Optimizing Data Pipeline Performance in Modern GPU Architectures

Authors

  • Ashvini Byri Scholar, University of Southern California, Parel, Mumbai 400012,
  • Satish Vadlamani Scholar, Osmania University, West Palladio Place, Middletown, DE, USA, satish.sharma.
  • Ashish Kumar Scholar, Tufts University, Medford, MA, 02155 USA
  • Om Goel Independent Researcher, Abes Engineering College Ghaziabad,
  • Shalu Jain Independent Researcher, Maharaja Agrasen Himalayan Garhwal University, Pauri Garhwal, Uttarakhand,
  • Raghav Agarwal Independent Researcher , Mangal Pandey Nagar, Meerut (U.P.) India 250002,

DOI:

https://doi.org/10.36676/jrps.v11.i4.1583

Keywords:

Data pipeline optimization, GPU architectures, memory management, parallel execution, data transfer bottlenecks, task scheduling

Abstract

Optimizing data pipeline performance in modern GPU architectures is critical for achieving high computational throughput and efficient resource utilization in data-intensive applications. With the rise of deep learning, scientific simulations, and real-time analytics, GPUs have become integral in accelerating data processing tasks. However, ensuring optimal performance involves addressing several challenges, such as memory bandwidth limitations, data transfer bottlenecks between CPU and GPU, and efficient parallel execution of workloads.

This research explores techniques for improving data pipeline performance by focusing on memory management, load balancing, and task scheduling. One key strategy is optimizing data movement through techniques like memory coalescing, which minimizes access latency, and overlapping data transfers with computation. Furthermore, leveraging the architectural advances in modern GPUs, such as unified memory and NVLink, can significantly reduce data transfer overhead. Task parallelism and efficient workload distribution across multiple GPU cores also play a crucial role in enhancing pipeline throughput.

Additionally, the study highlights the importance of tuning GPU kernels and optimizing data preprocessing steps to ensure minimal latency and maximum throughput. By adopting advanced profiling tools and performance monitoring techniques, bottlenecks can be identified, and pipeline optimization strategies can be fine-tuned. The findings presented provide a comprehensive approach for designing and optimizing data pipelines, leading to significant performance improvements in GPU-based systems, ultimately driving the next generation of high-performance computing applications.

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Cherukuri, H., Pandey, P., & Siddharth, E. (2020). Containerized data analytics solutions in on-premise financial services. International Journal of Research and Analytical Reviews (IJRAR), 7(3), 481-491. https://www.ijrar.org/papers/IJRAR19D5684.pdf

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Eeti, E. S., Jain, E. A., & Goel, P. (2020). Implementing data quality checks in ETL pipelines: Best practices and tools. International Journal of Computer Science and Information Technology, 10(1), 31-42. Available at: http://www.ijcspub/papers/IJCSP20B1006.pdf

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Published

31-12-2020

How to Cite

Ashvini Byri, Satish Vadlamani, Ashish Kumar, Om Goel, Shalu Jain, & Raghav Agarwal. (2020). Optimizing Data Pipeline Performance in Modern GPU Architectures. International Journal for Research Publication and Seminar, 11(4), 302–318. https://doi.org/10.36676/jrps.v11.i4.1583

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