Patent Grant
8667329
This disclosure relates to processing transactions in graph-based applications.
Complex computations can often be expressed as a data flow through a directed graph, with components of the computation being associated with the vertices of the graph and data flows between the components corresponding to links (arcs, edges) of the graph. A system that implements such graph-based computations is described in U.S. Pat. No. 5,966,072, EXECUTING COMPUTATIONS EXPRESSED AS GRAPHS. One approach to executing a graph-based computation is to execute a number of processes, each associated with a different vertex of the graph, and to establish communication paths between the processes according to the links of the graph. For example, the communication paths can use TCP/IP or UNIX domain sockets, or use shared memory to pass data between the processes.
In one aspect, in general, a computer-implemented method includes preparing a graph-based computation having a plurality of nodes representing graph components connected by one or more links representing data flows between the graph components by identifying at least a first set of graph components to process a plurality of transactions in the computation; and associating with a first transaction a first group of one or more data records and data operations corresponding to the data records, and associating with a second transaction a second group of one or more data records and data operations corresponding to the data records. The method includes executing the graph-based computation to process at least the first and second transactions, including delaying execution by the first set of graph components of a data operation corresponding to an initial data record of the second group until after execution by the first set of graph components of a data operation corresponding to a final data record of the first group.
The aspect can include one or more of the following features.
Executing the graph-based computation can include executing the first and second transactions as a first batch of transactions by processing the data records and data operations corresponding to the first and second transactions; and upon detecting a failure, rolling back the first batch of transactions by rolling back all processed data records and data operations corresponding to the processed transactions.
The method can also include specifying a first graph component having at least an output port as a beginning of a data flow for the first t of graph components; and specifying a second graph component having at least an input port as an end of the data flow for the first set of graph components.
The method can also include dynamically invoking the first set of graph components to process the one or more transactions.
The method can also include, upon successfully processing substantially all transactions in the first batch of transactions, committing the first batch of transactions. Committing the first batch of transactions can include committing all the data operations corresponding to each one of the transactions in the first batch of transactions at substantially the same time.
Detecting a failure can include, for example, detecting a failure of any of the data operations corresponding to each one of the data records in a transaction of the first batch of transactions.
The method can also include, after detecting the failure, re-processing, as a second different batch of transactions, unprocessed transactions of the first batch of transactions and the processed transactions that were not committed during processing of the first batch of transactions.
The method can also include, after detecting the failure, re-processing, as a series of discrete batches, each one of the processed and unprocessed transactions associated with the first batch of transactions.
The method can also include, upon detecting the failure, identifying one or more transactions that failed as failed transactions, re-processing, as a second different batch, the first batch of transactions excluding the failed transactions, and re-processing, as third batch, the failed transactions.
The method can also include, in the course of executing the first transaction, performing one or more data operations in a first database by a set of graph components belonging to the graph-based computation. The method can also include, in the course of executing the first transaction, establishing a first session with the first database, the first session being shared by one or more of a first set of graph components processing the first transaction.
A size of the first transactions can be specified by a user.
In another aspect, in general, a computer-readable medium stores a computer program including instructions for causing a computer to prepare a graph-based computation having a plurality of nodes representing graph components connected by one or more links representing data flows between the graph components, including identifying at least a first set of graph components to process a plurality of transactions in the computation, associating with a first transaction a first group of one or more data records and data operations corresponding to the data records, and associating with a second transaction a second group of one or more data records and data operations corresponding to the data records, and execute the graph-based computation to process at least the first and second transactions, including delaying execution by the first set of graph components of a data operation corresponding to an initial data record of the second group until after execution by the first set of graph components of a data operation corresponding to a final data record of the first group.
In another aspect, in general, a computer system includes means for preparing a graph-based computation having a plurality of nodes representing graph components connected by one or more links representing data flows between the graph components, including identifying at least a first set of graph components to process a plurality of transactions in the computation, and associating with a first transaction a first group of one or more data records and data operations corresponding to the data records, and associating with a second transaction a second group of one or more data records and data operations corresponding to the data records; and means for executing the graph-based computation to process at least the first and second transactions, including delaying execution by the first set of graph components of a data operation corresponding to an initial data record of the second group until after execution by the first set of graph components of a data operation corresponding to a final data record of the first group.
The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.
Referring to
The runtime environment 106 may be hosted on one or more general-purpose computers under the control of a suitable operating system, such as the UNIX operating system. For example, the runtime environment 106 can include a multiple-node parallel computing environment including a configuration of computer systems using multiple central processing units (CPUs), either local (e.g., multiprocessor systems such as SMP computers), or locally distributed (e.g., multiple processors coupled as clusters or MPPs), or remotely, or remotely distributed (e.g., multiple processors coupled via LAN or WAN networks), or any combination thereof. Concurrent execution of multiple components in a graph provides one form of parallelism. Additional parallelism can be achieved by distributing different components of a graph to different computing nodes (e.g., different CPUs). Elements of a graph (e.g., data sets, components, and flows) can be explicitly or implicitly replicated to introduce parallelism into the runtime environment 106.
A user 103 can interact with the runtime environment 106, for example, using a command line or graphical interface. The runtime environment 106 includes a pre-execution module 110 to read the stored graph data structure specifying a given dataflow graph, and to allocate and configure computing resources such as processes for performing the computations of a component (e.g., a process or thread of execution within the host operating system).
The runtime environment 106 also includes an execution module 112 to schedule and control execution of the processes assigned to a dataflow graph by the pre-execution module 110. The execution module 112 can interact with external computing resources coupled to the system 100 such as database engines, data storage, or other modules that are accessed during processing associated with the graph components.
After execution of a dataflow graph, or at predetermined intervals during execution, a reporting module 114 provides information characterizing a given computation, such as statistics associated with the individual components of the dataflow graph. Some of the information reported by the reporting module 114 is obtained from outputs generated by the dataflow graph. Some of the information reported is obtained by monitoring execution of the dataflow graph.
A runtime monitoring module (RMM) 116 monitors execution of one or more processes assigned to a dataflow graph and provides information to the reporting module 114. The information includes, for example, central processing unit (CPU) time devoted to running each component, or amount of data processed by each component.
Referring to
The flow of records from the output of an upstream component (e.g., 204A) to a downstream component (e.g., 204B or 240) is generally allowed to occur over any of the dataflow links 245 concurrently. This allows different components 204 in the dataflow graph 200 to perform tasks concurrently on different records, which provides a form of pipeline parallelism for the overall computation being performed by the dataflow graph 200. In other words, a given part of the computation is performed by upstream components 204 on a given set of records in parallel with a different part of the computation being performed by downstream components 204 on a different set of records.
The dataflow graph 200 may run on parallel processing systems and make changes to multiple external collections of data (such as files and databases) by a combination of file manipulation operations (such as creating, deleting and renaming) and data manipulation operations (such as reading and writing). One such external collection of data is the database system 209. The components 204 of the dataflow graph 200 are configured to access the database system 209. In this regard, the data records being processed by the graph 200 are associated with one or more data operations to be performed on the database system 209.
In an implementation, the components 204 can be configured to provide transactional properties, as described in more detail below. By way of an example, referring to
If the first data record 312a is processed independently of the second data record 312b, then the independent commits to the database 209 would result in the database possibly updating an account's balance before the customer's information is validated. As such, the data contained in the database system 209 would not be in a desired form. To address this situation, the data records 312 and associated operations that need to be committed or failed (i.e., rolled back) together as a single unit are grouped as a transaction that is committed as a whole only after all the records 312 and operations corresponding to the records 312 are processed. The records 312 that are grouped together to be processed as part of the same transaction can be called a “unit of work.” Further, different transactions can optionally be grouped together into a batch of transactions that are processed as a unit such that the transactions in the batch are either committed together or the entire batch is rolled back. The size of a batch of transactions, i.e., the number of transactions in the batch, can be specified by a user, or automatically determined by the pre-execution module 110 (
In this example, the components 306a-c are each configured to access, e.g., receive, modify, and/or delete, data in the database system 209. A series of data records 312 are received as input by the “read” component 315. When processing of the data records 312 and associated data operations result in permanent changes to the database system 209, the changes are said to be “committed.” Such committed changes are made visible to all processes within the computing system 100. In this regard, the commit operations are carried out for each transaction corresponding to data records 312 and associated operations to make changes effected by the data records 312 and associated data operation permanent in the database system 209. If a failure occurs, the failed transaction including the data records 312 and associated data operations can be rolled back (i.e., the earlier state of the database system 209 before the operation was performed can be restored) as a single unit, and the data records 312 and associated operations can be either discarded, or re-processed after addressing the reason for failure. Failure can occur for a variety of reasons. For example, the failure can be a system failure, e.g., an error in memory allocation or a conflict in writing to a memory space. The failure could also be a failure in the underlying data operation, e.g., attempting to withdraw funds from a depleted account. In such a scenario, the transaction including the data records 312 and associated data operations would fail.
In general, committing after processing every transaction including data records 312 and associated data operations can be expensive. In a typical commit operation, the database system 209 writes to a physical disk (e.g., a hard disk that stores data associated with the database system 209) every modification made by the data operation to the database system 209. In some examples, a log file (not shown) may also need to be updated thus increasing further the number of processing cycles involved in carrying out the commit operation. As such, a commit operation may require considerable resources and processing cycles. As such, the exemplary techniques described herein for grouping commit operations for a batch of multiple transactions reduce some of this expense and conserves resources.
As described above, processing an incoming flow of data records in units of work associated with separate transactions enables certain transactional properties (e.g., isolation, as described in more detail below) by suppressing the pipeline parallelism that would otherwise be present within a set of components handling operations for those transactions. Referring again to
In an implementation, the set 250 can be established through the use of one or more special components 240, 255 that delimit the beginning and end of the transaction processing for a given transaction. In some examples, the components 240, 255 can be specified by a designer (e.g., developer 101 of
In some implementations, at startup, the graph 200 can be analyzed to determine the components 204 in the set 250. The components 204 in the set 250 can be executed in a single process. In this approach, the pre-execution module 110 (
For the components 204 in a given set 250, the BT 240 and ET 255 are specified as described above. There can be one input to the BT 240, an optional output from one or more ETs 255, and other outputs from the components in the set 250. In some implementations, there can be components upstream of the BT 240 (e.g., component 204A) and downstream of the ET 255.
In an implementation, a component 204 that may potentially participate in processing a given transaction can use a “discovery” process to indicate to the BT 240 that they are part of the set 250. For example, components 204D-G are part of the set 250. Each component 204 that is folded into a single process can include a start-up routine that is called during start-up of the transaction. Each of the components 204D-G is assigned a parameter specifying a name of the set 250. During start up, the components 204D-G register themselves as components 204 assigned to the set 250 so that the BT 240 can know the components 204D-G involved in processing a given transaction.
In one example, a new transaction starts when the BT 240 receives a data record 201 while no other transaction is active. At the start of the transaction, the BT 240 instructs each component 204 and the ET 255 to begin the processing the transaction. Data records 201 associated with the transaction are passed between the components 204D-G as part of the data flow 245 in the transaction. At the end of processing the transaction the ET 255 enables the results of the transaction to be committed or aborted, as described above.
Further, in some implementations, one or more components 204D-G in the set 250 can share a connection to access the database system 209. As such, in the course of executing a transaction using the components 204D-G in the set 250, a session can be established with the database system 209 and shared by the components 240D-G in the set 250. In this manner, in the context of processing a batch of transactions, all data records and associated data operations relevant to the database system 209 can be processed within the same database session.
To improve reliability in the computation graph context, the TUs of the dataflow graph 200 are configured to implement transactions providing the “ACID” properties, i.e., the system 100 ensures “atomicity,” “consistency,” “isolation,” and “durability” with respect to the data records 201 and associated data operations for a given transaction. For example, to meet the atomicity property, the TU interacts with the database system 209 such that all operations associated with changes to the database system 209 performed within a single transaction are committed together. For example, in the example of
In addition to atomicity, the system 100 ensures that the data in the database 209 is in a consistent state when a transaction starts and ends. This ensures that the database 209 meets the “consistency” property. For example, in an application that transfers funds from one account to another, the consistency property ensures that the total value of funds in both the accounts is the same at the start and end of each transaction.
Further, the system 100 ensures an intermediate state of a transaction is invisible to other transactions. This ensures that the system 100 meets the “isolation” property. For example, in an application that transfers funds from one account to another, another transaction sees the transferred funds in one account or the other, but not in both.
Finally, after a transaction successfully completes, the system 100 ensures that changes to data persist and are not lost, even in the event of a system failure. This ensures that the system 100 meets the “durability” property. For example, in an application that transfers funds from one account to another, the durability property ensures that the changes made to each account will not be lost.
Techniques can be used to deal with failures during the processing of a transaction. If a failure occurs, partial changes may have been made to the database system 209, which render that data in the database system 209 unusable by the current application or other applications until corrected. Typically, this will happen if a record in the database system 209 has been modified, deleted, or created in the course of a computation. In parallel processing systems, the problem is intensified since the database system 209 will often be spread over many different computing systems and storage units (e.g., magnetic disks), making the work required to “roll back” the state of the data increase proportionately with the number of storage units.
To recover from such failures, it can be necessary to shut down the current (i.e., failed) application, and then either undo all changes made by the application since its start (a “full rollback”), or restore the state of the system to an intermediate “checkpoint” and restart execution from that point (a “partial rollback”).
As suggested above, a feature of the dataflow graph 200 is that the data records 201 and their corresponding data operations can be associated with multiple transactions. The data record 201 and corresponding data operations associated with a given transaction can either be committed (i.e., made permanent) or rolled back (i.e., undone) as a single unit.
Referring now to
In an implementation, the input data records 312 are grouped together into a batch of transactions 414a, 414b (generally 414) that each corresponds to a different group of records in a “unit of work” to be processed in the TU 402. The grouping of data records 312 into units of work for transactions 414 can be done in a variety of ways. For example, a user 103 can define a transaction size (i.e., a number of data records 312) for each transaction 414. In some examples, the pre-execution module 110 (
In an example, to provide isolation between different transactions, only one transaction, e.g., transaction 414a, is processed by the components 406a-d of the set 402 at a time. Accordingly, only after the last data record of the transaction 414a has been processed by the last component 406d can first data record of the transaction 414b be processed by the first component 406a.
In some implementations, commit operations are not performed after each transaction, but rather are delayed and performed for a batch of transactions at once. In the following example, a “batch” refers to a batch of multiple transactions and their corresponding units of work, each made up of records to be processed in those transactions. Referring now to
If a failure were to occur during processing of the transaction for unit of work 512c, the changes represented by the results 514a and 514b of the previously processed transactions would be rolled back. Input units of work 510a and 510b that correspond to these results 514a and 514b of the processed transactions are still available at the input of the set 502. In some implementations, when a failure occurs, the transactions corresponding to the units of work 510a-e can be re-processed by unbundling the batch 509. That is, each transaction corresponding to the units of work 510a-e, including associated data records and operations, in the batch 509 can be processed separately and changes to the database 209 based on one or more individual transactions can be committed before processing the next transaction. In some implementations, since the failed transaction can be identified, e.g., transaction corresponding to input unit of work 510c (or processed unit of work 512c), the transactions including associated data records and operations in the batch 509 can be re-processed as a new batch 509 without the failed transaction and associated data records (of input unit of work 510c) and operations. In some implementations, the failed transaction can be processed alone, and the rest of the transactions in the batch 509 can be divided into two or more smaller batches of transactions (not shown) and processed one after the other. For example, the transactions corresponding to units of work 510a and 510b can be processed as a first batch, and the transaction corresponding to units of work 510d and 510e can be processed as a second batch.
Referring now to
Data records and their corresponding data operations are associated with transactions in a batch of transactions to be processed by the TU. (Step 606) For example, a transaction 414 (
During processing of the transaction 414, a failure may occur. As described above, a data record 312 and its associated data operation can fail for a variety of reasons including, e.g., system failures and/or operational failures. Accordingly, while the transaction 414 is being processed, the transaction 414 can be checked for such failures, or other errors. (Step 618) If no failure is detected, then the results of processing the data records 312 and corresponding data operations associated with the transaction 414 are committed as a single unit. (Step 622) If a failure were to occur in the processing of the transaction 414, then any changes made to the corresponding databases (e.g., database 209 in
The techniques described above can be implemented using software for execution on a computer. For instance, the software forms procedures in one or more computer programs that execute on one or more programmed or programmable computer systems (which may be of various architectures such as distributed, client/server, or grid) each including at least one processor, at least one data storage system (including volatile and non-volatile memory and/or storage elements), at least one input device or port, and at least one output device or port. The software may form one or more modules of a larger program, for example, that provides other services related to the design and configuration of computation graphs. The nodes and elements of the graph can be implemented as data structures stored in a computer readable medium or other organized data conforming to a data model stored in a data repository.
The software may be provided on a storage medium, such as a CD-ROM, readable by a general or special purpose programmable computer or delivered (encoded in a propagated signal) over a communication medium of a network to the computer where it is executed. All of the functions may be performed on a special purpose computer, or using special-purpose hardware, such as coprocessors. The software may be implemented in a distributed manner in which different parts of the computation specified by the software are performed by different computers. Each such computer program is preferably stored on or downloaded to a storage media or device (e.g., solid state memory or media, or magnetic or optical media) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer system to perform the procedures described herein. The inventive system may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer system to operate in a specific and predefined manner to perform the functions described herein.
A number of embodiments of the invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. For example, some of the steps described above may be order independent, and thus can be performed in an order different from that described.
It is to be understood that the foregoing description is intended to illustrate and not to limit the scope of the invention, which is defined by the scope of the appended claims. For example, a number of the function steps described above may be performed in a different order without substantially affecting overall processing. Other embodiments are within the scope of the following claims.
This application claims priority to U.S. Application Ser. No. 61/245,862, filed on Sep. 25, 2009, incorporated herein by reference.
| Number | Name | Date | Kind |
|---|---|---|---|
| 3662343 | Goldstein et al. | May 1972 | A |
| 3662401 | Collins et al. | May 1972 | A |
| 4922418 | Dolecek | May 1990 | A |
| 4972314 | Getzinger et al. | Nov 1990 | A |
| 5127104 | Dennis | Jun 1992 | A |
| 5276899 | Neches | Jan 1994 | A |
| 5280619 | Wang | Jan 1994 | A |
| 5301336 | Kodosky | Apr 1994 | A |
| 5323452 | Dickman et al. | Jun 1994 | A |
| 5333319 | Silen | Jul 1994 | A |
| 5357632 | Pian et al. | Oct 1994 | A |
| 5495590 | Comfort et al. | Feb 1996 | A |
| 5630047 | Wang | May 1997 | A |
| 5692168 | McMahan | Nov 1997 | A |
| 5701400 | Amardo | Dec 1997 | A |
| 5712971 | Stanfill et al. | Jan 1998 | A |
| 5745778 | Alfieri | Apr 1998 | A |
| 5802267 | Shirakihara et al. | Sep 1998 | A |
| 5805462 | Poirot et al. | Sep 1998 | A |
| 5857204 | Lordi et al. | Jan 1999 | A |
| 5923832 | Shirakihara et al. | Jul 1999 | A |
| 5924095 | White | Jul 1999 | A |
| 5930794 | Linenbach et al. | Jul 1999 | A |
| 5933640 | Dion | Aug 1999 | A |
| 5966072 | Stanfill et al. | Oct 1999 | A |
| 5999729 | Tabloski, Jr. et al. | Dec 1999 | A |
| 6006242 | Poole et al. | Dec 1999 | A |
| 6012094 | Leymann et al. | Jan 2000 | A |
| 6014670 | Zamanian et al. | Jan 2000 | A |
| 6016516 | Horikiri | Jan 2000 | A |
| 6032158 | Mukhopadhhyay et al. | Feb 2000 | A |
| 6038558 | Powers et al. | Mar 2000 | A |
| 6044211 | Jain | Mar 2000 | A |
| 6044374 | Nesamoney et al. | Mar 2000 | A |
| 6044394 | Cadden et al. | Mar 2000 | A |
| 6088716 | Stanfill et al. | Jul 2000 | A |
| 6145017 | Ghaffari | Nov 2000 | A |
| 6173276 | Kant et al. | Jan 2001 | B1 |
| 6208345 | Sheard et al. | Mar 2001 | B1 |
| 6256637 | Venkatesh et al. | Jul 2001 | B1 |
| 6259988 | Galkowski et al. | Jul 2001 | B1 |
| 6272650 | Meyer et al. | Aug 2001 | B1 |
| 6301601 | Helland | Oct 2001 | B1 |
| 6314114 | Coyle et al. | Nov 2001 | B1 |
| 6324437 | Frankel et al. | Nov 2001 | B1 |
| 6330008 | Razdow et al. | Dec 2001 | B1 |
| 6339775 | Zamanian et al. | Jan 2002 | B1 |
| 6400996 | Hoffberg et al. | Jun 2002 | B1 |
| 6401216 | Meth et al. | Jun 2002 | B1 |
| 6437796 | Sowizral et al. | Aug 2002 | B2 |
| 6449711 | Week | Sep 2002 | B1 |
| 6480876 | Rehg et al. | Nov 2002 | B2 |
| 6496961 | Gupta et al. | Dec 2002 | B2 |
| 6538651 | Hayman et al. | Mar 2003 | B1 |
| 6584581 | Bay et al. | Jun 2003 | B1 |
| 6608628 | Ross et al. | Aug 2003 | B1 |
| 6611862 | Reisman | Aug 2003 | B2 |
| 6651234 | Gupta et al. | Nov 2003 | B2 |
| 6654907 | Stanfill et al. | Nov 2003 | B2 |
| 6658464 | Reisman | Dec 2003 | B2 |
| 6715145 | Bowman-Amuah | Mar 2004 | B1 |
| 6728879 | Atkinson | Apr 2004 | B1 |
| 6813761 | Das et al. | Nov 2004 | B1 |
| 6816825 | Ashar et al. | Nov 2004 | B1 |
| 6832369 | Kryka et al. | Dec 2004 | B1 |
| 6848100 | Wu et al. | Jan 2005 | B1 |
| 6879946 | Rong et al. | Apr 2005 | B2 |
| 7062483 | Ferrari et al. | Jun 2006 | B2 |
| 7082604 | Schneiderman | Jul 2006 | B2 |
| 7085426 | August | Aug 2006 | B2 |
| 7103597 | McGovern | Sep 2006 | B2 |
| 7103620 | Kunz et al. | Sep 2006 | B2 |
| 7130484 | August | Oct 2006 | B2 |
| 7137116 | Parkes et al. | Nov 2006 | B2 |
| 7164422 | Wholey et al. | Jan 2007 | B1 |
| 7165030 | Yi et al. | Jan 2007 | B2 |
| 7167850 | Stanfill | Jan 2007 | B2 |
| 7316001 | Gold et al. | Jan 2008 | B2 |
| 7356819 | Ricart et al. | Apr 2008 | B1 |
| 7398514 | Ulrich et al. | Jul 2008 | B2 |
| 7417645 | Beda et al. | Aug 2008 | B2 |
| 7457984 | Kutan et al. | Nov 2008 | B2 |
| 7467383 | Inchingolo et al. | Dec 2008 | B2 |
| 7505975 | Luo | Mar 2009 | B2 |
| 7577628 | Stanfill | Aug 2009 | B2 |
| 7636699 | Stanfill | Dec 2009 | B2 |
| 7716630 | Wholey et al. | May 2010 | B2 |
| 7756940 | Sagawa | Jul 2010 | B2 |
| 7840949 | Schumacher et al. | Nov 2010 | B2 |
| 7870556 | Wholey et al. | Jan 2011 | B2 |
| 7877350 | Stanfill et al. | Jan 2011 | B2 |
| 7979479 | Staebler et al. | Jul 2011 | B2 |
| 8566641 | Douros et al. | Oct 2013 | B2 |
| 20010055019 | Sowizral et al. | Dec 2001 | A1 |
| 20020080181 | Razdow et al. | Jun 2002 | A1 |
| 20020087921 | Rodriguez | Jul 2002 | A1 |
| 20020091747 | Rehg et al. | Jul 2002 | A1 |
| 20020091748 | Rehg et al. | Jul 2002 | A1 |
| 20020111876 | Rudraraju et al. | Aug 2002 | A1 |
| 20020129340 | Tuttle | Sep 2002 | A1 |
| 20020147745 | Houben et al. | Oct 2002 | A1 |
| 20020184616 | Chessell et al. | Dec 2002 | A1 |
| 20030004771 | Yaung | Jan 2003 | A1 |
| 20030023413 | Srinivasa | Jan 2003 | A1 |
| 20030033432 | Simpson et al. | Feb 2003 | A1 |
| 20030091055 | Craddock et al. | May 2003 | A1 |
| 20030126240 | Vosseler | Jul 2003 | A1 |
| 20030204804 | Petri et al. | Oct 2003 | A1 |
| 20040006745 | Van Helden et al. | Jan 2004 | A1 |
| 20040041838 | Adusumilli et al. | Mar 2004 | A1 |
| 20040073529 | Stanfill | Apr 2004 | A1 |
| 20040093559 | Amaru et al. | May 2004 | A1 |
| 20040098452 | Brown et al. | May 2004 | A1 |
| 20040107414 | Bronicki et al. | Jun 2004 | A1 |
| 20040111469 | Manion et al. | Jun 2004 | A1 |
| 20040148373 | Childress et al. | Jul 2004 | A1 |
| 20040177099 | Ganesh et al. | Sep 2004 | A1 |
| 20040205726 | Chedgey et al. | Oct 2004 | A1 |
| 20040207665 | Mathur | Oct 2004 | A1 |
| 20040210831 | Feng et al. | Oct 2004 | A1 |
| 20040225657 | Sarkar | Nov 2004 | A1 |
| 20040260590 | Golani et al. | Dec 2004 | A1 |
| 20050021689 | Marvin et al. | Jan 2005 | A1 |
| 20050034112 | Stanfill | Feb 2005 | A1 |
| 20050039176 | Fournie | Feb 2005 | A1 |
| 20050059046 | Labrenz et al. | Mar 2005 | A1 |
| 20050086360 | Mamou et al. | Apr 2005 | A1 |
| 20050097561 | Schumacher et al. | May 2005 | A1 |
| 20050102670 | Bretl et al. | May 2005 | A1 |
| 20050144277 | Flurry et al. | Jun 2005 | A1 |
| 20050144596 | McCullough et al. | Jun 2005 | A1 |
| 20050149935 | Benedetti | Jul 2005 | A1 |
| 20050177531 | Bracewell | Aug 2005 | A1 |
| 20050193056 | Schaefer et al. | Sep 2005 | A1 |
| 20050216421 | Barry et al. | Sep 2005 | A1 |
| 20050240621 | Robertson et al. | Oct 2005 | A1 |
| 20050262470 | Gavrilov | Nov 2005 | A1 |
| 20050289527 | Illowsky et al. | Dec 2005 | A1 |
| 20060085462 | Todd | Apr 2006 | A1 |
| 20060095722 | Biles et al. | May 2006 | A1 |
| 20060098017 | Tarditi et al. | May 2006 | A1 |
| 20060206872 | Krishnaswamy | Sep 2006 | A1 |
| 20060282474 | MacKinnon, Jr. | Dec 2006 | A1 |
| 20060294150 | Stanfill et al. | Dec 2006 | A1 |
| 20060294459 | Davis et al. | Dec 2006 | A1 |
| 20070011668 | Wholey et al. | Jan 2007 | A1 |
| 20070022077 | Stanfill | Jan 2007 | A1 |
| 20070035543 | David et al. | Feb 2007 | A1 |
| 20070094211 | Sun et al. | Apr 2007 | A1 |
| 20070118839 | Berstis et al. | May 2007 | A1 |
| 20070139441 | Lucas et al. | Jun 2007 | A1 |
| 20070143360 | Harris et al. | Jun 2007 | A1 |
| 20070150429 | Huelsman et al. | Jun 2007 | A1 |
| 20070174185 | McGoveran | Jul 2007 | A1 |
| 20070179923 | Stanfill | Aug 2007 | A1 |
| 20070239766 | Papaefstathiou et al. | Oct 2007 | A1 |
| 20070271381 | Wholey et al. | Nov 2007 | A1 |
| 20070285440 | MacInnis et al. | Dec 2007 | A1 |
| 20080049022 | Sherb et al. | Feb 2008 | A1 |
| 20080126755 | Wu et al. | May 2008 | A1 |
| 20080288608 | Johnson | Nov 2008 | A1 |
| 20080294615 | Furuya et al. | Nov 2008 | A1 |
| 20090030863 | Stanfill et al. | Jan 2009 | A1 |
| 20090064147 | Beckerle et al. | Mar 2009 | A1 |
| 20090083313 | Stanfill et al. | Mar 2009 | A1 |
| 20090182728 | Anderson | Jul 2009 | A1 |
| 20090193417 | Kahlon | Jul 2009 | A1 |
| 20090224941 | Kansal et al. | Sep 2009 | A1 |
| 20090327196 | Studer et al. | Dec 2009 | A1 |
| 20100070955 | Kahlon | Mar 2010 | A1 |
| 20100169137 | Jastrebski et al. | Jul 2010 | A1 |
| 20100174694 | Staebler et al. | Jul 2010 | A1 |
| 20100180344 | Malyshev et al. | Jul 2010 | A1 |
| 20100211953 | Wakeling et al. | Aug 2010 | A1 |
| 20100218031 | Agarwal et al. | Aug 2010 | A1 |
| 20100281488 | Krishnamurthy et al. | Nov 2010 | A1 |
| 20110093433 | Stanfill et al. | Apr 2011 | A1 |
| 20120054255 | Buxbaum | Mar 2012 | A1 |
| Number | Date | Country |
|---|---|---|
| 64-013189 | Jan 1989 | JP |
| 06-236276 | Aug 1994 | JP |
| 08-278892 | Oct 1996 | JP |
| 08-305576 | Nov 1996 | JP |
| 63-231613 | Sep 1998 | JP |
| 11-184766 | Jul 1999 | JP |
| 2000-99317 | Apr 2000 | JP |
| 2002-229943 | Aug 2002 | JP |
| 2005-317010 | Nov 2005 | JP |
| 2006-504160 | Feb 2006 | JP |
| WO 9800791 | Jan 1998 | WO |
| WO 0211344 | Feb 2002 | WO |
| WO2005001687 | Jan 2005 | WO |
| WO 2005086906 | Sep 2005 | WO |
| WO 2008124319 | Oct 2008 | WO |
| WO 2009039352 | Mar 2009 | WO |
| Entry |
|---|
| Babaoglu, O et al., “Mapping parallel computations onto distributed systems in Paralex” Compuero '91. Advanced Computer Technology, Reliable Systems and Applications. 5th Annual European Computer Conference. Proceedings. Bologna, Italy May 13-16, 1991, Los Alamitos, CA, USA, IEEE Comput. Soc, US, May 13, 1991, pp. 123-130. |
| Baer, J.L. et al., “Legality and Other Properties of Graph Models of Computations.” Journal of the Association for Computing Machinery, vol. 17, No. 3, Jul. 1970, pp. 543-554. |
| Bookstein, A. et al., “Modeling Word Occurrences for the Compression of Concordances.” ACM Transactions on Information Systems, vol. 15, No. 3, Jul. 1997, pp. 254-290. |
| Cytron, Ron et al., “Efficiently Computing Static Single Assignment Form and the Control Dependence Graph.” ACM Transactions on Programming Languages and Systems, vol. 13, No. 4, Oct. 1991, pp. 451-490. |
| Ebert, Jurgen et al., “A Declarative Approach to Graph-Based Modeling.” Workshop on Graph-Theoretic Concepts in Computer Science, 1994, pp. 1-19. |
| European Search Report issued in application No. EP10003554, dated Sep. 24, 2010, 7 pages. |
| Gamma et al. “Design Patterns: Elements of Reusable Object-Oriented Software”, Sep. 1999. |
| International Search Report & Written Opinion issued in PCT application No. PCT/US08/71206, mailed Oct. 22, 2008, 12 pages. |
| International Search Report & Written Opinion issued in PCT application No. PCT/US10/49966, dated Nov. 23, 2010, 8 pages. |
| Jawadi, Ramamohanrao et al., “A Graph-based Transaction Model for Active Databases and its Parallel Implementation.” U. Florida Tech. Rep TR94-0003, 1994, pp. 1-29. |
| Kebschull, U. et al., “Efficient Graph-Based Computation and Manipulation of Functional Decision Diagrams.” University of Tubingen, 1993 IEEE, pp. 278-282. |
| Li, Xiqing et al., “A Practical External Sort for Shared Disk MPPs.” Proceedings of Supercomputing '93, 1993, 24 pages. |
| Martin, David et al., “Models of Computations and Systems—Evaluation of Vertex Probabilities in Graph Models of Computations.” Journal of the Association for Computing Machinery, vol. 14, No. 2, Apr. 1967, pp. 281-299. |
| Ou, Chao-Wei et al., “Architecture-Independent Locality-Improving Transformations of Computational Graphs Embedded in k-Dimensions.” Proceedings of the 9th International Conference on Supercomputing, 1995, pp. 289-298. |
| “RASSP Data Flow Graph Design Application Note.” International Conference on Parallel Processing, Dec. 2000, Retrieved from Internet <http://www.atl.external.lmco.com/projects/rassp/RASSP—legacy/appnotes/FLOW/APNOTE—FLOW—02 >, 5 pages. |
| Stanfill, Craig, “Massively Parallel Information Retrieval for Wide Area Information Servers.” 1991 IEEE International Conference on Systems, Man and Cybernetics, Oct. 1991, pp. 679-682. |
| Stanfill, Craig et al., “Parallel Free-Text Search on the Connection Machine System.” Communications of the ACM, vol. 29, No. 12, Dec. 1986, pp. 1229-1239. |
| Stanfill, Craig, “The Marriage of Parallel Computing and Information Retrieval.” IEE Colloquium on Parallel Techniques for Information Retrieval, Apr. 1989, 5 pages. |
| Supplemental European Search Report issued in application No. EP08796632, dated Sep. 24, 2010, 6 pages. |
| Wah, B.W. et al., “Report on Workshop on High Performance Computing and Communications for Grand Challenge Applications: Computer Vision, Speech and Natural Language Processing, and Artificial Intelligence.” IEEE Transactions on Knowledge and Data Engineering, vol. 5, No. 1, Feb. 1993, 138-154. |
| Burch, J.R. et al., “Sequential circuit verification using symbolic model checking.” In Design Automation Conference, 1990, Proceedings of the 27th ACM/IEEE. Jun. 24-28, 1990, pp. 46-51. |
| Guyer et al., “Finding Your Cronies: Static Analysis for Dynamic Object Colocation.” Oct. 2004, ACM, pp. 237-250. |
| Grove et al., “A Framework for Call Graph Construction Algorithms.” Nov. 2001, ACM TOPLAS, vol. 23, Issue 6, pp. 685-746. |
| Herniter, Marc E., “Schematic Capture with MicroSim PSpice,” 2nd Edition, Prentice Hall, Upper Saddle River, N.J., 1996, pp. 51-52, 255-280, 292-297. |
| International Search Report & Written Opinion issued in PCT application No. PCT/US01/23552, mailed Jan. 24, 2002, 5 pages. |
| International Search Report & Written Opinion issued in PCT application No. PCT/US06/24957, dated Jan. 17, 2008, 14 pages. |
| International Search Report & Written Opinion issued in PCT application No. PCT/US07/75576, mailed Sep. 16, 2008, 13 pages. |
| International Search Report & Written Opinion received in PCT application No. PCT/US10/24036, mailed Mar. 23, 2010, 11 pages. |
| Just et al., “Review and Analysis of Synthetic Diversity for Breaking Monocultures.” Oct. 2004, ACM, pp. 23-32. |
| Krahmer et al., “Graph-Based Generation of Referring Expressions.” Mar. 2003, MIT Press, vol. 29, No. 1, pp. 53-72. |
| Supplemental European Search Report issued in application No. EP07813940, dated Nov. 26, 2009, 7 pages. |
| International Search Report & Written Opinion received in PCT application No. PCT/US2011/040440, mailed Oct. 12, 2011, 13 pages. |
| Control-M; New Dimension Software. User Manual. New Dimension Software Ltd., 1999. |
| Romberg, M., “UNICORE: Beyond Web-based Job-Submission,” Proceedings of the 42nd Cray User Group Conference, Noordwijk (May 22-26, 2000). |
| “Unicenter AutoSys Job Management,” Computer Associates, Copyright 2001. |
| Krsul, Ivan et al., “VMPlants: Providing and Managing Virtual Machine Execution Environments for Grid Computing.” Proceedings of the ACM/IEEE SC2004 Conference on Supercomputing, 2001, Nov. 6-12, 2004, 12 pages. |
| Vajracharya, Suvas et al., “Asynchronous Resource Management.” Proceedings of the 15th International Parallel and Distributed Processing Symposium, Apr. 2001, 10 pages. |
| Japanese Office Action, with English Translation, JP application No. 2008-519474, mailed Sep. 25, 2012, 8 pages. |
| Shoten, Iwanami, “Encyclopedic Dictionary of Computer Science,” (with English Translation), May 25, 1990, p. 741. |
| Japanese Office Action, with English Translation, JP application No. 2009-523997, mailed Oct. 23, 2012, 7 pages. |
| European Search Report issued in application No. EP10741775, dated Nov. 14, 2012, 4 pages. |
| Russell, Nick, et al., “Workflow Control-Flow Patterns a Revised View,” Workflow Patterns Initiative, 2006, pp. 1-134. |
| van der Aalst, W.M.P., et al., “Workflow Patterns,” Distributed and Parallel Databases, 14, 5-51, 2003. |
| Japanese Office Action, with English Translation, JP application No. 2011-000948, mailed Jan. 8, 2013, 11 pages. |
| Supplemental European Search Report issued in application No. EP06774092, dated Dec. 19, 2012, 5 pages. |
| “Topological sorting,” Wikipedia, accessed Dec. 10, 2012, 2 pages. |
| “Visual Lint: Squash Bugs Early with Interactive C/C++, C# and Java Code Analysis for Microsoft Visual Studio and Eclipse,” [retrieved from the internet Dec. 3, 2012: www.riverblade.co.uk/products/visual—lint.] (2 pages). |
| Japanese Office Action, with English Translation, JP application No. 2010-518415, mailed Feb. 21, 2013, 11 pages. |
| U.S. Appl. No. 09/627,252. |
| U.S. Appl. No. 10/268,509. |
| U.S. Appl. No. 11/467,724. |
| U.S. Appl. No. 11/733,579. |
| U.S. Appl. No. 11/169,014. |
| U.S. Appl. No. 11/167,902. |
| U.S. Appl. No. 12/977,545. |
| U.S. Appl. No. 11/836,349. |
| U.S. Appl. No. 12/180,141. |
| U.S. Appl. No. 12/704,998. |
| U.S. Appl. No. 13/161,010. |
| U.S. Appl. No. 13/678,921. |
| U.S. Appl. No. 13/678,928. |
| Japanese Office Action for Japanese Application No. 2010-518415, with English Translation, dated Nov. 18, 2013, 11 pages. |
| Dillon, Laura K., et al., “Inference Graphs: A Computational Structure Supporting Generation of Customizable and Correct Analysis Components,” IEEE Transactions on Software Engineering, vol. 29, No. 2, Feb. 2003, pp. 133-150. |
| Evripidou, Paraskevas, et al., “Incorporating input/output operations into dynamic data-flow graphs,” Parallel Computing 21 (1995) 1285-1311. |
| Extended European Search Report, EP 12165575, mailed May 10, 2013, 9 pages. |
| Frankl, Phyllis G., et al., “An Applicable Family of Data Flow Testing Criteria,” IEEE Transactions on Software Engineering, vol. 14, No. 10, Oct. 1988, pp. 1483-1498. |
| Whiting, Paul G., et al., “A History of Data-Flow Languages,” IEEE Annals of the History of Computing, vol. 16, No. 4, 1994, pp. 38-59. |
| Number | Date | Country | |
|---|---|---|---|
| 20110078500 A1 | Mar 2011 | US |
| Number | Date | Country | |
|---|---|---|---|
| 61245862 | Sep 2009 | US |
