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Ahmad, T. (author), Ma, Chengxin (author), Al-Ars, Z. (author), Hofstee, H.P. (author)
Current cluster scaled genomics data processing solutions rely on big data frameworks like Apache Spark, Hadoop and HDFS for data scheduling, processing and storage. These frameworks come with additional computation and memory overheads by default. It has been observed that scaling genomics dataset processing beyond 32 nodes is not efficient on...
conference paper 2022
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Ahmad, T. (author), Ahmed, N. (author), Peltenburg, J.W. (author), Al-Ars, Z. (author)
The rapidly growing size of genomics data bases, driven by advances in sequencing technologies, demands fast and cost-effective processing. However, processing this data creates many challenges, particularly in selecting appropriate algorithms and computing platforms. Computing systems need data closer to the processor for fast processing....
conference paper 2020