For decades, high-performance computing (HPC) has played a crucial role in advancing healthcare. Enabled by techniques like next-generation sequencing (NGS), modern computer systems have helped us understand the biology of disease and helped facilitate the development of newer more effective drugs. While once deployed mainly in research, today HPC is edging ever closer to front-line clinical care supporting advanced diagnostics, genetic counseling, and personalized medicine.
While core bioinformatics applications aren’t going anywhere, modern medicine is increasingly a big data problem as well. Determining the optimal treatment depends on collaboration, the fusion of genetic data with other rich datasets like MRIs, ultrasound images, and population level studies. Increasingly analytics and AI techniques like machine learning are helping healthcare providers sift through massive amounts of data to make better decisions for patients more quickly and efficiently.
For IT organizations, providing a foundation to support this rapid innovation has been challenging. With the need for increasingly specialized hardware, new application frameworks, cost challenges, and the need to share data as never before, institutions are increasingly embracing cloud computing. Univa has played a key role in helping life sciences companies manage diverse workloads on HPC clusters both on-premises and in the cloud. In this Podcast, Univa will discuss how workloads are evolving, share some of the pressures faced by their life-sciences customers, and discuss practical approaches to modernizing IT environments to support the increased variety of applications in the life sciences software ecosystem.
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