Systems and methods for task parallelization

• US 10,459,703 B2
• Filed: 10/31/2017
• Issued: 10/29/2019
• Est. Priority Date: 03/18/2015
• Status: Active Grant

• Current Assignee: Finastra Global Ltd.
• Original Assignee: Misys Global Limited
• Inventors: Leenhouwers, Bram, Gabreau, Edouard, Grimaud, Meggie, Antonov, Alexis
• Primary Examiner(s): Sun, Charlie
• Application Number:
  US15/799,866
• Publication Number:
  US 20180052692A1
• Time in Patent Office: 728 Days
• CPC Class Codes: G06F 8/427 Parsing
• CPC Class Codes: G06F 8/443 Optimisation
• CPC Class Codes: G06F 8/456 Parallelism detection
• CPC Class Codes: G06F 9/5016 the resource being the memory

Abstract

Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to obtain at least one script and at least one document, wherein the script includes one or more instructions to be translated for execution in a parallelized computing environment, and wherein the document includes data that is referenced by the script. A syntax tree for the script can be determined. At least one approach for optimizing the syntax tree can be applied. Parallelized code for execution in the parallelized computing environment can be generated. A binary representation of the document can be determined. The parallelized code can be processed based at least in part on the binary representation of the document.

Claims

1. A computer-implemented method comprising:

obtaining, by a computing system, at least one script and at least one document, wherein the script includes one or more instructions to be translated for execution in a parallelized computing environment, and wherein the document includes data that is referenced by the script;

determining, by the computing system, a syntax tree for the script;

applying, by the computing system, at least one approach for optimizing the syntax tree, wherein the at least one approach for optimizing the syntax tree includes determining optimal variable types in the syntax tree;

determining, by the computing system, at least one inferred data type for at least one variable referenced in the syntax tree;

determining, by the computing system, the at least one variable corresponds to an assignment statement that references a second variable and to a same data type as the second variable;

generating, by the computing system, parallelized code for execution in the parallelized computing environment;

determining, by the computing system, a binary representation of the document; and

processing, by the computing system, the parallelized code based at least in part on the binary representation of the document.

2. The computer-implemented method of claim 1, the method further comprising:

obtaining at least one second document, wherein the second document includes the data referenced by the script;

determining a binary representation of the second document;

determining an interlaced binary structure based at least in part on the binary representation of the document and the binary representation of the second document; and

processing the parallelized code with respect to the document and the second document based at least in part on the interlaced binary structure.

3. The computer-implemented method of claim 1, wherein the determining the at least one inferred data type for the at least one variable further comprises:

determining the at least one variable corresponds to a binary statement that includes a minus operator; and

determining the at least one variable is one of a double data type, a date data type, a string data type, a Boolean data type, a custom data type, an array data type, a matrix data type, or a cube data type.

4. The computer-implemented method of claim 1, wherein the determining the at least one inferred data type for the at least one variable further comprises:

determining the at least one variable corresponds to a binary statement that includes a multiplication operator; and

determining the at least one variable is one of a double data type, a date data type, a string data type, a Boolean data type, a custom data type, an array data type, a matrix data type, or a cube data type.

5. The computer-implemented method of claim 1, wherein the at least one script is written in a Groovy programming language.

6. The computer-implemented method of claim 1, wherein the determining the syntax tree of the script further comprises:

obtaining the syntax tree using an API of a Groovy programming language.

7. The computer-implemented method of claim 1, wherein the generating the parallelized code further comprises:

determining the parallelized code by at least transforming the optimized syntax tree to a parallelization framework code.

8. The computer-implemented method of claim 7, wherein the parallelization framework code corresponds to one of an OpenCL parallelization framework, a CUDA parallelization framework, or a Java bytecode.

9. A system comprising:

at least one processor; and

a memory storing instructions that, when executed by the at least one processor, cause the system to perform;

obtaining at least one script and at least one document, wherein the script includes one or more instructions to be translated for execution in a parallelized computing environment, and wherein the document includes data that is referenced by the script;

determining a syntax tree for the script;

applying at least one approach for optimizing the syntax tree, wherein the at least one approach for optimizing the syntax tree includes determining optimal variable types in the syntax tree;

determining at least one inferred data type for at least one variable referenced in the syntax tree;

determining the at least one variable corresponds to an assignment statement that references a second variable and to a same data type as the second variable;

generating parallelized code for execution in the parallelized computing environment;

determining a binary representation of the document; and

processing the parallelized code based at least in part on the binary representation of the document.

10. The system of claim 9, wherein the determining the at least one inferred data type for the at least one variable further comprises:

determining the at least one variable corresponds to a binary statement that includes a minus operator; and

determining the at least one variable is one of a double data type, a date data type, a string data type, a Boolean data type, a custom data type, an array data type, a matrix data type, or a cube data type.

11. The system of claim 9, wherein the determining the at least one inferred data type for the at least one variable further comprises:

determining the at least one variable corresponds to a binary statement that includes a multiplication operator; and

determining the at least one variable is one of a double data type, a date data type, a string data type, a Boolean data type, a custom data type, an array data type, a matrix data type, or a cube data type.

12. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform:

obtaining at least one script and at least one document, wherein the script includes one or more instructions to be translated for execution in a parallelized computing environment, and wherein the document includes data that is referenced by the script;

determining a syntax tree for the script;

applying at least one approach for optimizing the syntax tree, wherein the at least one approach for optimizing the syntax tree includes determining optimal variable types in the syntax tree;

determining at least one inferred data type for at least one variable referenced in the syntax tree;

determining the at least one variable corresponds to an assignment statement that references a second variable and to a same data type as the second variable;

generating parallelized code for execution in the parallelized computing environment;

determining a binary representation of the document; and

processing the parallelized code based at least in part on the binary representation of the document.

13. The non-transitory computer-readable storage medium of claim 12, wherein the determining the at least one inferred data type for the at least one variable further comprises:

determining the at least one variable corresponds to a binary statement that includes a minus operator; and

determining the at least one variable is one of a double data type, a date data type, a string data type, a Boolean data type, a custom data type, an array data type, a matrix data type, or a cube data type.

14. The non-transitory computer-readable storage medium of claim 12, wherein the determining the at least one inferred data type for the at least one variable further comprises:

determining the at least one variable corresponds to a binary statement that includes a multiplication operator; and

determining the at least one variable is one of a double data type, a date data type, a string data type, a Boolean data type, a custom data type, an array data type, a matrix data type, or a cube data type.