While particular XML applications may benefit from special-purpose hardware such as XML chips [Leventhal and Lemoine 2009] or appliances [Salz, Achilles and Maze 2009], the bulk of the world's XML processing workload will continue to be handled by XML software stacks on commodity processors. Exploiting the SIMD capabilities of such processors such as the SSE instructions of x86 chips, parallel bit stream technology offers the potential of dramatic improvement over byte-at-a-time processing for a variety of XML processing tasks. Character set issues such as Unicode validation and transcoding [Cameron 2007], normalization of line breaks and white space and XML character validation can be handled fully in parallel using this representation. Lexical item streams, such as the bit stream marking the positions of opening angle brackets, can also be formed in parallel. Bit-scan instructions of commodity processors may then be used on lexical item streams to implement rapid single-instruction scanning across variable-length multi-byte text blocks as in the Parabix XML parser [Cameron, Herdy and Lin 2008]. Overall, these techniques may be combined to yield end-to-end performance that may be 1.5X to 15X faster than alternatives [Herdy, Burggraf and Cameron 2008].

Continued research in parallel bit stream techniques as well as more conventional application of SIMD techniques in XML processing offers further prospects for improvement of core XML components as well as for tackling performance-critical tasks further up the stack. A newly prototyped technique for parallel tag parsing using bitstream addition is expected to improve parsing performance even beyond that achieved using sequential bit scans. Several techniques for improved symbol table performance are being investigated, including parallel hash value calculation and length-based sorting using the cheap length determination afforded by bit scans. To deliver the benefits of parallel bit stream technology to the Java world, we are developing Array Set Model (ASM) representations of XML Infoset and other XML information models for efficient transmission across the JNI boundary.

Amplifying these software advances, continuing hardware advances in commodity processors increase the relative advantage of parallel bit stream techniques over traditional byte-at-a-time processors. For example, the Intel Core architecture improved SSE processing to give superscalar execution of bitwise logic operations (3 instructions per cycle vs. 1 in Pentium 4). Upcoming 256-bit AVX technology extends the register set and replaces destructive two-operand instructions with a nondestructive three-operand form. General purpose programming on graphic processing units (GPGPU) such as the upcoming 512-bit Larrabee processor may also be useful for XML applications using parallel bit streams. New instruction set architectures may also offer dramatic improvements in core algorithms. Using the relatively simple extensions to support the principle of inductive doubling, a 3X improvement in several core parallel bit stream algorithms may be achieved [Cameron and Lin 2009]. Other possibilities include direct implementation of parallel extract and parallel deposit (pex/pdep) instructions [Hilewitz and Lee 2006], and bit-level interleave operations as in Larrabee, each of which would have important application to parallel bit stream processing.

Further prospects for XML performance improvement arise from leveraging the intraregister parallelism of parallel bit stream technology to exploit the interchip parallelism of multicore computing. Parallel bit stream techniques can support multicore parallelism in both data partitioning and task partitioning models. For example, the datasection partitioning approach of Wu, Zhang, Yu and Li may be used to partition blocks for speculative parallel parsing on separate cores followed by a postprocessing step to join partial S-trees [Wu et al. 2008].

In our view, the established and expected performance advantages of parallel bit stream technology over traditional byte-at-a-time processing are so compelling that parallel bit stream technology should ultimately form the foundation of every high-performance XML software stack. We envision a common high-performance XML kernel that may be customized to a variety of processor architectures and that supports a wide range of existing and new XML APIs. Widespread deployment of this technology should greatly benefit the XML community in addressing both the deserved and undeserved criticism of XML on performance grounds. A further benefit of improved performance is a substantial greening of XML technologies.

To complement our research program investigating fundamental algorithms and issues in high-performance XML processing, our work also involves development of open source software implementing these algorithms, with a goal of full conformance to relevant specifications. From the research perspective, this approach is valuable in ensuring that the full complexity of required XML processing is addressed in reporting and assessing processing results. However, our goal is also to use this open source software as a basis of technology transfer. A Simon Fraser University spin-off company, called International Characters, Inc., has been created to commercialize the results of this work using a patent-based open source model.

To date, we have not yet been successful in establishing a broader community of participation with our open source code base. Within open-source communities, there is often a general antipathy towards software patents; this may limit engagement with our technology, even though it has been dedicated for free use in open source.

A further complication is the inherent difficulty of SIMD programming in general, and parallel bit stream programming in particular. Considerable work is required with each new algorithmic technique being investigated as well as in retargetting our techniques for each new development in SIMD and multicore processor technologies. To address these concerns, we have increasingly shifted the emphasis of our research program towards compiler technology capable of generating parallel bit stream code from higher-level specifications.

A Catalog of Parallel Bit Streams for XML


In this section, we introduce the fundamental concepts of parallel bit stream technology and present a comprehensive catalog of parallel bit streams for use in XML processing. In presenting this catalog, the focus is on the specification of the bit streams as data streams in one-to-one correspondence with the character code units of an input XML stream. The goal is to define these bit streams in the abstract without initially considering memory layouts, register widths or other issues related to particular target architectures. In cataloging these techniques, we also hope to convey a sense of the breadth of applications of parallel bit stream technology to XML processing tasks.

Basis Bit Streams

Given a byte-oriented text stream represented in UTF-8, for example, we define a transform representation of this text consisting of a set of eight parallel bit streams for the individual bits of each byte. Thus, the Bit0 stream is the stream of bits consisting of bit 0 of each byte in the input byte stream, Bit1 is the bit stream consisting of bit 1 of each byte in the input stream and so on. The set of streams Bit0 through Bit7 are known as the basis streams of the parallel bit stream representation. The following table shows an example XML character stream together with its representation as a set of 8 basis streams.

Table I

XML Character Stream Transposition.

Input Data < t a g / >
ASCII 00111100 01110100 01100001 01100111 00101111 00111110
Bit0 0 0 0 0 0 0
Bit1 0 1 1 1 0 0
Bit2 1 1 1 1 1 1
Bit3 1 1 0 0 0 1
Bit4 1 0 0 0 1 1
Bit5 1 1 0 1 1 1
Bit6 0 0 0 1 1 1
Bit7 0 0 1 1 1 0

Depending on the features of a particular processor architecture, there are a number of algorithms for transposition to parallel bit stream form. Several of these algorithms employ a three-stage structure. In the first stage, the input byte stream is divided into a pair of half-length streams consisting of four bits for each byte, for example, one stream for the high nybble of each byte and another for the low nybble of each byte. In the second stage, these streams of four bits per byte are each divided into streams consisting of two bits per original byte, for example streams for the Bit0/Bit1, Bit2/Bit3, Bit4/Bit5, and Bit6/Bit7 pairs. In the final stage, the streams are further subdivided in the individual bit streams.

Using SIMD capabilities, this process is quite efficient, with an amortized cost of 1.1 CPU cycles per input byte on Intel Core 2 with SSE, or 0.6 CPU cycles per input byte on Power PC G4 with Altivec. With future advances in processor technology, this transposition overhead is expected to reduce, possibly taking advantage of upcoming parallel extract (pex) instructions on Intel technology. In the ideal, only 24 instructions are needed to transform a block of 128 input bytes using 128-bit SSE registers using the inductive doubling instruction set architecture, representing an overhead of less than 0.2 instructions per input byte.

General Streams

This section describes bit streams which support basic processing operations.

Deletion Mask Streams

DelMask (deletion mask) streams marks character code unit positions for deletion. Since the deletion operation is dependency free across many stages of XML processing, it is possible to simply mark and record deletion positions as deletion mask streams for future processing. A single invocation of a SIMD based parallel deletion algorithm can then perform the deletion of positions accumulated across a number of stages through a bitwise ORing of deletion masks. For example, deletion arises in the replacement of predefined entities with a single character, such as in the replacement of the &amp; entity, with the & character. Deletion also arises in XML end-of-line handling, and CDATA section delimeter processing. Several algorithms to delete bits at positions marked by DelMask are possible [Cameron 2008].

The following table provides an example of generating a DelMask in the context of bit stream based parsing of well-formed character references and predefined entities. The result is the generation of a DelMask stream.

Table II

DelMask Stream Generation

Input Data &gt; &#13; &#x0a;
GenRefs _11______________
DecRefs _______11________
HexRefs ______________11_
DelMask 111__1111__11111_
ErrorFlag _________________

Error Flag Streams

Error flag streams indicates the character code unit positions of syntactical errors. XML processing examples which benefit from the marking of error positions include UTF-8 character sequence validation and XML parsing [Cameron 2008].

The following table provides an example of using bit streams to parse character references and predefined entities which fail to meet the XML 1.0 well-formedness constraints. The result is the generation of an error flag stream that marks the positions of mal-formed decimal and hexical character references respectively.

Table III

Error Flag Stream Generation

Input Data &gt; &#, &#x;
GenRefs _11___________
DecRefs ______________
HexRefs ______________
DelMask 111__11__111__
ErrorFlag _______1____1_

Lexical Item Streams

Lexical item streams differ from traditional streams of tokens in that they are bit streams that mark the positions of tokens, whitespace or delimiters. Additional bit streams, such as the reference streams and callout streams, are subsequently constructed based on the information held within the set of lexical items streams. Differentiation between the actual tokens that may occur at a particular point (e.g., the different XML tokens that begin “<”) may be performed using multicharacter recognizers on the bytestream representation [Cameron, Herdy and Lin 2008].

A key role of lexical item streams in XML parsing is to facilitate fast scanning operations. For example, a left angle bracket lexical item stream may be formed to identify those character code unit positions at which a “<” character occurs. Hardware register bit scan operations may then be used by the XML parser on the left angle bracket stream to efficiently identify the position of the next “<”. Based on the capabilities of current commodity processors, a single register bit scan operation may effectively scan up to 64 byte positions with a single instruction.

Overall, the construction of the full set of lexical item stream computations requires approximately 1.0 CPU cycles per byte when implemented for 128 positions at a time using 128-bit SSE registers on Intel Core2 processors [Cameron, Herdy and Lin 2008]. The following table defines the core lexical item streams defined by the Parabix XML parser.

Table IV

Lexical item stream descriptions.

LAngle Marks the position of any left angle bracket character.
RAngle Marks the position of any right angle bracket character.
LBracket Marks the position of any left square bracker character.
RBracket Marks the position of any right square bracket character.
Exclam Marks the position of any exclamation mark character.
QMark Marks the position of any question mark character.
Hyphen Marks the position of any hyphen character.
Equals Marks the position of any equal sign character.
SQuote Marks the position of any single quote character.
DQuote Marks the position of any double quote character.
Slash Marks the position of any forward slash character
NameScan Marks the position of any XML name character.
WS Marks the position of any XML 1.0 whitespace character.
PI_start Marks the position of the start of any processing instruction at the '?' character position.
PI_end Marks the position of any end of any processing instruction at the '>' character position.
CtCD_start Marks the position of the start of any comment or CDATA section at the '!' character position.
EndTag_start Marks the position of any end tag at the '/' character position.
CD_end Marks the position of the end of any CDATA section at the '>' character position.
DoubleHyphen Marks the position of any double hyphen character.
RefStart Marks the position of any ampersand character.
Hash Marks the position of any hash character.
x Marks the position of any 'x' character.
Digit Marks the position of any digit.
Hex Marks the position of any hexidecimal character.
Semicolon Marks the position of any semicolon character.

The following illustrates a number of the lexical item streams.

Table V

Lexical Item Streams

Input Data <tag><tag> text &lt; &#x3e; </tag></tag>
LAngle 1____1______________________1_____1_____
RAngle ____1____1_______________________1_____1
WS __________1____1____1______1____________
RefStart ________________1____1__________________
Hex __1____1____1___________11_____1_____1__
Semicolon ___________________1______1_____________
Slash _____________________________1_____1____

UTF-8 Byte Classification, Scope and Validation Streams

An XML parser must accept the UTF-8 encoding of Unicode [XML 1.0]. It is a fatal error if an XML document determined to be in UTF-8 contains byte sequences that are not legal in that encoding. UTF-8 byte classification, scope, XML character validation and error flag bit streams are defined to validate UTF-8 byte sequences and support transcoding to UTF-16.

UTF-8 Byte Classification Streams

UTF-8 byte classification bit streams classify UTF-8 bytes based on their role in forming single and multibyte sequences. The u8Prefix and u8Suffix bit streams identify bytes that represent, respectively, prefix or suffix bytes of multibyte UTF-8 sequences. The u8UniByte bit stream identifies those bytes that may be considered single-byte sequences. The u8Prefix2, u8Prefix3, and u8Prefix4 refine the u8Prefix respectively indicating prefixes of two, three or four byte sequences respectively.

UTF-8 Scope Streams

Scope streams represent expectations established by UTF-8 prefix bytes. For example, the u8Scope22 bit stream represents the positions at which the second byte of a two-byte sequence is expected based on the occurrence of a two-byte prefix in the immediately preceding position. The u8scope32, u8Scope33, u8Scope42, u8scope43, and u8Scope44 complete the set of UTF-8 scope streams.

The following example demonstrates the UTF-8 character encoding validation process using parallel bit stream techniques. The result of this validation process is an error flag stream identifying the positions at which errors occur.

Table VI

UTF-8 Scope Streams

Input Data A Text in Farsi: ى ك  م ت ن  ف ا ر س ى
High Nybbles 42567726624677632D8DBDBDAD82D8DAD82D8D8
Low Nybbles 10458409E061239A099838187910968A9509399
u8Unibyte 11111111111111111__________1______1____
u8Prefix _________________1_1_1_1_1__1_1_1__1_1_
u8Suffix __________________1_1_1_1_1__1_1_1__1_1
u8Prefix2 _________________1_1_1_1_1__1_1_1__1_1_
u8Scope22 __________________1_1_1_1_1__1_1_1__1_1
ErrorFlag _______________________________________

UTF-8 Validation Streams

Proper formation of UTF-8 byte sequences requires that the correct number of suffix bytes always follow a UTF-8 prefix byte, and that certain illegal byte combinations are ruled out. For example, sequences beginning with the prefix bytes 0xF5 through 0xFF are illegal as they would represent code point values above 10FFFF. In addition, there are constraints on the first suffix byte following certain special prefixes, namely that a suffix following the prefix 0xE0 must fall in the range 0xA0–0xBF, a suffix following the prefix 0xED must fall in the range 0x80–0x9F, a suffix following the prefix 0xF0 must fall in the range 0x90–0xBF and a suffix following the prefix 0xF4 must fall in the range 0x80–0x8F. The task of ensuring that each of these constraints hold is known as UTF-8 validation. The bit streams xE0, xED, xF0, xF4, xA0_xBF, x80_x9F, x90_xBF, and x80_x8F are constructed to flag the aforementioned UTF-8 validation errors. The result of UTF-8 validation is a UTF-8 error flag bit stream contructed as the ORing of a series of UTF-8 validation tests.

XML Character Validation Streams

The UTF-8 character sequences 0xEF 0xBF 0xBF and 0xEF 0xBF 0xBE correspond to the Unicode code points 0xFFFE and 0xFFFF respectively. In XML 1.0, 0xFFFE and 0xFFFF represent characters outside the legal XML character ranges. As such, bit streams which mark 0xEF, 0xBF, and 0xBE character are constructed to flag illegal UTF-8 character sequences.

UTF-8 to UTF-16 Transcoding

UTF-8 is often preferred for storage and data exchange, it is suitable for processing, but it is significantly more complex to process than UTF-16 [Unicode]. As such, XML documents are typically encoded in UTF-8 for serialization and transport, and subsequently transcoded to UTF-16 for processing with programming languages such as Java and C#. Following the parallel bit stream methods developed for the u8u16 transcoder, a high-performance standalone UTF-8 to UTF-16 transcoder [Cameron 2008], transcoding to UTF-16 may be achieved by computing a series of 16 bit streams. One stream for each of the individual bits of a UTF-16 code unit.

The bit streams for UTF-16 are conveniently divided into groups: the eight streams u16Hi0, u16Hi1, ..., u16Hi7 for the high byte of each UTF-16 code unit and the eight streams u16Lo1, ..., u16Lo7 for the low byte. Upon conversion of the parallel bit stream data back to byte streams, eight sequential byte streams U16h0, U16h1, ..., U16Hi7 are used for the high byte of each UTF-16 code unit, while U16Lo0, U16Lo1,..., U16Lo7 are used for the corresponding low byte. Interleaving these streams then produces the full UTF-16 doublebyte stream.

UTF-8 Indexed UTF-16 Streams

UTF-16 bit streams are initially defined in UTF-8 indexed form. That is, with sets of bits in one-to-one correspondence with UTF-8 bytes. However, only one set of UTF-16 bits is required for encoding two or three-byte UTF-8 sequences and only two sets are required for surrogate pairs corresponding to four-byte UTF-8 sequences. The u8LastByte (u8UniByte , u8Scope22 , u8Scope33 , and u8Scope44 ) and u8Scope42 streams mark the positions at which the correct UTF-16 bits are computed. The bit sets at other positions must be deleted to compress the streams to the UTF-16 indexed form.

Control Character Streams

The control character bit streams marks ASCII control characters in the range 0x00-0x1F. Additional control character bit streams mark the tab, carriage return, line feed, and space character. In addition, a bit stream to mark carriage return line combinations is also constructed. Presently, control character bit streams support the operations of XML 1.0 character validation and XML end-of-line handling.

XML Character Validation

Legal characters in XML are the tab, carriage return, and line feed characters, together with all Unicode characters and excluding the surrogate blocks, as well as hexadecimal OxFFFE and OxFFFF [XML 1.0]. The x00_x1F bit stream is constructed and used in combination with the additional control character bit streams to flags the positions of illegal control characters.

XML 1.0 End-of-line Handling

In XML 1.0 the two-character sequence CR LF (carriage return, line feed) as well as any CR character not followed by a LF character must be converted to a single LF character [XML 1.0].

By defining carriage return, line feed, and carriage return line feed bit streams, dentoted CR, LF and CRLF respectively, end-of-line normalization processing can be performed in parallel using only a small number of logical and shift operations.

The following example demonstrates the generation of the CRLF deletion mask. In this example, the position of all CR characters followed by LF characters are marked for deletion. Isolated carriage returns are then replaced with LF characters. Completion of this process satisfies the XML 1.0 end-of-line handling requirements. For clarity, this example encodes input data carriage returns as C characters, whereas line feed characters are shown as L characters.

Table VII

XML 1.0 End-of-line Handling

Input Data first line C second line CL third line L one more C nothing left
CR -----------1-------------1------------------------1-------------
LF --------------------------1------------1------------------------
DelMask --------------------------1-------------------------------------

Call Out Streams

Call out bit streams mark the extents of XML markup structures such as comments, processing instruction and CDATA sections as well as physical structures such as character and entity references and general references. Call out streams are also formed for logical markup structures such start tags, end tags and empty element tags.

Comment, Processing Instruction and CDATA Section Call Out Streams

Comments, processing instructions and CDATA sections call out streams, Ct_Span, PI_Span and CD_Span respectively, define sections of an XML document which contain markup that is not interpreted by an XML processor. As such, the union of Ct_Span, PI_Span and CD_Span streams defines the regions of non-interpreteable markup. The stream formed by this union is termed the CtCDPI_Mask.

The following tables provides an example of constructing the CtCDPI_Mask.

Table VIII

CtCDPI Mask Generation

Input Data <?php?> <!-- example --> <![CDATA[ shift: a<<1 ]]>
CD_Span ___________________________1111111111111111111111_
Ct_Span ___________111111111111___________________________
PI_Span _11111____________________________________________
CtCDPI_Mask _111111__111111111111111__111111111111111111111111
ErrorFlag __________________________________________________

With the removal of all non-interpreteable markup, several phases of parallel bit stream based SIMD operations may follow operating on up to 128 byte positions on current commondity processors and assured of XML markup relevancy. For example, with the extents identification of comments, processing instructions and CDATA sections, XML names may be identified and length sorted for efficient symbol table construction.

As an aside, comments and CDATA sections must first be validated to ensure that comments do not contain "--" sequences and that CDATA sections do not contain illegal "]]>" sequences prior to ignorable markup stream generation.

Reference Call Out Streams

The reference call out streams are the GenRefs, DecRefs, and HexRefs streams. This subset of the call out streams marks the extents of all but the closing semicolon of general and character references.

Predefined character (&lt;,&gt;,&amp;,&apos;,&quot;) and numeric character references (&#nnnn;, &#xhhhh;) must be replaced by a single character [XML 1.0]. As previously shown, this subset of call out streams enables the construction of a DelMask for references.

Tag Call Out Streams

Whereas sequential bit scans over lexical item streams form the basis of XML parsing, in the current Parabix parser a new method of parallel parsing has been developed and prototyped using the concept of bitstream addition. Fundamental to this method is the concept of a cursor stream, a bit stream marking the positions of multiple parallel parses currently in process.

The results of parallel parsing using the bit stream addition technique produces a set of tag call out bit streams. These streams mark the extents of each start tag, end tag and empty element tag. Within tags, additional streams mark start and end positions for tag names, as well as attribute names and values. An error flag stream marks the positions of any syntactic errors encountered during parsing.

The set of tag call out streams consists of the ElemNames, AttNames, AttVals, Tags, EmptyTagEnds and EndTags bit streams. The following example demonstrates the bit stream output produced which from parallel parsing using bit stream addition.

Table IX

Tag Call Out Streams

Input Data <root><t1>text</t1><t2 a1='foo' a2 = 'fie'>more</t2><tag3 att3='b'/></root>
ElemNames _1111__11___________11_______________________________1111__________________
AttNames _______________________11_______11________________________1111_____________
AttrVals __________________________11111______11111_____________________111_________
EmptyTagEnds ___________________________________________________________________1_______
EndTags _______________111______________________________111__________________11111_
Start/EmptyTags _1111__11___________1111111111111111111111___________11111111111111________
ErrorFlag ___________________________________________________________________________

SIMD Beyond Bitstreams: Names and Numbers

Whereas the fundamental innovation of our work is the use of SIMD technology in implementing parallel bit streams for XML, there are also important ways in which more traditional byte-oriented SIMD operations can be useful in accelerating other aspects of XML processing.

Name Lookup

Efficient symbol table mechanisms for looking up element and attribute names is important for almost all XML processing applications. It is also an important technique merely for assessing well-formedness of an XML document; rather than validating the character-by-character composition of each occurrence of an XML name as it is encountered, it is more efficient to validate all but the first occurrence by first determining whether the name already exists in a table of prevalidated names.

The first symbol table mechanism deployed in the Parabix parser simply used the hashmaps of the C++ standard template library, without deploying any SIMD technology. However, with the overhead of character validation, transcoding and parsing dramatically reduced by parallel bit stream technology, we found that symbol lookups then accounted for about half of the remaining execution time in a statistics gathering application [Cameron, Herdy and Lin 2008]. Thus, symbol table processing was identified as a major target for further performance improvement.

Our first effort to improve symbol table performance was to employ the splash tables with cuckoo hashing as described by Ross [Ross 2006], using SIMD technology for parallel bucket processing. Although this technique did turn out to have the advantage of virtually constant-time performance even for very large vocabularies, it was not particularly helpful for the relatively small vocabularies typically found in XML document processing.

However, a second approach has been found to be quite useful, taking advantage of parallel bit streams for cheap determination of symbol length. In essence, the length of a name can be determined very cheaply using a single bit scan operation. This then makes it possible to use length-sorted symbol table processing, as follows. First, the occurrences of all names are stored in arrays indexed by length. Then the length-sorted arrays may each be inserted into the symbol table in turn. The advantage of this is that a separate loop may be written for each length. Length sorting makes for very efficient name processing. For example hash value computations and name comparisons can be made by loading multibyte values and performing appropriate shifting and masking operations, without the need for a byte-at-a-time loop. In initial experiments, this length-sorting approach was found to reduce symbol lookup cost by a factor of two.

Current research includes the application of SIMD technology to further enhance the performance of length-sorted lookup. We have identified a promising technique for parallel processing of multiple name occurrences using a parallel trie lookup technique. Given an array of occurrences of names of a particular length, the first one, two or four bytes of each name are gathered and stored in a linear array. SIMD techniques are then used to compare these prefixes with the possible prefixes for the current position within the trie. In general, a very small number of possibilities exist for each trie node, allowing for fast linear search through all possibilities. Typically, the parallelism is expected to exceed the number of possibilities to search through at each node. With length-sorting to separate the top-level trie into many small subtries, we expect only a single step of symbol lookup to be needed in most practical instances.

The gather step of this algorithm is actually a common technique in SIMD processing. Instruction set support for gather operations is a likely future direction for SIMD technology.

Numeric Processing

Many XML applications involve numeric data fields as attribute values or element content. Although most current XML APIs uniformly return information to applications in the form of character strings, it is reasonable to consider direct API support for numeric conversions within a high-performance XML engine. With string to numeric conversion such a common need, why leave it to application programmers?

High-performance string to numeric conversion using SIMD operations also can considerably outperform the byte-at-a-time loops that most application programmers or libraries might employ. A first step is reduction of ASCII bytes to corresponding decimal nybbles using a SIMD packing operation. Then an inductive doubling algorithm using SIMD operations may be employed. First, 16 sets of adjacent nybble values in the range 0-9 can be combined in just a few SIMD operations to 16 byte values in the range 0-99. Then 8 sets of byte values may similarly be combined with further SIMD processing to produce doublebyte values in the range 0-9999. Further combination of doublebyte values into 32-bit integers and so on can also be performed using SIMD operations.

Using appropriate gather operations to bring numeric strings into appropriate array structures, an XML engine could offer high-performance numeric conversion services to XML application programmers. We expect this to be an important direction for our future work, particularly in support of APIs that focus on direct conversion of XML data into business objects.

APIs and Parallel Bit Streams

The ILAX Streaming API

The In-Line API for XML (ILAX) is the base API provided with the Parabix parser. It is intended for low-level extensions compiled right into the engine, with minimum possible overhead. It is similar to streaming event-based APIs such as SAX, but implemented by inline substitution rather than using callbacks. In essence, an extension programmer provides method bodies for event-processing methods declared internal to the Parabix parsing engine, compiling the event processing code directly with the core code of the engine.

Although ILAX can be used directly for application programming, its primary use is for implementing engine extensions that support higher-level APIs. For example, the implementation of C or C++ based streaming APIs based on the Expat [Expat] or general SAX models can be quite directly implemented. C/C++ DOM or other tree-based APIs can also be fairly directly implemented. However, delivering Parabix performance to Java-based XML applications is challenging due to the considerable overhead of crossing the Java Native Interface (JNI) boundary. This issue is addressed with the Array Set Model (ASM) concept discussed in the following section.

With the recent development of parallel parsing using bitstream addition, it is likely that the underlying ILAX interface of Parabix will change. In essence, ILAX suffers the drawback of all event-based interfaces: they are fundamentally sequential in number. As research continues, we expect efficient parallel methods building on parallel bit stream foundations to move up the stack of XML processing requirements. Artificially imposing sequential processing is thus expected to constrain further advances in XML performance.

Efficient XML in Java Using Array Set Models

In our GML-to-SVG case study, we identified the lack of high-performance XML processing solutions for Java to be of particular interest. Java byte code does not provide access to the SIMD capabilities of the underlying machine architecture. Java just-in-time compilers might be capable of using some SIMD facilities, but there is no real prospect of conventional compiler technology translating byte-at-a-time algorithms into parallel bit stream code. So the primary vehicle for delivering high-performance XML processing is to call native parallel bit stream code written in C through JNI capabilities.

However, each JNI call is expensive, so it is desirable to minimize the number of calls and get as much work done during each call as possible. This mitigates against direct implementation of streaming APIs in Java through one-to-one mappings to an underlying streaming API in C. Instead, we have concentrated on gathering information on the C side into data structures that can then be passed to the Java side. However, using either C pointer-based structures or C++ objects is problematic because these are difficult to interpret on the Java side and are not amenable to Java's automatic storage management system. Similarly, Java objects cannot be conveniently created on the C side. However, it is possible to transfer arrays of simple data values (bytes or integers) between C and Java, so that makes a reasonable focus for bulk data communication between C and Java.

Array Set Models are array-based representations of information representing an XML document in accord with XML InfoSet [XML Infoset] or other XML data models relevant to particular APIs. As well as providing a mechanism for efficient bulk data communication across the JNI boundary, ASMs potentially have a number of other benefits in high-performance XML processing.

  • Prefetching. Commodity processors commonly support hardware and/or software prefetching to ensure that data is available in a processor cache when it is needed. In general, prefetching is most effective in conjunction with the continuous sequential memory access patterns associated with array processing.

  • DMA. Some processing environments provide Direct Memory Access (DMA) controllers for block data movement in parallel with computation. For example, the Cell Broadband Engine uses DMA controllers to move the data to and from the local stores of the synergistic processing units. Arrays of contiguous data elements are well suited to bulk data movement using DMA.

  • SIMD. Single Instruction Multiple Data (SIMD) capabilities of modern processor instruction sets allow simultaneous application of particular instructions to sets of elements from parallel arrays. For effective use of SIMD capabilities, an SoA (Structure of Arrays) model is preferrable to an AoS (Array of Structures) model.

  • Multicore processors. Array-oriented processing can enable the effective distribution of work to the individual cores of a multicore system in two distinct ways. First, provided that sequential dependencies can be minimized or eliminated, large arrays can be divided into separate segments to be processed in parallel on each core. Second, pipeline parallelism can be used to implement efficient multipass processing with each pass consisting of a processing kernel with array-based input and array-based output.

  • Streaming buffers for large XML documents. In the event that an XML document is larger than can be reasonably represented entirely within processor memory, a buffer-based streaming model can be applied to work through a document using sliding windows over arrays of elements stored in document order.

Saxon-B TinyTree Example

As a first example of the ASM concept, current work includes a proof-of-concept to deliver a high-performance replacement for building the TinyTree data structure used in Saxon-B 6.5.5, an open-source XSLT 2.0 processor written in Java [Saxon]. Although XSLT stylesheets may be cached for performance, the caching of source XML documents is typically not possible. A new TinyTree object to represent the XML source document is thus commonly constructed with each new query so that the overall performance of simple queries on large source XML documents is highly dependent on TinyTree build time. Indeed, in a study of Saxon-SA, the commercial version of Saxon, query time was shown to be dominated by TinyTree build time [Kay 2008]. Similar performance results are demonstrable for the Saxon-B XSLT processor as well.

The Saxon-B processor studied is a pure Java solution, converting a SAX (Simple API for XML) event stream into the TinyTree Java object using the efficient Aelfred XML parser [Ælfred]. The TinyTree structure is itself an array-based structure mapping well suited to the ASM concept. It consists of six parallel arrays of integers indexed on node number and containing one entry for each node in the source document, with the exception of attribute and namespace nodes [Saxon]. Four of the arrays respectively provide node kind, name code, depth, and next sibling information for each node, while the two others are overloaded for different purposes based on node kind value. For example, in the context of a text node , one of the overloaded arrays holds the text buffer offset value whereas the other holds the text buffer length value. Attributes and namespaces are represented using similiar parallel array of values. The stored TinyTree values are primarily primitive Java types, however, object types such as Java Strings and Java StringBuffers are also used to hold attribute values and comment values respectively.

In addition to the TinyTree object, Saxon-B maintains a NamePool object which represents a collection of XML name triplets. Each triplet is composed of a Namespace URI, a Namespace prefix and a local name and encoded as an integer value known as a namecode. Namecodes permit efficient name search and look-up using integer comparison. Namecodes may also be subsequently decoded to recover namespace and local name information.

Using the Parabix ILAX interface, a high-performance reimplementation of TinyTree and NamePool data structures was built to compare with the Saxon-B implementation. In fact, two functionally equivalent versions of the ASM java class were constructed. An initial version was constructed based on a set of primitive Java arrays constructed and allocated in the Java heap space via JNI New<PrimitiveType>Array method call. In this version, the JVM garbage collector is aware of all memory allocated in the native code. However, in this approach, large array copy operations limited overall performance to approximately a 2X gain over the Saxon-B build time.

To further address the performance penalty imposed by copying large array values, a second version of the ASM Java object was constructed based on natively backed Direct Memory Byte Buffers [Hitchens 2002]. In this version the JVM garbage collector is unaware any native memory resources backing the Direct Memory Byte Buffers. Large JNI-based copy operations are avoided; however, system memory must be explicitly deallocated via a Java native method call. Using this approach, our preliminary results show an approximate total 2.5X gain over Saxon-B build time.

Compiler Technology

An important focus of our recent work is on the development of compiler technology to automatically generate the low-level SIMD code necessary to implement bit stream processing given suitable high-level specifications. This has several potential benefits. First, it can eliminate the tedious and error-prone programming of bit stream operations in terms of register-at-a-time SIMD operations. Second, compilation technology can automatically employ a variety of performance improvement techniques that are difficult to apply manually. These include algorithms for instruction scheduling and register allocation as well as optimization techniques for common subexpression expression elimination and register rematerialization among others. Third, compiler technology makes it easier to make changes to the low-level code for reasons of perfective or adaptive maintenance.

Beyond these reasons, compiler technology also offers the opportunity for retargetting the generation of code to accommodate different processor architectures and API requirements. Strategies for efficient parallel bit stream code can vary considerably depending on processor resources such as the number of registers available, the particular instruction set architecture supported, the size of L1 and L2 data caches, the number of available cores and so on. Separate implementation of custom code for each processor architecture would thus be likely to be prohibitively expensive, prone to errors and inconsistencies and difficult to maintain. Using compilation technology, however, the idea would be to implement a variety of processor-specific back-ends all using a common front end based on parallel bit streams.

Character Class Compiler

The first compiler component that we have implemented is a character class compiler, capable of generation all the bit stream logic necessary to produce a set of lexical item streams each corresponding to some particular set of characters to be recognized. By taking advantage of common patterns between characters within classes, and special optimization logic for recognizing character-class ranges, our existing compiler is able to generate well-optimized code for complex sets of character classes involving numbers of special characters as well as characters within specific sets of ranges.

Regular Expression Compilation

Based on the character class compiler, we are currently investigating the construction of a regular expression compiler that can implement bit-stream based parallel regular-expression matching similar to that describe previously for parallel parsing by bistream addition. This compiler works with the assumption that bitstream regular-expression definitions are deterministic; no backtracking is permitted with the parallel bit stream representation. In XML applications, this compiler is primarily intended to enforce regular-expression constraints on string datatype specifications found in XML schema.

Unbounded Bit Stream Compilation

The Catalog of XML Bit Streams presented earlier consist of a set of abstract, unbounded bit streams, each in one-to-one correspondence with input bytes of a text file. Determining how these bit streams are implemented using fixed-width SIMD registers, and possibly processed in fixed-length buffers that represent some multiple of the register width is a source of considerable programming complexity. The general goal of our compilation strategy in this case is to allow operations to be programmed in terms of unbounded bit streams and then automatically reduced to efficient low-level code with the application of a systematic code generation strategy for handling block and buffer boundary crossing. This is work currently in progress.


Parallel bit stream technology offers the opportunity to dramatically speed up the core XML processing components used to implement virtually any XML API. Character validation and transcoding, whitespace processing, and parsing up to including the full validation of tag syntax can be handled fully in parallel using bit stream methods. Bit streams to mark the positions of all element names, attribute names and attribute values can also be produced, followed by fast bit scan operations to generate position and length values. Beyond bit streams, byte-oriented SIMD processing of names and numerals can also accelerate performance beyond sequential byte-at-a-time methods.

Advances in processor architecture are likely to further amplify the performance of parallel bit stream technology over traditional byte-at-a-time processing over the next decade. Improvements to SIMD register width, register complement and operation format can all result in further gains. New SIMD instruction set features such as inductive doubling support, parallel extract and deposit instructions, bit interleaving and scatter/gather capabilities should also result in significant speed-ups. Leveraging the intraregister parallelism of parallel bit stream technology within SIMD registers to take of intrachip parallelism on multicore processors should accelerate processing further.

Technology transfer using a patent-based open-source business model is a further goal of our work with a view to widespread deployment of parallel bit stream technology in XML processing stacks implementing a variety of APIs. The feasibility of substantial performance improvement in replacement of technology implementing existing APIs has been demonstrated even in complex software architectures involving delivery of performance benefits across the JNI boundary. We are seeking to accelerate these deployment efforts both through the development of compiler technology to reliably apply these methods to a variety of architectures as well as to identify interested collaborators using open-source or commercial models.


This work is supported in part by research grants and scholarships from the Natural Sciences and Engineering Research Council of Canada, the Mathematics of Information Technology and Complex Systems Network and the British Columbia Innovation Council.

We thank our colleague Dan Lin (Linda) for her work in high-performance symbol table processing.


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Rob Cameron

Professor of Computing Science

Simon Fraser University

Dr. Rob Cameron is Professor and Director of Computing Science at Simon Fraser University. With a broad spectrum of research interests related to programming languages, software engineering and sociotechnical design of public computing infrastructure, he has recently been focusing on high performance text processing using parallel bit stream technology and its applications to XML. He is also a patentleft evangelist, advocating university-based technology transfer models dedicated to free use in open source.

Ken Herdy

Graduate Student, School of Computing Science

Simon Fraser University

Ken Herdy completed an Advanced Diploma of Technology in Geographical Information Systems at the British Columbia Institute of Technology in 2003 and earned a Bachelor of Science in Computing Science with a Certificate in Spatial Information Systems at Simon Fraser University in 2005.

Ken is currently pursuing graduate studies in Computing Science at Simon Fraser University with industrial scholarship support from the Natural Sciences and Engineering Research Council of Canada, the Mathematics of Information Technology and Complex Systems NCE, and the BC Innovation Council. His research focus is an analysis of the principal techniques that may be used to improve XML processing performance in the context of the Geography Markup Language (GML).

Ehsan Amiri

Graduate Student, School of Computing Science

Simon Fraser University

Ehsan Amiri is a PhD student of Computer Science at Simon Fraser University. Before that he studied at Sharif University of Technology, Tehran, Iran. While his graduate research has been focused on theoretical problems like fingerprinting, Ehsan has worked on some software projects like development of a multi-node firewall as well. More recently he has been developing compiler technology for automatic generation of bit stream processing code.