View source for 18748889. Parallel Processing Of Data simplified abstract (Google LLC)

=Parallel Processing Of Data=


==Organization Name==

[[:Category:Google LLC|Google LLC]]


[[Category:Google LLC]]

==Inventor(s)==

[[:Category:Craig D. Chambers of Seattle WA (US)|Craig D. Chambers of Seattle WA (US)]][[Category:Craig D. Chambers of Seattle WA (US)]]

[[:Category:Ashish Raniwala of Bellevue WA (US)|Ashish Raniwala of Bellevue WA (US)]][[Category:Ashish Raniwala of Bellevue WA (US)]]

[[:Category:Frances J. Perry of Seattle WA (US)|Frances J. Perry of Seattle WA (US)]][[Category:Frances J. Perry of Seattle WA (US)]]

[[:Category:Stephen R. Adams of Seattle WA (US)|Stephen R. Adams of Seattle WA (US)]][[Category:Stephen R. Adams of Seattle WA (US)]]

[[:Category:Robert R. Henry of Seattle WA (US)|Robert R. Henry of Seattle WA (US)]][[Category:Robert R. Henry of Seattle WA (US)]]

[[:Category:Robert Bradshaw of Seattle WA (US)|Robert Bradshaw of Seattle WA (US)]][[Category:Robert Bradshaw of Seattle WA (US)]]

[[:Category:Nathan Weizenbaum of Seattle WA (US)|Nathan Weizenbaum of Seattle WA (US)]][[Category:Nathan Weizenbaum of Seattle WA (US)]]

==Parallel Processing Of Data - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18748889 titled 'Parallel Processing Of Data

'''Simplified Explanation''': 

The patent application describes a method for generating and executing parallel data operations in a data parallel pipeline.

'''Key Features and Innovation''':
* Specification of multiple parallel data objects and operations in a data parallel pipeline.
* Generation of a dataflow graph based on the pipeline.
* Application of graph transformations to optimize the dataflow graph.
* Execution of combined parallel operations to produce materialized data objects.

'''Potential Applications''':
This technology can be applied in various fields such as big data processing, machine learning, and scientific computing.

'''Problems Solved''':
This technology addresses the need for efficient parallel processing of large datasets and complex operations.

'''Benefits''':
* Improved performance in data processing tasks.
* Scalability for handling large volumes of data.
* Enhanced productivity in parallel computing environments.

'''Commercial Applications''':
Optimized data processing in industries such as finance, healthcare, and e-commerce can lead to cost savings and improved decision-making processes.

'''Prior Art''':
Researchers can explore prior art related to data parallel processing, graph transformations, and parallel computing techniques.

'''Frequently Updated Research''':
Stay informed about advancements in parallel computing, dataflow optimization, and distributed computing systems.

'''Questions about Data Parallel Pipeline''':
1. How does the technology optimize parallel data operations?
2. What are the potential challenges in implementing a data parallel pipeline system?

1. A relevant generic question not answered by the article, with a detailed answer:
How does the data parallel pipeline technology compare to traditional sequential data processing methods?
The data parallel pipeline technology offers significant advantages over traditional sequential processing methods by enabling parallel execution of operations on multiple data objects simultaneously. This results in faster processing times and improved scalability for handling large datasets efficiently.

2. Another relevant generic question, with a detailed answer:
What are the key components of a dataflow graph in a data parallel pipeline system?
The key components of a dataflow graph include parallel data objects, parallel operations, dependencies between operations, and the flow of data between different stages of processing. By representing the pipeline as a dataflow graph, the system can optimize the execution of operations and improve overall performance.


==Original Abstract Submitted==

A data parallel pipeline may specify multiple parallel data objects that contain multiple elements and multiple parallel operations that operate on the parallel data objects. Based on the data parallel pipeline, a dataflow graph of deferred parallel data objects and deferred parallel operations corresponding to the data parallel pipeline may be generated and one or more graph transformations may be applied to the dataflow graph to generate a revised dataflow graph that includes one or more of the deferred parallel data objects and deferred, combined parallel data operations. The deferred, combined parallel operations may be executed to produce materialized parallel data objects corresponding to the deferred parallel data objects.



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