Declarative programming model with a native programming language

Patent metadata

Field	Value
Publication number	US-9600250-B2
Application number	US-90070810-A
Country	US
Kind code	B2
Filing date	Oct 8, 2010
Priority date	Oct 8, 2010
Publication date	Mar 21, 2017
Grant date	Mar 21, 2017

Abstract

An execution environment in a computer system supports a declarative programming model where user code is written with a query syntax in a native programming language to express inherent parallelism in terms of data flow. The execution environment translates queries in the user code into a runtime agnostic representation and dynamically selects an execution runtime for executing the runtime agnostic representation.

First claim

What is claimed is:

1. A computer readable storage medium, which does not include transitory propagating signals, storing computer-executable instructions that, when executed by a computer system, cause the computer system to perform a method comprising:
identifying a query syntax in user code written in a native programming language;
translating the query syntax into a runtime agnostic representation of the user code that expresses tasks and the interactions of tasks without explicit reference to programming features or constructs of any particular execution runtime;
dynamically selecting an execution runtime from at least two execution runtimes for executing the runtime agnostic representation that is executable with the dynamically selected execution runtime; and
delineating a programming model for the user code from the at least two execution runtimes to permit the user code to target different execution runtimes of the at least two execution runtimes.

2. The computer readable storage medium of claim 1 , wherein the query syntax expresses a set of tasks in terms of data flow.

3. The computer readable storage medium of claim 2 , wherein at least two of the set of tasks are able to be scheduled for concurrent execution.

4. The computer readable storage medium of claim 1 , wherein the runtime agnostic representation expresses a set of tasks defined by the query syntax in terms of data flow.

5. The computer readable storage medium of claim 1 , wherein the runtime agnostic representation forms a directed acyclic graph (DAG).

6. The computer readable storage medium of claim 1 , wherein the dynamically selected execution runtime is one of a set of two or more execution runtimes in the computer system.

7. The computer readable storage medium of claim 1 , wherein the native programming language is C++.

8. The computer readable storage medium of claim 1 , wherein the dynamically selected execution runtime corresponds to one of a single machine, a cluster of machines, or a cloud of machines.

9. The computer readable storage medium of claim 1 , wherein the computer system includes one of a single machine, a cluster of machines, or a cloud of machines.

10. A computer readable storage medium, which does not include transitory propagating signals, storing computer-executable instructions that, when executed by a computer system, cause the computer system to perform a method comprising:
extracting implicit parallelism in a query in user code written in a native programming language;
representing the implicit parallelism as a runtime representation including a set of tasks and a set of interactions of the set of tasks without explicit reference to programming features or constructs of any particular execution runtime;
dynamically selecting an execution runtime from at least two execution runtimes for executing the runtime representation that is executable with the dynamically selected execution runtime; and
delineating a programming model for the user code from the at least two execution runtimes to permit the user code to target different execution runtimes of the at least two execution runtimes.

11. The computer readable storage medium of claim 10 , wherein the query expresses the set of tasks in terms of data flow.

12. The computer readable storage medium of claim 10 , further comprising:
representing the implicit parallelism as a runtime agnostic representation that forms a directed acyclic graph (DAG).

13. The computer readable storage medium of claim 10 , wherein the dynamically selected execution runtime is one of a set of two or more execution runtimes in the computer system.

14. The computer readable storage medium of claim 10 , wherein the native programming language is C++.

15. The computer readable storage medium of claim 10 , wherein the dynamically selected execution runtime corresponds to one of a single machine, a cluster of machines, or a cloud of machines.

16. The computer readable storage medium of claim 10 , wherein the computer system includes one of a single machine, a cluster of machines, or a cloud of machines.

17. A method performed by a computer system, the method comprising:
identifying a query syntax in user code that expresses a set of tasks in terms of data flow, the user code written in a native programming language;
translating the query syntax into a runtime agnostic representation of the user code that expresses tasks and the interactions of tasks without explicit reference to programming features or constructs of any particular execution runtime;
dynamically selecting an execution runtime from at least two execution runtimes in the computer system for executing the runtime agnostic representation that is executable with the dynamically selected execution runtime, the runtime agnostic representation forming a directed acyclic graph (DAG); and
delineating a programming model for the user code from the at least two execution runtimes to permit the user code to target different execution runtimes of the at least two execution runtimes.

18. The method of claim 17 , wherein the native programming language is C++.

19. The method of claim 17 , wherein the dynamically selected execution runtime corresponds to one of a single machine, a cluster of machines, or a cloud of machines.

20. The method of claim 17 , wherein the computer system includes a single machine, a cluster of machines, or a cloud of machines.

Assignees

• Varadarajan Krishnan
• Chu Michael L
• Microsoft Technology Licensing Llc

Inventors

• Varadarajan Krishnan
• Chu Michael L

Classifications

• G06F9/5027

  the resource being a machine, e.g. CPUs, Servers, Terminals · CPC title

• G06F8/456 Primary

  Parallelism detection · CPC title

• G06F2209/5017

  Task decomposition · CPC title

• G06F8/433 Primary

  Dependency analysis; Data or control flow analysis · CPC title