Life science research has long been compute-intensive but requirements have largely been satisfied with traditional workstations and simple clusters. That’s changing: “Roughly 25 percent of life scientists, and this includes bench-level scientists, will require HPC capabilities in 2015, few of whom have ever used a command line,” said Ari Berman, GM of Government Services, the BioTeam consulting firm.
Predictably, the flood of DNA sequence data is a major driver. NIH now generates 1.5PB of data a month, and that is only from internal work and doesn’t include NIH-funded external research. “[This might be the] first real case in life science where 100Gb networking might be really needed,” said Berman.
However there are many contributors to the growing data flood and computing complexity in LS including, for example, proteomic data, protein structure data, cell and organelle imaging data, pathway modeling data, and efforts to integrate all of them for analysis.