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HPTMT Parallel Operators for High Performance Data Science and Data Engineering.

Vibhatha Abeykoon1, Supun Kamburugamuve2, Chathura Widanage2

  • 1Indiana University Alumni, Bloomington, IN, United States.

Frontiers in Big Data
|February 24, 2022
PubMed
Summary
This summary is machine-generated.

The HPTMT architecture unifies data engineering and data science for high-performance computing. It efficiently integrates deep learning and data processing using compact data structures like Apache Arrow for optimal performance.

Keywords:
Apache software foundationcylondeep learningexascale and HPC systemsparallel computation

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Area of Science:

  • Computer Science
  • Data Science
  • High-Performance Computing

Background:

  • Data-intensive applications are prevalent across scientific disciplines, encompassing data engineering, deep learning, and machine learning.
  • A lack of standardized data structures and operators hinders interoperability between different implementations.
  • Existing big data processing systems often face challenges in efficiently integrating diverse data science and engineering tasks.

Purpose of the Study:

  • To introduce and elaborate on the HPTMT architecture, designed to unify data engineering and data science.
  • To demonstrate the architecture's capability in linking various aspects of data engineering and data science.
  • To showcase an end-to-end application integrating deep learning and data engineering using the proposed architecture.

Main Methods:

  • Proposed the HPTMT architecture, defining a set of data structures, operators, and an execution model.
  • Developed and illustrated an end-to-end application integrating deep learning and data engineering components.
  • Emphasized the use of efficient, compact data structures, specifically Apache Arrow, for high-performance data representation.

Main Results:

  • The HPTMT architecture demonstrates superior suitability for high-performance computing environments compared to existing big data systems.
  • The system effectively scales sequential computations to distributed environments while maintaining optimal performance.
  • Integration of compact data structures like Apache Arrow enhances processing efficiency.

Conclusions:

  • The HPTMT architecture provides an efficient and unified framework for data-intensive scientific applications.
  • The proposed system facilitates seamless integration of data engineering and data science tasks, improving performance and usability.
  • The architecture's emphasis on efficient data structures is crucial for advancing high-performance computing in data science.