High-Performance Data Computing: Parallel Frameworks, Execution Strategies, and Real-World Deployments

Prudhvi Naayini , Srikanth Kamatala

Abstract

The accelerating growth of data volume and complexity has made high-performance computing (HPC) indispensable in modern data processing. This paper offers a thorough exploration of high-performance data computing, examining foundational concepts, execution strategies, and widely used frameworks such as MPI, OpenMP, CUDA, Hadoop MapReduce, and Apache Spark. We present key hardware and software architectures that power both scientific computing and big data analytics. Through comparative insights and illustrative diagrams, we analyze shared vs. distributed memory systems, parallel speedup models, and fault-tolerant frameworks. Real-world deployments ranging from climate simulations to social media analytics demonstrate how parallelism enables scalability, speed, and resilience in data-intensive environments. We conclude with emerging trends in hybrid architectures, GPU acceleration, and convergence of HPC and big data ecosystems. This survey serves as a practical reference for researchers and practitioners building the next generation of scalable data computing systems.

Keywords

High-performance computing; Data-intensive computing; Parallel frameworks; Big data analytics; Distributed systems; GPUs; MPI; Apache Spark.

Cite This Article

Naayini, P., Kamatala, S. (2023). High-Performance Data Computing: Parallel Frameworks, Execution Strategies, and Real-World Deployments. International Journal of Scientific Advances (IJSCIA), Volume 4| Issue 6: Nov-Dec 2023, Pages 1056-1064, URL: https://www.ijscia.com/wp-content/uploads/2025/04/Volume4-Issue6-Nov-Dec-No.541-1056-1064.pdf

Volume 4 | Issue 6: Nov-Dec 2023