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Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
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Concurrent query processing in a GPU-based database system.

Hao Li1, Yi-Cheng Tu1, Bo Zeng2

  • 1Department of Computer Science and Engineering, University of South Florida, Tampa, FL, United States of America.

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|April 17, 2019
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Summary
This summary is machine-generated.

This study optimizes GPU resource allocation for multiple applications by using a novel knapsack model and dynamic programming. This approach enhances system throughput and significantly reduces total workload running time.

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Area of Science:

  • Computer Science
  • High-Performance Computing
  • Database Systems

Background:

  • Modern GPUs offer immense computational power for large-scale data processing.
  • Existing High-Performance Computing (HPC) systems often treat applications as isolated entities on GPUs.
  • GPU-based Database Management Systems (DBMSs) require efficient resource management for concurrent tasks on a single device.

Purpose of the Study:

  • To optimize system throughput for multiple GPU applications through effective resource allocation.
  • To enhance the performance of concurrent kernel execution on GPUs.
  • To develop a general pre-processing model with batch-level control for GPU task scheduling.

Main Methods:

  • Constructed a variation of the multi-dimensional knapsack model to maximize concurrency in multi-kernel GPU environments.
  • Developed a dynamic programming algorithm to solve the knapsack model, ensuring optimal thread concurrency.
  • Utilized compile-time performance modeling and batch-level control for kernel launching parameters.

Main Results:

  • The dynamic programming algorithm guarantees optimal solutions for thread concurrency.
  • The algorithm exhibits pseudopolynomial time and space complexity.
  • Extensive experiments validated the model's effectiveness on real-world GPU queries, showing significant reductions in total workload execution time compared to sequential and MultiQx-GPU methods.

Conclusions:

  • The proposed resource allocation model and algorithm effectively enhance GPU system throughput.
  • This approach offers a significant improvement in performance for concurrent GPU applications.
  • The method provides a powerful tool for optimizing large-scale data processing on GPUs.