Google llc (20240338235). Parallel Processing Of Data simplified abstract

Parallel Processing Of Data

Organization Name

google llc

Inventor(s)

Craig D. Chambers of Seattle WA (US)

Ashish Raniwala of Bellevue WA (US)

Frances J. Perry of Seattle WA (US)

Stephen R. Adams of Seattle WA (US)

Robert R. Henry of Seattle WA (US)

Robert Bradshaw of Seattle WA (US)

Nathan Weizenbaum of Seattle WA (US)

Parallel Processing Of Data - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240338235 titled 'Parallel Processing Of Data

Simplified Explanation: The patent application describes a method for generating a dataflow graph from a data parallel pipeline, applying graph transformations to optimize the graph, and executing combined parallel operations to produce materialized parallel data objects.

• The patent involves creating a dataflow graph from a data parallel pipeline.
• Graph transformations are applied to optimize the dataflow graph.
• Combined parallel operations are executed to generate materialized parallel data objects.

Key Features and Innovation:

• Generation of a dataflow graph from a data parallel pipeline.
• Application of graph transformations to optimize the dataflow graph.
• Execution of combined parallel operations to produce materialized parallel data objects.

Potential Applications: The technology can be applied in various fields such as data processing, parallel computing, and big data analytics.

Problems Solved: The technology addresses the need for efficient processing of parallel data objects and operations in data pipelines.

Benefits:

• Improved performance in data processing tasks.
• Enhanced scalability in handling large datasets.
• Streamlined workflow in parallel computing environments.

Commercial Applications: Optimized data processing solutions for industries such as finance, healthcare, and e-commerce, leading to increased efficiency and productivity.

Questions about Data Parallel Pipeline Optimization: 1. How does the technology improve the efficiency of data processing tasks? 2. What industries can benefit the most from the optimized data parallel pipeline technology?

Frequently Updated Research: Stay updated on the latest advancements in data parallel pipeline optimization and parallel computing technologies to leverage the benefits of this innovative approach.

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.