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Summary
This summary is machine-generated.

This study introduces a new methodology for developing high-performance data analytics (HPDA) applications by integrating traditional high-performance computing (HPC) workflows with data analytic frameworks using task-based programming models (TBPMs). The developed approach offers seamless integration and improved performance compared to existing solutions like Spark.

Keywords:
Big Data High PerformanceData AnalyticsParallel ComputingTask Based Programming Models

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Area of Science:

  • Computer Science
  • Computational Science

Background:

  • High-performance data analytics (HPDA) integrates high-performance computing (HPC) with data analytic frameworks.
  • Current HPDA research primarily focuses on optimizing data analytic frameworks on HPC technologies, neglecting application development integration.
  • A gap exists in seamlessly combining HPC workflows and data analytic algorithms from a developer's perspective.

Purpose of the Study:

  • To present a novel methodology for developing HPDA applications by leveraging task-based programming models (TBPMs).
  • To enable seamless integration of traditional HPC workflows and data analytic transformations within a unified development approach.
  • To demonstrate the efficacy and performance benefits of the proposed methodology compared to existing frameworks.

Main Methods:

  • Developed a methodology for HPDA application development based on TBPMs.
  • Implemented a prototype using the PyCOMPSs task-based programming model.
  • Validated the approach by developing and evaluating HPDA applications, comparing performance against the Spark framework.

Main Results:

  • HPDA applications can be developed seamlessly using the proposed TBPM-based methodology.
  • The implemented prototype demonstrates superior performance compared to the Spark framework for specific HPDA tasks.
  • The study validates the feasibility of integrating data analytics directly into HPC applications.

Conclusions:

  • Task-based programming models provide an effective foundation for unified HPDA application development.
  • The proposed methodology facilitates the seamless combination of HPC and data analytics, offering performance advantages.
  • This work paves the way for more integrated and efficient scientific computing by merging HPC and data analytics.