Patent Application: US-23835108-A

Abstract:
a language for semi - structured documents , xml has emerged as the core of the web services architecture , and is playing crucial roles in messaging systems , databases , and document processing . however , the processing of xml documents has a reputation for poor performance , and a number of optimizations have been developed to address this performance problem from different perspectives , none of which have been entirely satisfactory . parallel xml parsing leverages the growing prevalence of multicore architectures in all sectors of the computer market , and yields significant performance improvements . the design consists of an initial preparsing phase to determine the structure of the xml document , followed by a full , parallel parse . the results of the preparsing phase are used to help partition the xml document for data parallel processing . the parallel parsing phase is , for example , a modification of the libxml2 xml parser , which demonstrates that the approach applies to real - world , production quality parsers . empirical study shows the parallel xml parsing algorithm can improve the xml parsing performance significantly and scales well .

Description:
any kind of parsing is based on some kind of machine abstraction . the problems of an arbitrary division scheme arise from a lack of information about the state of the parsing machine at the beginning of each chunk . without this state , the machine does not know how to start parsing the chunk . unfortunately , the full state of the parser after the nth character cannot be provided without first considering each of the preceding n − 1 characters . this thus leads us to the parallel xml parsing ( pxp ) approach provided herein . first , an initial pass is used to determine the logical tree structure of an xml document . this structure is then used to divide the xml document such the divisions between the chunks occur at well - defined points in the xml grammar . this provides enough context so that each chunk can be parsed starting from an unambiguous state . this seems counterproductive at first glance , since the primary purpose of xml parsing is to build a tree - structured data model ( i . e ., xml infoset ) from the xml document . however the tree structure needed to guide the parallel parsing can be significantly smaller and simpler than that ultimately generated by a normal xml parser , and does not need to include all the information in the xml infoset data model . this simple tree structure , specifically designed for xml data decomposition , is called the “ skeleton ” of the xml document . to distinguish from the actual xml parsing , the procedure to parse and generate the skeleton from the xml document is called “ preparsing ”. once the preparsing is complete and the logical tree structure of the xml document known , the document can be divided into chunks , which are preferably balanced with respect to anticipated parsing duration , and then launch multiple threads to parse the chunks in parallel . consequently , this parallelism can significantly improve performance . this architecture is schematically shown in fig1 . for simplicity and performance , pxp currently maps the entire document into memory with the mmap ( ) system call . nothing precludes this general approach from working on streamed documents , or documents too large to fit into memory , but the design and implementation may be significantly more complex . the goal of preparsing is to determine the tree structure of the xml document so that it can be used to guide the data - parallel , full parsing . conceptually the xml infoset represents the tree structure of the xml document . however since only internal nodes ( i . e ., the element item ) determine the topology of the tree , which is meaningful for xml data decomposition , those leaf nodes in the xml infoset , such as attribute information items , comment information items , and even character information items , can be ignored by the skeleton . further the element tag names are also ignored by the skeleton since they don &# 39 ; t affect the topology of the tree at all . as shown in the fig2 , the skeleton essentially is a tree of unnamed nodes , isomorphic to the original xml document , and constructed from all starttag / end - tag pairs . to facilitate the xml data decomposition , the skeleton records the location of the start tag and end tag of each element , the parent - child relationships , and the number of children of every element . well - formed xml is not a regular language [ 4 ], and it cannot be parsed by a finite - state automaton , but rather requires at least a push - down automaton . so even determining the fundamental structure of the xml document , just for preparsing , requires executing a push - down automaton . however since preparsing is an additional processing step for parallel parsing , it is an additional overhead not normally incurred during xml parsing . furthermore , since it is sequential , it fundamentally limits the parallel parsing performance . however , it has been shown that in various test cases , the preparsing operation can build the skeleton at minimal cost , i . e ., short latency . according to the xml specification [ 5 ] a non - validating xml parser must determine whether or not a xml document is well - formed . a xml document is considered well - formed if it satisfies both requirements below . for simplicity , dtd and validating xml parsing may be omitted from a prototype system and method in accordance with the present invention . further , dtd is being replaced by the xml schema validation , which is usually a separate process after the xml parsing : 1 ) it conforms to the syntax production rules defined in the xml specification . however , since preparsing will be followed by a full - fledged xml parsing stage , the preparsing itself can ignore many errors . that is , for a well - formed xml document , the preparser must generate the correct result , but for an ill - formed xml document , the preparser does not need to detect any errors . thus , the preparser only detects weak conformance to the xml specification , and hence is simpler to implement and optimize than a fully validating preparser . as the skeleton only contains the location of the element nodes in the xml document , preparsing only needs to consider the element tag pairs , and can ignore other syntactic units and production rules for such as comments , character data , and attributes . consequently , the preparsing has a much simpler set of production rules compared to standard xml . for example the production rule of the start tag in xml 1 . 0 is defined as : because preparsing can ignore attribute and attvalue , and even the entire name production rule , the syntax could seemingly be simplified to just : however the above simplified production rule is incorrect due to ambiguity , because attvalue allows the & gt ; character by its production rule , which , if it appears , will cause the preparser to misidentify the location of the actual right angle bracket of the tag . therefore , the correct rules are : with same concern of possible ambiguity , the pi , comment , and cdata elements should be preserved in the preparsing production rules set because they are allowed to contained any string , including the & lt ; character , which would otherwise cause the preparser to misidentify the location of the end tag . the rest of production rules of standard xml are ignored by the preparsing . the simplified preparsing syntax results in a much simpler parsing automaton ( fig3 ), which only requires six major states , than the one needed by complete xml parsing . predictably , the preparsing automaton runs much faster than the general xml parsing automaton . in addition to the simplified syntax preparsing also benefits from omitting other well - formedness constraints . usually in order to check the well - formedness constraints , a general xml parser will perform a number of additional comparisons , transformations , sorting , and buffering , all of which can result in significant performance bottlenecks . for instance , the fundamental well - formedness constraint is that the name in the end - tag of an element must match the name in the starttag . to check this constraint , the general xml parser might push the start tag name onto a stack whenever a start tag is encountered , and pop the stack to match the name of the end tag . the preparser , however , treats the xml document as a sequence of unnamed open and close tag pairs . therefore , it can merely increment the top pointer of the stack for any start tag , and decrement for any end tag . finally , if the top pointer points to the bottom of the stack , the preparser considers the xml document to be correct without an expensive string comparison . another well - formedness constraint example is that the attribute name must not appear more than once in the same start - tag . to verify that , a full xml parser must perform an expensive uniqueness test , which is not required for preparsing . finally , preparsing obviously does not need to resolve namespace prefixes , since it completely ignores the tag name . however , a full xml parser supporting namespaces , requires expensive lookup operations to resolve namespace prefixes . the only constraint the preparsing requires is that the open tag has to be paired with a close tag . a simple stack is adopted for this checking , and the skeleton nodes are generated as the result of pushing and popping of the stack . another important source of performance advantages of preparsing compared to full parsing is that the skeleton is much lighter - weight than the dom structure . thus , preparsing is able to generate the skeleton substantially faster than full xml parsing is able to generate the dom . when compared to sax , the preparser benefits from avoiding callbacks . during the parallel parsing phase , the structural information in the skeleton is used to divide the document into chunks , each of which contains a forest of subtrees of the xml document . each chunk is parsed by a thread . for any parallel data technique to be effective , load - balancing must be used to prevent idle threads . ideally , the document may be divided into chunks such that there is one chunk for each thread and such that each chunk takes exactly the same amount of time to parse . depend on when and how the partitioning is performed , two strategies are available : static partitioning and dynamic partitioning . in a static partitioning scheme , a tree can be statically partitioned into several equally - sized subparts by using a graph partitioning tool ( e . g ., metis [ 6 ]), which can divide the graph / tree into n equally sized parts . the advantage of static partitioning is it can generate a very well - balanced load for every thread , thus leading to good parallelism . however since the static partitioning occurs before the actual xml parsing , it knows little about the parsing context ( e . g ., namespace declarations ). in other words , cuts made by the static partitioning will create following problems : 1 ) the characters of the xml document corresponding to the subgraph may no longer be contiguous . metis will create connected subgraphs , but a connected subgraph of the logical tree structure does not necessarily correspond to a contiguous sequence of characters in the xml document . in order to parse the resulting characters , either a contiguous sequence is reconstructed by memory copying , or the xml parser modified to handle non - contiguous character sequences . 2 ) the namespace scope may be split between subgraphs , which means a namespace prefix may be used in one subgraph , but defined in another . these inter - chunk references will create strong memory and synchronization dependencies between threads , which will degrade performance . the static partitioning algorithm must be executed sequentially before the parallel parsing , thus the performance gained by the parallelism may be offset by the cost of the static partitioning algorithm , which usually is not trivial . which are responsible for the bulk of most large xml documents , static partitioning is able to provide good parallelism . that is because a linear array can easily be divided into equal sized ranges ( i . e ., subgraphs ) without an expensive graph partitioning step . the division is based on the left to right order , so every range is contiguous in the xml document . the static pxp algorithm has been developed , which is a simple static partitioning and parallel parsing algorithm capable of parsing xml document with array structures . this serves to provide a baseline for comparison of more complex techniques . conveniently , a function from libxml2 [ 1 ] may be leveraged , which is a widely - used and efficient xml parsing library written in c , to perform the parsing . this function can parse a “ well - balanced chunk ” of an xml document within the context ( dtd , namespaces , etc .) of the given node . a well - balanced chunk is defined as any valid content allowed by the xml grammar . since the regions generated by the static partitioning are well - balanced , the above function may be used to parse each chunk . any element range generated by static array partitioning is a well balanced chunk . then the static pxp algorithm consists of the following steps : 1 ) construct a faked xml document in memory containing just an empty root element by copying the open / close tag pair of the root element from the original xml document , since the size of the root element is assumed to be much smaller than the whole document , the cost of any memory operations used by this step are acceptable . 2 ) call the libxml2 function xmlparsememory ( ) to parse the faked xml document , thus obtaining the root xml node . this node contains the namespace declarations required by its children , and will be treated as the context for the following parse of the ranges of the array . 3 ) the number of elements in each chunk is calculated by simply dividing the total number of elements in the array , which was calculated during the preparsing stage , by the number of available threads , so that every thread has a balanced work load . the start positions and data length of the chunk can be inferred from the location information of its first element and the last element . 4 ) create a thread to parse each chunk in parallel . each thread invokes xmlparseinnodecontext ( ) to parse and build the dom structure . 5 ) finally the parsed results of each thread will be spliced back under the root node . in summary , the static partitioning strategy is somewhat impractical for xml documents with irregular tree structures , due to strong dependencies between the different processing steps . however for those xml documents containing an array , it provides an upper bound on the performance gain of parallel parsing , and is useful for evaluation of other parallel parsing approaches as the guideline . a graph can be statically partitioned into a set of subgraphs using a number of different techniques [ 21 ]. a natural partitioning for xml is as a set of subtrees . the set of connected nodes above the subtrees is called the top tree , as shown in fig3 ( b ). each subtree is considered a task . a preferred static partitioning algorithm maintains a list of these tasks . at each iteration , the largest task is removed from the list , and the root of the corresponding subtree is moved to the top tree . each child of the subtree then forms a new subtree , and is placed onto the list as a new task . using the skeleton , the static partitioning scheme generates the set of subtrees starting from the root . the root node is first parsed , and the top tree initialized with it . all the immediate subtrees are then added below the root to the task list . the process then proceeds recursively as follows . at every iteration , the largest task is removed from the list , parse the root of the corresponding subtree and move it to the top tree . the generated subtrees which were under this node are then added back to the task list . a priority queue is used to efficiently maintain the largest task in the list . the effect is to grow the top tree down as subtasks are recursively created and new dom nodes added to the bottom of the top tree . note that the top tree consists of actual dom nodes , while the skeleton is the abbreviated structure in a concise form . when a dom node is added to the bottom of the top tree , the child nodes have not yet been created , but are merely “ logical ” nodes , which exist only in so far as they are in the skeleton . however , the dom nodes in the top tree need to be created by parsing the corresponding xml with a full parser , which creates a problem . because xml includes all descendants of a node within the lexical range of the node , as defined by the start - and end - tags , a parser cannot be used directly on the original text to generate a single , childless dom node . parsing the original text would also generate all the children nodes , leaving no work to be done during the actual parallel parsing stage . as a workaround to this lexical problem , a duplicate is created , but childless element in a temporary string buffer , and parse this duplicate element to generate a childless dom node . the duplicate element is created simply by concatenating the start - and end - tags of the original element , and omitting all of the content . for example , if the original element were & lt ; a & gt ;& lt ; c1 & gt ; . . . & lt ;/ c1 & gt ;& lt ; c2 /& gt ;& lt ;/ a & gt ;, then the temporary buffer would contain & lt ; a & gt ;& lt ;/ a & gt ;. this procedure could be expensive if the top tree is large , but in practice it has been found that most large documents are relatively shallow , and thus the top tree is small compared to the total size . the “ size ” of a task should correspond to how much time it will take to complete . for example , the number of characters in the corresponding xml chunk may be used an estimate of the processing time . it has been found so far that this works well , but a more sophisticated measure may be needed for some documents . to prevent too many small subtree tasks from being generated , and thus increasing overhead , recursion is terminated when the number of tasks is greater than a preset limit , for example , 100 . when such a limit is reached , there will usually be enough tasks such that they can be assigned to threads in a well - balanced manner . as new subtasks are recursively created , and the top tree grows down , new dom nodes are repeatedly added to the top tree . below any node in the top tree , the left - to - right ordering of its children will correspond to the order in which they were added to that node . in a straightforward , but incorrect , implementation , this order would correspond to the order that the corresponding subtrees were removed from the task list , which would not correspond to the actual left - to - right ordering of the elements in the xml document . thus , to preserve the left - to - right ordering , placeholder children are created between the bottom of the top tree and the subtrees below it . these placeholders are added to the parent node immediately when it is added to the top tree , and thus the left - to - right ordering is known and can be preserved at that time . each new subtree task is created with a pointer to its corresponding placeholder . these placeholders are removed as the top tree grows down , so that only the leaves of the top tree have placeholders . even these are removed after the parallel parsing is complete , as described below . the entire process is diagrammed in fig5 . the placeholder nodes also serve to isolate concurrent operations from one another . once subtree partitioning is complete , each subtree task will be executed by a separate thread . these threads will complete at different times , however , so if a thread directly adds the root of a subtree to the parent node , this will result in different left - to - right orderings depending on the completion order . the placeholder nodes avoid this problem because libxml2 can be used to directly replace the placeholder nodes with the actual subtree root nodes , in place , as shown in fig4 . after finalization of the task partitioning , a logical three level structure connecting top tree , placeholders , and tasks together is provided as shown in fig3 ( c ). the nodes in the top tree are fully constructed , permanent dom nodes ; the nodes in the placeholder level are temporary dom nodes used to connect the tasks to the proper left - right position within the permanent dom nodes ; and the nodes below this are logical nodes that currently only exist in the skeleton . xml documents consist of one or more long sequences of child elements . the sequences may have similar , or even identical types of elements . these are called sequences arrays . the subtree - based task partitioning scheme is not suitable for arrays , which may contain a large number of child nodes . adding all of these to the task list would be prohibitively inefficient . therefore , a simple heuristic may be used to recognize arrays in the skeleton , and handle them differently , which also improves load balancing . the heuristic treats a node as an array if the number of children is greater than some limit . during the traversal , the number of children is checked to determine if it is greater than this limit . if so , the children are treated as an array . the limit may , for example , be based on the number of threads , and is preferably set to 20 times the number of threads . once the current node is identified as an array , its elements are divided into equal - sized , continuous ranges . the size is chosen such that the number of ranges in the array is equal to the number of threads . each range is treated as a task . this differs from the subtree tasks , in that each task is now a forest rather than a subtree . these tasks are added to a fifo queue as they are generated , which is used during task assignment as described below . for subtree tasks , one node is created as the placeholder for the entire subtree . when the task finishes , the placeholder is simply replaced with the actual dom node . for array tasks , a separate placeholder must be created for each range . this is because otherwise , each thread would attempt to add the child nodes in its range concurrently to the parent node , resulting in race conditions . thus , a range placeholder is created for each array task , to isolate the concurrent operations of each thread from one another . when a thread finishes a subtree task , it can immediately replace the placeholder node with the actual root node of the subtree . when a thread finishes an array task , however , the result is a forest of subtrees that must be added to the actual array element , which is in the position of the parent of the range placeholder ( one level higher ), rather than in the position of the range placeholder itself . this operation cannot occur in parallel , because otherwise multiple threads would attempt to add child nodes at the same time . thus , an additional table is maintained for each array to record the proper ordering for the child range placeholders , and traverse this array sequentially after the end of the parallel parsing stage . fig1 a shows an xml document with two large arrays , its top tree with placeholders and task partitioning are shown in fig1 b and 12c . rph means range placeholder , ph means a subtree task placeholder . once the xml document has been partitioned into tasks , the tasks must be assigned to threads in a balanced manner , which is the goal of the task assignment stage . there are two sets of tasks , the subtree tasks and the array tasks . first , the array tasks in the fifo queue are assigned to threads in a round - robin fashion . this is because for most large xml documents the arrays are the largest part and also the most easily balanced part . as the tasks are assigned , the current workload of each thread is tracked . because the range sizes are carefully chosen , each thread will be guaranteed to have exactly one array task per array . also , the fifo queue will maintain the order of the ranges on each array , which will be used in the post - processing stage . after the array tasks have been assigned , the subtree tasks are then assigned to the threads . each subtree task is assigned to the thread that currently has the least workload . note that each thread &# 39 ; s task set is maintained by using a private queue . this eliminates contention during the actual parsing phase . in contrast with static partitioning , the dynamic partitioning strategy partitions the xml document and generates the subtasks during the actual xml parsing . after the preparser generates the skeleton , the tree structure is traversed in parallel to complete the parsing . whenever a node is visited by a thread , its corresponding serialization ( start tag ) will be parsed and the related dom node will be built . the parallel tree traversal is equivalent to a complete , parallel depth - first search ( dfs ) ( in which the desired node is not found ), which partitions the tree dynamically and searches for a specific goal in parallel using multiple threads . after rao [ 7 ], dynamic partitioning consists of two phases : task partitioning ; and subtask distribution . task partitioning refers to how a thread splits its current task into subtasks when another thread needs work . a common strategy is node splitting [ 8 ], in which each of the n nodes spawned by a node in a tree are themselves given away as individual subtasks . however , for parallel xml parsing , node splitting may generate too many small tasks , since most of nodes represents a single leaf element in the xml document , thus increasing the communications cost . since xml is a depth - first , left - to - right serialization of a tree , a sequence of sibling element nodes in the skeleton corresponds to a contiguous chunk of the xml document . therefore , if each parsing task covers a sequence of sibling element nodes , this will maximize the size of each workload , with little communication cost . in dynamic partitioning , a simple but effective policy of splitting the workload in half is adopted , as shown in fig4 a and 4b . that is , the running thread splits the unparsed siblings of the current element node into two halves in the left - to - right order , whenever the partitioning is requested . subtask distribution refers to how and when subtasks are distributed for the donator thread to the requester thread . if work splitting is performed only when an idle processor requests for work , it is called requester initiated subtask distribution . in contrast if the generation of subtasks is independent of the work requests from idle processors , the scheme is referred to as a donator initiated subtask distribution scheme . for parallel xml parsing , it is preferred that the parsing thread will parse as much xml data as possible without any interruption , unless other threads are idle and asking for tasks , so as to achieve a better performance . also , any thread can be the donator or the requester . the requester initiated subtask distribution is a preferred partition strategy in the pxp . to implement parallel parsing with dynamic partitioning , libxml2 is employed . since dynamic partitioning requires the parser do the task partitioning and subtask generation during the parsing , however , the libxml2 xmlparseinnodecontext ( ) function cannot simply be applied as in the static partitioning scheme . instead , the xmlparseinnodecontext ( ) source code must be changed to integrate the dynamic partitioning and generation logic into the original parsing code . the actual modified function is xmlparsecontent ( ), which is invoked by xmlparseinnodecontext ( ) to parse the xml content . the modified algorithm is called “ dynamic pxp ” and its basic steps are : 1 ) create multiple threads , and assign the root node of skeleton as the initial parsing task to the first thread . other threads are idle . 2 ) when a thread is idle , it posts its request on a request queue , and waits for the request to be filled by some donator thread . 3 ) every thread , once it begins parsing , parses normally as libxml2 does , except when an open tag is being parsed . at that time , it checks the request queue for threads that need work . if such a requester thread exists , the thread splits the current workload ( i . e ., the unparsed sibling nodes ) into two regions . the first half is donated to the requester thread , and the thread resumes parsing at the beginning location of the second half region . since every skeleton node records the number of its children elements , as well as its location information , it is easy to figure out the begin location and data length of the subtask . also to avoid excessively small tasks , the user can set a threshold to prevent task partitioning if the remaining work is less than the threshold . 4 ) once the requester thread obtains the parsing task , it begins the parsing at the beginning location of the donated subtask . due to the dynamic nature , the donator is able to pass its current parsing context ( e . g ., the namespace declarations ) to the requester as the requester &# 39 ; s initial parsing context , which will in turn make a clone of the parsing context for itself before parsing to avoid the synchronization cost . also the donator will create a dummy node as the “ placeholder ” for the parsing task , and the subtrees generated by the requester will be inserted under the placeholder , and once the parsing task is completed , the placeholder will be spliced within the entire dom tree . in summary , dynamic partitioning load - balances during the parsing , and it can be applied to any irregular tree structure without the need of the extra partitioning algorithm . however , the dynamic nature incurs a synchronization and communication cost among the threads , which is not needed by the static partitioning scheme . experiments were performed to measure the performance of the preparsing , and then experiments were performed to measure the performance improvement and the scalability of the parallel xml parsing ( static and dynamic partition ) algorithm over the different xml documents . the experiments were executed on general purpose computer which has two 2 dual - core amd opteron processors and 4 gb of ram , running a linux 2 . 6 . 9 operating system . every test was run five times to get the average time and the measurement of the first time is discarded , so as to measure performance with the file data already cached , rather than being read from disk . the programs are compiled by g ++ 3 . 4 . 5 with the option - o3 . the libxml2 library employed is version 2 . 6 . 16 . during initial experiments , poor speedup was observed during a number of tests that presumably should have performed well . the was attributed to lock contention in malloc ( ). to avoid this , a simple , thread - optimized allocator was written and deployed around malloc ( ). this allocator maintains a separate pool of memory for each thread . thus , as long as the allocation request can be satisfied from this pool , no locks need to be acquired . to fill the pool initially , the test is run once , then all memory is freed , returning it to each pool . the allocator is intended simply to avoid lock contention . a production allocator would use other techniques to reduce lock contention . one possibility is to simply use a two - stage technique , where large chunks of memory are obtained from a global pool , and then managed individually for each thread in a thread - local pool . preparsing generates the skeleton which is necessary for pxp . however , this is an additional step compared to normal xml parsing , which needs to be performed sequentially before the actual parallel parsing . thus , to help determine whether or not this cost is acceptable , and understand the overall pxp performance , preparsing time was measured and compared to full libxml2 parsing . since preparsing linearly traverses the xml document without backtracking or other bookkeeping , the time complexity is linear in the size of the document , and independent of the structural complexity of the document . the preparsing test was designed to maximize the performance of a full sequential parser , and used a simple array of elements which varied in size . the test document is shown in the appendix . first , elements in the array were varied to increase document size . then for the comparison , the costs of two widely - used parsing methods were measured : building dom with libxml2 , and parsing with the sax implementation in libxml2 . in addition , for the libxml2 sax implementation , empty callback routines were used . thus , libxml2 sax is expected to be extremely fast . the results are shown in fig5 . according to fig5 , it can be sees that preparsing is nearly 12 times faster than sequential parsing with libxml2 to build dom . even for libxml2 sax parsing , preparsing is over 6 times faster . even though the preparser builds a tree , the tree is simple and does not require expensive memory management . these results show that even the preparsing does not occupy much time , and the time left for actual parallel parsing is enough to result in significant speedup . speedup measures how well a parallel algorithm scales , and is important for evaluating the efficiency of parallel algorithms . it is calculated by dividing the sequential time by the parallel time . for these experiments , the sequential time refers to the time needed by libxml2 xmlparseinnodecontext ( ) to parse the whole xml document . to be consistent , static pxp , dynamic pxp , and the sequential program are all configured to use the thread - optimized memory allocator . each program is run five times and the timing result of the first time is discarded to warm the cache . the upper bound of the speedup that the pxp algorithms could achieve is first measured . to do that , a big xml document is used in the previous preparsing experiment is selected as the parsing test document . the array in the xml document has around 50 , 000 elements and every element includes up to 28 attributes and the size of the file is 35 mb . since the test document just contains a simple array structure , both static pxp and dynamic pxp algorithms may be applied on it . fig6 shows how the static / dynamic pxp algorithms scales with the number of threads when parsing this test document . the diagonal dashed line shows the theoretical ideal speedup . from the graph it can be see that when the number of threads is one or two , the speedups of the pxp is sublinear , but if the preparsing time is subtracted from the total time the speedups of static pxp is close to linear . this indicates the preparsing dominates the overhead , and the static pxp presents the upper bound of the parallel performance . the speedups of dynamic pxp are slightly lower than the ones of the static pxp , which indicates the cost of communication and synchronization starts to be a factor , but is relatively minor . when the number of threads is increased , the speedup of the pxp ( dynamic or static ) become less , because when the work load of every thread decreases , the overhead of the preparsing becomes more significant than before . also , the dynamic pxp obtains less speedup than the static pxp due to the increasing communication cost . furthermore , even the speedup of the static pxp , omitting the preparsing cost , starts to drop away from the theoretical limit . this may be because of shared memory or cache conflicts . unlike the static pxp , dynamic pxp is able to parse the xml documents with any tree shape . so to further study the performance improvement of dynamic pxp , the previous xml document was modified such that the big array structure assumed an irregular tree shape , which consists of a five top - level elements under the root , each with a randomly chosen number of children . each of these children is an element from the array of the first test , and so the total number of these child elements in the modified document is same as the one of the original document . the dynamic pxp on this modified xml document is compared against the dynamic pxp on the original array xml document . this comparison shows how the dynamic pxp scales for the xml documents with irregular shape or regular shape . from the results shown in fig7 , it can be seen that there is little difference between two xml documents , which imply that dynamic pxp ( and the task partitioning of dividing the remaining work in half ) is able to effectively handle the large xml file with an irregular shape . these tests did not actually further parse the element contents . in a typed parsing scenario , where schema or other information can be used to interpret the element content , even better scalability would be obtained . for example , if a large array of doubles including the ascii - to - double conversion are being parsed , each thread has an increased workload relative to the preparsing stage and other overheads , and thus speedup would be improved . the parallel xml parsing system and method according to the present invention has been shown to performs well for up to four cores , and is not thereby limited . an efficient parallel xml parsing scheme needs an effective data decomposition method , which implies a better understanding of the tree structure of the xml document . preparsing is designed to extract the minimal tree structure ( i . e ., skeleton ) from the xml document as quickly as possible . indeed , it is possible to communicate the preparsed data with the xml document ( analogous to an index ), which can be created at its source , or at an intermediate location , which is then provided as metadata which accompanies the xml document , which eliminates the preparsing inefficiency entirely , while only increasing the size of the xml document by a small amount , and imposing only a small burden on the author of the xml document . for example , the preparse information could be generated in an xml validator , which may be executed as a matter of course before the xml document is initially released . the key to the high performance of the preparsing is its highly simplified syntax as well as the obviation of full wellformedness constraints checking . aided by the skeleton , the algorithm can partition the xml document into chunks and parse them in parallel . depending upon when the document is partitioned , the static pxp and dynamic pxp algorithms are employed . the former is only for the xml documents with array structures and can give the best case benefit of parallelism , while the latter is applicable to any structures , but with some communication and synchronization cost . the experiments show the preparsing is much faster than full xml parsing ( either sax or dom ), and based on it the parallel parsing algorithms can speedup the parsing and dom building significantly and scales well . since the preparsing becomes the bottleneck as the number of threads increase , it may also be possible to parallelize that task , for further speedup . experiments were run on a sun fire t1000 machine , with 6 cores and 24 hardware threads ( cmt ). most large xml documents , particularly in scientific applications , are relatively broad rather than deep , also they typically contain one or two large arrays and the structure tends to be shallow . hence a large xml file was selected for analysis containing molecular information representing the typical structural shape of xml documents in scientific applications . this was based on xml documents obtained from the protein data bank [ 19 ]. it consists of two large arrays representing the molecule data as well as a couple elements for the molecule attributes . to obtain the different sizes , the molecule data part of the documents were repeated . every test was run ten times to get the average time and the measurement of the first time is discarded , so as to measure performance with the file data already cached , rather than being read from disk . the programs are compiled by sun workshop 5 . 2 cc with the option - o , and version 2 . 6 . 16 libxml2 library was used . tests employing straight libxml2 ( without parallelization ) also employ the special allocator designed for use with the parallelization scheme , since the results of straight libxml2 are better with this allocator than without it , and this provides a more consistent basis for comparison . the special allocator is intended simply to avoid lock contention , since the focus of the experiment is on the parsing itself . the implementation according to an embodiment of the current invention performs the stages as described herein , and thus the time for processing of each stage can be measured . the sequential stages include preparsing , task partitioning , and post - parsing , and the parallel stages comprise the parallel parsing , which is done by all the threads in parallel . the performance breakdown experiment is done on the aforementioned test xml document sized to 18 m bytes . the result is shown on fig1 , in which the different levels represents the percentage of the running time of each stage . between 2 and 32 threads were tested , but only even numbered results are shown on fig1 for clarity . the most immediate feature to note is that the preparsing is by far the most time consuming part of the sequential stage , and the static partitioning stage is not a significant limit to the parallelism . as a general trend up to 24 threads , the percentage of tome spent on the preparsing stage grows from 13 % to 53 %. percentage on task partitioning grows from 0 . 17 % to 0 . 71 %. percentage on post - processing grows from 0 . 16 % to 0 . 73 %. this means that as the number of threads increase , the sequential stages consume an increasing percentage of the total time , and obviously can cause the performance to degrade . meanwhile , the time cost of the parallel parsing stages drops from 87 % to 46 %. when the participating threads reach more than 24 , additional hardware threads are no longer available , thus requiring the os to start context switching threads . this causes the percentage of parallel parsing to increase suddenly from 24 threads &# 39 ; 46 % to 26 threads &# 39 ; 52 %, and then minor reduction at each increased measurement results until 32 threads &# 39 ; 48 %. as the number of threads increase , the post - processing stages increase from 2 threads &# 39 ; 0 . 16 % to 32 threads &# 39 ; 0 . 74 %. to show the benefit of the static approach on these kind of shallow structured xml documents , the static approach is compared with a dynamic approach as discussed above . referring to fig1 , four types of measurement were conducted on both the static parallel parsing approach and dynamic parallel parsing approach . the tests were conducted on the test xml document sized to 18 m bytes , and to better explore the cause of performance limitations on each approach , two graph lines are provided : one is the total speedup and the other is the speedup not counting the sequential stages . speedup in this case is computed against the time obtained by a pure libxml2 parse without any preparsing or other steps only needed for pxp . for the static approach , the sequential stages include preparsing , task partitioning and assignment , postprocessing ; and for the dynamic approach , the sequential stages include just preparsing . it appears that every six threads , the speedup will have a distinct drop . this is due to the fact that there are only six cores on the machine . every six threads , the number of hardware threads per core must increase . to better see the speedup degradation pattern , the efficiency graph as shown in fig1 was generated . the efficiency is defined as the speedup divided by the number of threads . with the efficiency graph , the performance degradation can then be clearly seen . since the dynamic load - balancing is obtained by runtime work load stealing , which incurs intensive communication and synchronization cost with an increasing number of threads , it is not scalable to the number of cores , as fig1 shows . without preparsing , it reaches the maximum speedup with 16 threads , which is 6 . 1 . after that point , it drops to the lowest value of 0 . 47 at 29 threads and continues to below one time speedup with the increase of the threads number . and for efficiency , the dynamic approach also dropped to 0 . 02 in the 32 threads case . these two figures show that though the dynamic approach has proved to be more flexible and suitable for parallel xml parsing on complex xml documents , for large xml documents with a simple array - like structure , as commonly seen in many types of xml documents , the static approach is more scalable than the dynamic approach . note that although more sophisticated dynamic load - balancing technologies exist to improve the scalability , for those shallow structured documents the static scheme should be hard to challenge . to better understand the pros and cons between the static approach and the dynamic approach , tests were also performed on complex structured xml . a deep and irregular structured xml document was generated with the size of 19 m bytes . the tests results are shown in fig1 , however , indicate that in such case the speedup of static approach is much worse than the dynamic approach . the reason is that for complex xml documents it is harder to statically achieve a balanced load . thus , with the number of threads increased , load imbalance becomes more serious . in addition , the near - 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