This paper discusses the I-WAY project and provides an overview of the papers in this issue of IJSA. The I-WAY is an experimental environment for building distributed vir tual reality applications and for exploring issues of distrib uted wide-area resource management and scheduling. The goal of the I-WAY project is to enable researchers to use multiple internetworked supercomputers and ad vanced visualization systems to conduct very large scale computations. By connecting 12 ATM testbeds, 17 super computer centers, 5 virtual reality research sites, and over 60 applications groups, the I-WAY project has created an extremely diverse wide-area environment for exploring advanced applications. This environment has provided a glimpse of the future for advanced scientific and engineer ing computing.

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Our work seeks to transform how new and emergent variants of pandemic causing viruses, specially SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pre-training on over 110 million prokaryotic gene sequences, and then finetuning a SARS-CoV-2 specific model on 1.5 million genomes, we show that GenSLM can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLM represents one of the first whole genome scale foundation models which can generalize to other prediction tasks. We demonstrate the scaling of GenSLMs on both GPU-based supercomputers and AI-hardware accelerators, achieving over 1.54 zettaflops in training runs. We present initial scientific insights gleaned from examining GenSLMs in tracking the evolutionary dynamics of SARS-CoV-2, noting that its full potential on large biological data is yet to be realized.

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