  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.
