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Performance Modeling in CUDA Streams - A Means for High-Throughput Data Processing.

Hao Li, Di Yu, Anand Kumar

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    |November 14, 2015
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    Summary
    This summary is machine-generated.

    This study models performance for concurrent GPU operations in push-based database systems. We developed a predictive model for compute-bound kernels using NVIDIA CUDA streams, enhancing resource allocation and query processing efficiency.

    Keywords:
    CUDACUDA streamDBMSGPGPUGPUpush-based systems

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    Area of Science:

    • Computer Science
    • Database Systems
    • High-Performance Computing

    Background:

    • Push-based database management systems (DBMS) stream data for concurrent query processing, demanding significant computational power.
    • Modern Graphics Processing Units (GPUs) offer substantial computational capabilities for data-intensive tasks.
    • Efficient resource allocation is crucial for heterogeneous query processing in GPU-accelerated DBMS.

    Purpose of the Study:

    • To develop a performance model for concurrent CUDA kernels within a push-based DBMS.
    • To investigate the relationship between resource occupancy and performance for compute-bound kernels.
    • To analyze the CUDA stream mechanism and its kernel scheduling disciplines.

    Main Methods:

    • Implemented a push-based DBMS (G-SDMS) leveraging NVIDIA's CUDA framework.
    • Developed a performance model correlating resource occupancy with kernel execution time.
    • Conducted experiments with synthetic and real-world CUDA kernels to validate models and scheduling disciplines.

    Main Results:

    • Established a predictive model for the performance of compute-bound CUDA kernels.
    • Demonstrated the connection between kernel resource occupancy and execution performance.
    • Identified and summarized key kernel scheduling disciplines within the CUDA stream mechanism.

    Conclusions:

    • The developed performance models accurately predict CUDA kernel performance under concurrent execution.
    • Understanding resource occupancy is key to optimizing performance in GPU-accelerated database systems.
    • The findings facilitate improved resource allocation and scheduling for heterogeneous queries in G-SDMS.