Challenges and opportunities for RISC-V architectures towards genomics-based workloads

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. Current implementations are implemented in x86-based supercomputers (e.g. MareNostrum-IV at the Barcelona Supercomputing Center (BSC)), and future steps propose RISC-V as part of the next MareNostrum generations. Here we describe the Variant Interaction Use Case, highlighting the characteristics leveraging high-performance computing, indicating the caveats and challenges towards the next RISC-V developments and designs to come from a first comparison between x86 and RISC-V architectures on real Variant Interaction executions over real hardware implementations.
This work has been partially financed by the European Commission (EU-HORIZON NEARDATA GA.101092644, VITAMIN-V GA.101093062), the MEEP Project which received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 946002. The JU receives support from the European Union’s Horizon 2020 research and innovation program and Spain, Croatia and Turkey. Also by the Spanish Ministry of Science (MICINN) under scholarship BES-2017-081635, the Research State Agency (AEI) and European Regional Development Funds (ERDF/FEDER) under DALEST grant agreement PID2021-126248OBI00, MCIN/AEI/10.13039/ 501100011033/FEDER and PID GA PID2019-107255GB-C21, and the Generalitat de Catalunya (AGAUR) under grant agreements 2021-SGR-00478, 2021-SGR-01626 and ”FSE Invertint en el teu futur”.
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