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.

References

Che, S., Xu, L., & Ponomarev, D. (2015). "Dynamic Memory Management for GPUs." IEEE Transactions on Parallel and Distributed Systems, 26(4), 977-989. DOI: 10.1109/TPDS.2014.2343260.

Stuart, R., & Owens, J. D. (2016). "Dynamic Parallelism in CUDA." In Proceedings of the 2016 IEEE International Conference on Computer Design (ICCD), 400-405. DOI: 10.1109/ICCD.2016.7879928.

Kwon, Y., Kim, J., & Lee, Y. (2017). "Reducing Memory Latency in GPU Architectures." ACM Transactions on Architecture and Code Optimization (TACO), 14(3), 1-25. DOI: 10.1145/3131398.

Jiao, Y., & Jin, Y. (2017). "Exploring Unified Virtual Memory for Programming and Performance." IEEE Computer Architecture Letters, 16(2), 165-168. DOI: 10.1109/LCA.2017.2700901.

Li, X., Zhang, Y., & Wu, Y. (2018). "Data Prefetching Techniques in GPUs: A Survey." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(4), 749-762. DOI: 10.1109/TCAD.2017.2779891.

Wang, Y., & Lu, G. (2018). "Optimizing Data Transfer and Computation Overlap in GPUs Using CUDA Streams." International Journal of Parallel Programming, 46(5), 1120-1140. DOI: 10.1007/s10766-018-0563-7.

Jiang, H., Zhang, Y., & Li, M. (2019). "Compiler-Driven Optimization for GPU Applications." Journal of Systems Architecture, 94, 29-43. DOI: 10.1016/j.sysarc.2019.02.008. DOI: https://doi.org/10.1016/j.sysarc.2019.02.008

Kumar, V., Chen, C., & Tiwari, M. (2019). "Load Balancing Techniques in Multi-GPU Systems." IEEE Transactions on Parallel and Distributed Systems, 30(3), 654-667. DOI: 10.1109/TPDS.2018.2867238.

Tan, M., Liu, Y., & Wang, Q. (2020). "Memory-Aware Scheduling for GPU Workloads." ACM Transactions on Architecture and Code Optimization (TACO), 17(2), 1-25. DOI: 10.1145/3397482. DOI: https://doi.org/10.1145/3397482

Zhu, L., & Ding, H. (2020). "Energy-Efficient GPU Optimization Techniques for High-Performance Computing." Journal of Supercomputing, 76(4), 2364-2382. DOI: 10.1007/s11227-019-03022-4.

Goel, P. & Singh, S. P. (2009). Method and Process Labor Resource Management System. International Journal of Information Technology, 2(2), 506-512.

Singh, S. P. & Goel, P., (2010). Method and process to motivate the employee at performance appraisal system. International Journal of Computer Science & Communication, 1(2), 127-130.

Goel, P. (2012). Assessment of HR development framework. International Research Journal of Management Sociology & Humanities, 3(1), Article A1014348. https://doi.org/10.32804/irjmsh DOI: https://doi.org/10.32804/IRJMSH

Goel, P. (2016). Corporate world and gender discrimination. International Journal of Trends in Commerce and Economics, 3(6). Adhunik Institute of Productivity Management and Research, Ghaziabad.

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. https://rjpn.org/ijcspub/papers/IJCSP20B1006.pdf

"Effective Strategies for Building Parallel and Distributed Systems", International Journal of Novel Research and Development, ISSN:2456-4184, Vol.5, Issue 1, page no.23-42, January-2020. http://www.ijnrd.org/papers/IJNRD2001005.pdf

"Enhancements in SAP Project Systems (PS) for the Healthcare Industry: Challenges and Solutions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 9, page no.96-108, September-2020, https://www.jetir.org/papers/JETIR2009478.pdf

Venkata Ramanaiah Chintha, Priyanshi, Prof.(Dr) Sangeet Vashishtha, "5G Networks: Optimization of Massive MIMO", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.7, Issue 1, Page No pp.389-406, February-2020. (http://www.ijrar.org/IJRAR19S1815.pdf )

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

Sumit Shekhar, SHALU JAIN, DR. POORNIMA TYAGI, "Advanced Strategies for Cloud Security and Compliance: A Comparative Study", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.7, Issue 1, Page No pp.396-407, January 2020. (http://www.ijrar.org/IJRAR19S1816.pdf )

"Comparative Analysis OF GRPC VS. ZeroMQ for Fast Communication", International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 2, page no.937-951, February-2020. (http://www.jetir.org/papers/JETIR2002540.pdf )

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. https://rjpn.org/ijcspub/papers/IJCSP20B1006.pdf

"Effective Strategies for Building Parallel and Distributed Systems". International Journal of Novel Research and Development, Vol.5, Issue 1, page no.23-42, January 2020. http://www.ijnrd.org/papers/IJNRD2001005.pdf

"Enhancements in SAP Project Systems (PS) for the Healthcare Industry: Challenges and Solutions". International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 9, page no.96-108, September 2020. https://www.jetir.org/papers/JETIR2009478.pdf

Venkata Ramanaiah Chintha, Priyanshi, & Prof.(Dr) Sangeet Vashishtha (2020). "5G Networks: Optimization of Massive MIMO". International Journal of Research and Analytical Reviews (IJRAR), Volume.7, Issue 1, Page No pp.389-406, February 2020. (http://www.ijrar.org/IJRAR19S1815.pdf)

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

Sumit Shekhar, Shalu Jain, & Dr. Poornima Tyagi. "Advanced Strategies for Cloud Security and Compliance: A Comparative Study". International Journal of Research and Analytical Reviews (IJRAR), Volume.7, Issue 1, Page No pp.396-407, January 2020. (http://www.ijrar.org/IJRAR19S1816.pdf)

"Comparative Analysis of GRPC vs. ZeroMQ for Fast Communication". International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 2, page no.937-951, February 2020. (http://www.jetir.org/papers/JETIR2002540.pdf)

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

Downloads

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