Optimizing Memory Access in Modern Computing: Applications in Machine Learning and Big Data Workloads

Pradeep Kumar

doi:10.5281/zenodo.14993265

Optimizing Memory Access in Modern Computing: Applications in Machine Learning and Big Data Workloads

Author(s)	Pradeep Kumar
Country	United States
Abstract	Memory access has emerged as a critical bottleneck in modern computing systems, especially in the context of machine learning and big data workloads, where high computational demands often overwhelm memory subsystems. The widening performance gap between processors and memory, driven by slower improvements in memory technologies compared to CPUs, underscores the need for innovative optimization strategies (Hennessy & Patterson, 2017, p. 87). This research explores methods for optimizing memory access to enhance system performance, reduce latency, and improve resource efficiency in diverse computational environments. Key techniques investigated include the design of cache-friendly data structures to leverage temporal and spatial locality, dynamic memory allocation strategies to minimize overhead, and the use of large memory pages to mitigate translation lookaside buffer (TLB) misses (Drepper, 2007, p. 12). The study also evaluates the effectiveness of explicit and hardware-driven memory prefetching to reduce cache miss penalties and examines methods for addressing memory bandwidth limitations in high-demand systems. Applications in machine learning and big data are analyzed, focusing on tasks like neural network training, large-scale data aggregation, and distributed computing. Empirical results demonstrate that optimized memory access patterns can reduce latency by up to 40% in typical machine learning workloads and improve throughput in data-intensive systems by 30% (Alpaydin, 2020, p. 145). Additionally, the findings highlight the potential of emerging technologies, such as DDR5 and persistent memory, to address current challenges in memory subsystems. This research contributes to the design of scalable and energy-efficient systems, providing a foundation for optimizing memory access across a range of applications. Future work will focus on hybrid approaches that integrate software- and hardware-level solutions to address the evolving demands of modern computing environments.
Keywords	Memory Access Optimization, Machine Learning Workloads, Big Data Processing, Dynamic Memory Allocation, High-Performance Computing, Cache Optimization
Field	Engineering
Published In	Volume 12, Issue 1, January-June 2021
Published On	2021-02-03
Cite This	Optimizing Memory Access in Modern Computing: Applications in Machine Learning and Big Data Workloads - Pradeep Kumar - IJAIDR Volume 12, Issue 1, January-June 2021. DOI 10.5281/zenodo.14993265
DOI	https://doi.org/10.5281/zenodo.14993265
Short DOI	https://doi.org/g87gcm

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Optimizing Memory Access in Modern Computing: Applications in Machine Learning and Big Data Workloads

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