parallel data processing

USAGE SUMMARY

The phrase "parallel data processing" is correct and can be used in written English.
This phrase is typically used in the context of technology or computing, referring to the simultaneous processing of multiple data sources. For example, "Parallel data processing accelerates the speed of data analysis, allowing companies to gain insights into their data quicker than ever before."

✓ Grammatically correct

Science

News & Media

Academia

Human-verified examples from authoritative sources

Exact Expressions

37 human-written examples

Our architecture ensures parallel data processing using Directed Acyclic task graph.

Procedia Engineering

In addition to often producing short, elegant code for problems involving lists or collections, this model has proven very useful for large-scale highly parallel data processing.

Cornell University

In this paper we present our approach towards parallel data processing exploiting dynamic resource allocation in IaaS clouds.

Procedia Engineering

In this paper, we present a new MapReduce framework, called Grex, designed to leverage general purpose graphics processing units (GPUs) for parallel data processing.

Journal of Parallel and Distributed Computing

This heterogeneous (processor with reconfigurable hardware) platform consumes less power than a standard microprocessor and provides powerful parallel data processing capabilities: applying hardware/software (hw/sw) co-design allows real-time throughput with a very low power-per-feature rate.

Computers & Electrical Engineering

Data processing frameworks like Google's MapReduce and its open source implementation Hadoop, Microsoft's Dryad and so on are currently in use for parallel data processing in cloud-based companies.

Procedia Engineering

Human-verified similar examples from authoritative sources

Similar Expressions

23 human-written examples

We think ExCamera started the movement to (mis- use cloud-functions services for mis- usey "burst-parallel" data processing.

Stanford University

Apache Hadoop is a distributed parallel data-processing framework that supports MapReduce-type computations, enabling users to perform distributed computations effectively in increasingly brittle environments [ 11].

BioMed Research International

In the previously example i.e.: the indexation of the 18 divisions of Genbank both for EMBOSS and BLAST, if 18 CPUS (Xeon 5140 Woodcrest 2.3GHz, sharing data with Network File System) are used in parallel for data processing.

Bioinformatics

Actually an artificial neural network (ANN) is an enormously interconnected network structure comprising of several simple processing elements proficient of executing parallel computation for data processing.

Applied Water Science

Since it was proposed by Google in 2004, MapReduce has become the most popular technology that makes data-intensive computing possible for ordinary users, especially those that don't have any prior experience with parallel and distributed data processing.

Journal of Cloud Computing

Expert writing Tips

Best practice

Use "parallel data processing" when you want to specifically emphasize the concurrent handling and manipulation of data. If the emphasis is on computation alone, consider using "parallel computing".

Common error

Don't use "parallel data processing" interchangeably with distributed data processing. While both involve multiple processors, parallel processing often refers to tightly coupled systems, while distributed processing involves loosely coupled systems across a network.

Linguistic Context

The phrase "parallel data processing" functions as a noun phrase, typically used as a subject or object in a sentence. It describes a method of computing where data is processed simultaneously across multiple processors. Ludwig confirms its correct usage in various scientific and technical contexts.

Expression frequency: Common

Frequent in

Science

67%

News & Media

17%

Academia

16%

Less common in

Formal & Business

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "parallel data processing" is a grammatically correct and commonly used term, particularly in scientific, academic, and news contexts. As Ludwig highlights, this term refers to the simultaneous processing of data to enhance speed and efficiency. When discussing this technique, remember to be specific about the frameworks and architectures involved. While similar to "parallel computing", "parallel data processing" emphasizes the concurrent data handling aspect. Avoid confusing it with distributed data processing, which involves loosely coupled systems. Ludwig AI's analysis confirms its widespread use and technical accuracy, making it a valuable term in the lexicon of data science and computing.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

parallel computing for data

Rephrases the concept using "parallel computing" instead of "parallel data processing".

concurrent data handling

Focuses on the simultaneous management of data, emphasizing the handling aspect.

simultaneous information processing

Highlights the processing of information at the same time.

distributed data computation

Emphasizes the computation aspect, implying data is spread across multiple nodes.

multi-core data manipulation

Refers specifically to data manipulation on multi-core processors.

massively parallel computation

Highlights the massive scale of parallel computation involved.

concurrent data analysis

Focuses on the analysis of data happening concurrently.

high-performance data processing

Emphasizes the performance aspect of data processing.

parallel execution of data tasks

Focuses on execution of data tasks simultaneously.

accelerated data throughput

Highlights the increased throughput achieved through parallel processing.

FAQs

How is "parallel data processing" used in big data?

"Parallel data processing" is essential in big data for efficiently handling and analyzing massive datasets by dividing the workload across multiple processors or machines. Frameworks like MapReduce leverage this to achieve scalability and speed.

What are the advantages of "parallel data processing" over sequential processing?

"Parallel data processing" offers advantages such as reduced processing time, increased throughput, and the ability to handle larger datasets compared to sequential processing, which processes data one step at a time.

What's the difference between "parallel data processing" and "parallel computing"?

"Parallel computing" is a broader term referring to the use of multiple processors to solve a problem, while "parallel data processing" specifically focuses on the simultaneous processing of data as part of that computing process. The first one is about the computation and the other one is more focused on data manipulation.

How can I implement "parallel data processing" in my data analysis workflow?

You can implement "parallel data processing" using tools and frameworks such as Apache Spark, Hadoop, or by leveraging GPU-accelerated computing, allowing you to distribute the processing workload across multiple cores or nodes.