Challenges and Opportunities for RISC-V Architectures towards Genomics-based Workloads
Gonzalo Gomez-Sanchez, Aaron Call, Xavier Teruel, Lorena Alonso,, Ignasi Moran, Miguel Angel Perez, David Torrents, Josep Ll. Berral

TL;DR
This paper evaluates the potential of RISC-V architectures for genomics workloads, specifically using a benchmarking suite for Variant-Interaction Analytics, comparing performance with x86 supercomputers, and discussing future RISC-V developments.
Contribution
It introduces a genomics benchmarking suite for RISC-V, compares RISC-V and x86 architectures on real workloads, and discusses challenges and opportunities for RISC-V in scientific computing.
Findings
RISC-V shows promise for genomics workloads.
Performance comparison highlights current gaps and potential improvements.
Identifies key challenges for RISC-V adoption in high-performance computing.
Abstract
The use of large-scale supercomputing architectures is a hard requirement for scientific computing Big-Data applications. An example is genomics analytics, where millions of data transformations and tests per patient need to be done to find relevant clinical indicators. Therefore, to ensure open and broad access to high-performance technologies, governments, and academia are pushing toward the introduction of novel computing architectures in large-scale scientific environments. This is the case of RISC-V, an open-source and royalty-free instruction-set architecture. To evaluate such technologies, here we present the Variant-Interaction Analytics use case benchmarking suite and datasets. Through this use case, we search for possible genetic interactions using computational and statistical methods, providing a representative case for heavy ETL (Extract, Transform, Load) data processing.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBioinformatics and Genomic Networks · Gene expression and cancer classification · Machine Learning in Bioinformatics
