Evaluating Energy Efficiency of Genomics Algorithms on Processing-in-Memory Architectures
Résumé
Processing-in-Memory (PiM) is a novel computing paradigm for reducing data movements between memory and processing units, and thus minimizing energy consumption. PiMs are particularly well-suited to data-intensive applications, where traditional systems are often limited by memory bandwidth. Genomics is a representative example of such a domain, involving massive datasets and repetitive access patterns. In this paper, we evaluate the energy efficiency improvements achieved by running several genomic algorithms on a PiM-based system. Our experiments focus on realistic workloads and highlight the challenges and opportunities of parallelizing genomic tasks for PiM. The most significant gains are observed in large-scale database search applications, which naturally map to the parallel structure of PiM and benefit greatly from reduced data movement.
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