content of the
636comment, CDATA section or processing instruction.
641source data $\vartriangleright$ & \verb`<!-- <<<< --> <?php 1<2 ?> <t> <![CDATA[ <demo/> ]]>.`\\
642$M_i$ & \verb`_1_____________1________________1____________________`\\
643$M_f$ & \verb`____________1____________1_________________________1_`\\
644$m = n(M_f) - m_i$ & \verb`_111111111111__11111111111______11111111111111111111_`\\
645$M_1 = n(\verb:[<]:) \wedge \neg m$ & \verb`____________________________1________________________`
648\caption{Mask Formation for Comments, CDATA and PI}
652A single pass scan is made to identify these structures
653within the document.   Once complete, a mask bitstream
654of 1 bits is formed to identify the union of the interiors
655of these structures.   Figure \ref{fig:CtCDPImask}
656illustrates.   Here, we assume this pass is complete
657to produce the initial and final positions of these
658constructs as $M_i$ and $M_f$.
659Here, $M_i$ is actually the set of upshifted start positions
660for comments, CDATA sections and processing instructions.
661Note the upshifting is requires to properly match the
662delimiter $n(\verb`[<]`) \wedge \verb`[!?]`$.
663$M_f$ is the set of final positions determined by parsing.
664This then allows the mask bitstream $m$ to be computed by
665bitstream subtraction.   (To perform the subtraction,
666imaging reversing $M_i$ and $M_f$ to produce the
667internal little-endian representation.)   Note that this
668stream does not allow us to mask out the opening \verb:<:
669of these constructs themselves.   But we can mask
670out $n(\verb`[<]`)$ to produce $M_1$ as shown.  However,
671this is adequate as an initial marker bitstream for tag
672parsing, as the formation of $M_1 = n(\verb`[<]`)$ is the
673first step shown in Figure \ref{fig:stag-ex}.
675\subsection{Python Prototyping}
677Taking advantage of Python's built-in support for
678unbounded integers to represent arbitrary-size bitstreams,
679we have implemented a complete parsing prototype for XML
680using only bitstream addition, subtraction and bitwise logic.
681The \verb:ParallelScanThru: operation is a straightforward
682implementation of the scanning primitive $s$ as shown
683previously.   The operation $n$ is simply implemented using
684an upshift operation, while subtraction and bitwise logic
685are directly supported.
687We have also used a modified version of this prototype as the
688input language of an experimental bitstream compiler that
689we have developed.   This compiler implements the compilation to
690block-by-block processing presented in Section 5, following.
693\section{XML Well-Formedness}
695In this section, we consider the full application of the parsing techniques
696of the previous section to the problem of XML well-formedness checking \cite{TR:XML}.
697This application is useful as a well-defined and commercially significant
698example to assess the overall applicability of parallel bit stream techniques. 
699To what extent can the well-formedness requirements of XML be
700completely discharged using parallel bitstream techniques?
701Are those techniques worthwhile in every instance, or
702do better alternatives exist for certain requirements?
703For those requirements that cannot be fully implemented
704using parallel bitstream technology alone, what
705preprocessing support can be offered by parallel bit stream
706technology to the discharge of these requirements in other ways?
707We address each of these questions in this section,
708and look not only at the question of well-formedness, but also at
709the identification of error positions in documents that
710are not well-formed.
713\subsection{Error and Error-Check Bitstreams}
715Most of the requirements of XML well-formedness checking
716can be implemented using two particular types of computed
717bitstream: {\em error bitstreams}, introduced in the previous section, and {\em error-check bitstreams}.
718Recall that an error bitstream stream is a stream marking the location of definite errors in accordance with
719a particular requirement.  For example, the
720$E_0$, $E_1$, and $E_2$ bitstreams as computed during parsing of
721decimal character references in Figure \ref{fig:decref}
722are error bitstreams.  One bits mark definite errors and zero bits mark the
723absence of error according to the requirement.   
724Thus the complete absence of errors according to the
725requirements listed may be determined by forming the
726bitwise logical ``or'' of these bitstreams and confirming
727that the resulting value is zero. An error check bitstream is one
728that marks potential errors to be further checked in
729some fashion during post-bitstream processing.   
730An example is the bitstream marking the start positions
731of CDATA sections.   This is a useful information stream
732computed during bitstream processing to identify opening
733\verb:<![: sequences, but also marks positions to
734subsequently check for the complete opening
735delimiter  \verb:<![CDATA[: at each position.
737In typical documents, most of these error-check streams will be quite sparse
738or even zero.   Many of the error conditions could
739actually be fully implemented using bitstream techniques,
740but at the cost of a number of additional logical and shift
741operations.   In general, however, the conditions are
742easier and more efficient to check one-at-a-time using
743multibyte comparisons on the original source data stream.
744With very sparse streams, it is very unlikely that
745multiple instances occur within any given block, thus
746eliminating the benefit of parallel evaluation of the logic.
748The requirement for name checking merits comment.   XML
749names may use a wide range of Unicode character values.
750It is too expensive to check every instance of an XML name
751against the full range of possible values.   However, it is
752possible and quite inexpensive to use parallel bitstream
753techniques to verify that any ASCII characters within a name
754are indeed legal name start characters or name characters.
755Furthermore, the characters that may legally follow a
756name in XML are confined to the ASCII range.  This makes
757it useful to define a name scan character class to include all the legal ASCII characters
758for names as well as all non-ASCII characters. 
759A namecheck character class bitstream will then be defined to identify nonASCII
760characters found within namescans.   In most documents
761this bitstream will be all $0$s; even in documents with substantial
762internationalized content, the tag and attribute names used
763to define the document schema tend to be confined to the
764ASCII repertoire.   In the case that this bitstream is nonempty,
765the positions of all 1 bits in this bitstream denote characters
766that need to be individually validated.
768Attribute names within a single XML start tag or empty
769element tag must be unique.  This requirement could be
770implemented using one of several different approaches. Standard
771approaches include: sequential search, symbol lookup, and Bloom filters
774In general, the use of error-check bitstreams is a straightforward,
775convenient and reasonably efficient mechanism for
776checking the well-formedness requirements.
778\subsection{Tag Matching}
780Except for empty element tags, XML tags come in pairs with
781names that must be matched.   To discharge this requirement,
782we form a bitstream consisting of the disjunction of three
783bitstreams formed during parsing: the bitstream marking the
784positions of start or empty tags (which have a common
785initial structure), the bitstream marking tags that end using
786the empty tag syntax (``\verb:/>:''), and the bitstream
787marking the occurrences of end tags.   In post-bitstream
788processing, we iterate through this computed bitstream
789and match tags using an iterative stack-based approach.
791\subsection{Document Structure}
793An XML document consists of a single root element with recursively
794defined structure together with material in the document
795prolog and epilogs.  Verifying this top-level structure and
796the structure of the prolog and epilog material is not
797well suited to parallel bitstream techniques, in particular, nor
798to any form of parallelism, in general.  In essence, the
799prolog and epilog materials occur once per document instance
800Thus the requirements to check this top-level structure
801for well-formedness are relatively minor, with an overhead
802that is quite low for sufficiently sized files.
806Overall, parallel bitstream techniques are quite well-suited to
807verification problems such as XML well-formedness checking. 
808Many of the character validation and syntax checking requirements
809can be conveniently and efficiently implemented using error streams.
810Other requirements are also supported by the computation of
811error-check streams for simple post-bitstream processing or
812composite stream over which iterative stack-based procedures
813can be defined for checking recursive syntax.
815\section{Compilation to Block-Based Processing} 
817While a Python implementation of the techniques described in the previous section works on unbounded bitstreams, a corresponding
818C implementation needs to process an input stream in blocks of size equal to the
819SIMD register width of the processor it runs on.
820So, to convert Python code into C, the key question becomes how
821to transfer information from one block to the next one.
822The answer lies in the use of {\em carry bits}, the collection of carries resulting from bitwise additions.
824In fact, in the methods we have outlined, all the
825the information flow between blocks for parallel bit stream
826calculations can be modeled using carry bits.   The parallel
827scanning primitive uses only addition and bitwise logic.
828Since the logic operations do not require information flow
829accross block boundaries, the information flow is entirely
830accounted by the carry.   Carry bits can also be used to
831capture the information flow associated with upshift
832operations, which move information forward one position
833in the file.   In essence, an upshift by one position for
834a bitstream is equivalent to the addition of the stream
835to itself; the bit shifted out in an upshift is in this
836case equivalent to the carry generated by the additon.
837The only other information flow requirement in the
838calculation of parallel bit streams occurs with the
839bitstream subtractions that are used to calculate span streams.
840In this case, the information flow is based on borrows
841generated, which can be handled in the same way as carries.
843Properly determining, initializing and inserting carry bits
844into a block-by-block implementation of parallel bit stream
845code is a task too tedious for manual implementation.
846We have thus developed compiler technology to automatically
847insert declarations, initializations and carry save/restore
848operations into appropriate locations when translating
849Python operations on unbounded bit streams into the
850equivalent low-level C code implemented on a block-by-block
851bases.  Our current compiler toolkit is capable of inserting
852carry logic using a variety of strategies, including both
853simulated carry bit processing with SIMD registers, as
854well as carry-flag processing using the processor general
855purpose registers and ALU.   Details are beyond the
856scope of this paper, but are described in the on-line
857source code repository at
859\section{Performance Results}
861In this section, we compare the performance of our \verb:xmlwf:
862implementation using the Parabix2 technology described above with several
863other implementations.
864These include the original \verb:xmlwf:
865distributed as an example application of the \verb:expat: XML
866parser,  implementations based on the widely used Xerces
867open source parser using both SAX and DOM interfaces,
868and an implementation using our prior Parabix 1 technology with
869bit scan operations. 
871Table \ref{XMLDocChars} 
872shows the document characteristics of the XML instances selected for this performance study,
873including both document-oriented and data-oriented XML files.
874The jawiki.xml and dewiki.xml XML files are document-oriented XML instances of Wikimedia books, written in Japanese and German, respectively. The remaining files are data-oriented.  The roads.gml file is an instance of Geography Markup Language (GML),
875a modeling language for geographic information systems as well as an open interchange format for geographic transactions on the Internet \cite{GML04}.  The po.xml file is an example of purchase order data, while the soap.xml file contains a large SOAP message.
876Markup density is defined as the ratio of the total markup contained within an XML file to the total XML document size.
877This metric is reported for each document.
883File Name               & dewiki.xml            & jawiki.xml            & roads.gml     & po.xml        & soap.xml \\ \hline   
884File Type               & document      & document      & data  & data  & data   \\ \hline     
885File Size (kB)          & 66240                 & 7343                  & 11584         & 76450         & 2717 \\ \hline
886Markup Item Count       & 406792                & 74882                 & 280724        & 4634110       & 18004 \\ \hline               
887Attribute Count         & 18808                 & 3529                  & 160416        & 463397        & 30001\\ \hline
888Avg. Attribute Size     & 8                     & 8                     & 6             & 5             & 9\\ \hline
889Markup Density          & 0.07                  & 0.13                  & 0.57          & 0.76          & 0.87  \\ \hline
893 \caption{XML Document Characteristics} 
894 \label{XMLDocChars} 
897Table \ref{parsers-cpb} shows performance measurements for the
898various \verb:xmlwf: implementations applied to the
899test suite.   Measurements are made on a single core of an
900Intel Core 2 system running a stock 64-bit Ubuntu 10.10 operating system. 
901Measurements are reported in CPU cycles per input byte of
902the XML data files in each case.
903The first row shows the performance of the Xerces C parser
904using the tree-building DOM interface. 
905Note that the performance
906varies considerably depending on markup density.  Note also that
907the DOM tree construction overhead is substantial and unnecessary
908for XML well-formedness checking.  Using the event-based SAX interface
909to Xerces gives much better results as shown in the
910second row.   
911The third row shows the best performance of our byte-at-a-time
912parsers, using the original   based on expat.
914The remaining rows of Table \ref{parsers-cpb} show performance
915of parallel bit stream implementations.  The first row shows
916the performance of our Parabix 1 implementation using
917bit scan instructions.   While showing a substantial speed-up
918over the byte-at-a-time parsers in every case, note also that
919the performance advantage increases with increasing markup
920density, as expected.   The last two rows show different versions of
921the \verb:xmlwf: implemented based on the Parabix 2 technology
922as discussed in this paper.   They differ in the carry handling
923strategy, with the ``simd\_add'' row referring to carry
924computations performed with simulated calculation of
925propagated and generated carries using SIMD operations, while the
926``adc'' row refers to an implementation directly employing
927the processor carry flags and add-with-carry instructions on
92864-bit general registers.  In both cases, the overall
929performance is quite impressive, with the increased
930parallelism of parallel bit scans clearly paying off in
931improved performance for dense markup.
938Parser Class & Parser & dewiki.xml  & jawiki.xml    & roads.gml  & po.xml & soap.xml  \\ \hline 
939\multirow{3}{*}{Byte-at-a-time} & Xerces (DOM)    &    39.8    &   46.7    &  81.6    &   122.5   &    143.7  \\ \cline{2-7} 
940& Xerces (SAX)   &     24.0   &    30.4     &  40.3    &   54.3    &   64.3     \\ \cline{2-7}
941& expat      &  14.2     &  17.9   &    35.4    &   44.7     &  53.6      \\ \hline 
942\multirow{3}{*}{Parallel Bit Stream} & Parabix1   &    8.313   &    9.335   &     13.345   &    16.136   &      19.047 \\ \cline{2-7}
943& gcc (simd\_add)    &  6.174   &       6.405   &       7.948   &       8.565  &        9.172 \\ \cline{2-7} 
944& llvm (adc64)       &  5.757   &       6.142   &       6.763   &       7.424   &       7.952 \\ \hline
945 \end{tabular}
947 \caption{Parser Performance (Cycles Per Byte)} 
951%gcc (simd\_add)    &   6.174   &       6.405   &       7.948   &       8.565   &       9.172 \\ \hline
952%llvm (simd\_add)   &   6.104   &       6.335   &       8.332   &       8.849   &       9.811 \\ \hline
953%gcc (adc64)        &   9.23   &        9.921   &       10.394   &      10.705   &      11.751 \\ \hline
954%llvm (adc64)       &   5.757   &       6.142   &       6.763   &       7.424   &       7.952 \\ \hline
955%gcc (SAHFLAHF)    &    7.951   &       8.539   &       9.984   &       10.219   &      11.388 \\ \hline
956%llvm(SAHFLAHF)    &    5.61   &        6.02   &        6.901   &       7.597   &       8.183 \\ \hline
963In application to the problem of XML parsing and well-formedness
964checking, the method of parallel parsing with bitstream addition
965is effective and efficient.   Using only bitstream addition
966and bitwise logic, it is possible to handle all of the
967character validation, lexical recognition and parsing problems
968except for the recursive aspects of start and end tag matching.
969Error checking is elegantly supported through the use of error
970streams that eliminate separate if-statements to check for
971errors with each byte.   The techniques are generally very
972efficient particularly when markup density is high.   However, for some
973conditions that occur rarely and/or require complex combinations
974of upshifting and logic, it may be better to define simpler
975error-check streams that require limited postprocessing using
976byte matching techniques.
978The techniques have been implemented and assessed for present-day commodity processors employing current SIMD technology.
979As processor advances see improved instruction sets and increases
980in width of SIMD registers, the relative advantages of the
981techniques over traditional byte-at-a-time sequential
982parsing methods is likely to increase substantially.
983Of particular benefit to this method, instruction set modifications
984that provide for more convenient carry propagation for long
985bitstream arithmetic would be most welcome.
987A significant challenge to the application of these techniques
988is the difficulty of programming.   The method of prototyping
989on unbounded bitstreams has proven to be of significant value
990in our work.   Using the prototyping language as input to
991a bitstream compiler has also proven effective in generating
992high-performance code.   Nevertheless, direct programming
993with bitstreams is still a specialized skill; our future
994research includes developing yet higher level tools to
995generate efficient bitstream implementations from grammars,
996regular expressions and other text processing formalisms.
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