Scalability and Productivity in Genomics on Massively Parallel Systems

Center for Research on Programmable Plant Systems (CROPPS) Seminar

Speaker: Giulia Guidi

Date and Time: August 27 from 12:20 pm to 1:10 pm EDT

Location: 135 Emerson Hall and on Zoom

Abstract: The use of massively parallel systems continues to be crucial for processing large volumes of data at unprecedented speed and for scientific discoveries in simulation-based research areas. Today, these systems play a crucial role in new and diverse areas of data science, such as computational biology and data analytics. Computational biology is a key area where data processing is growing rapidly. The growing data volume and complexity have outpaced the processing capacity of single-node machines in these areas, making massively parallel systems an indispensable tool.

The diverse and non-trivial challenges of parallelism in these areas require computing infrastructures that go beyond the demand of traditional simulation-based sciences. However, programming on high-performance computing (HPC) systems poses significant productivity and scalability challenges. It is important to introduce an abstraction layer that provides programming flexibility and productivity while ensuring high system performance. As we enter the post-Moore’s Law era, effective programming of specialized architectures is critical for improved performance in HPC. As large-scale systems become more heterogeneous, their efficient use for new, often irregular, and communication-intensive data analysis computation becomes increasingly complex. In this talk, we discuss how to achieve performance and scalability on extreme-scale systems while maintaining productivity for new data-intensive biological challenges.

Bio: Giulia Guidi is an Assistant Professor in the Department of Computer Science and a Graduate Field Faculty in the Department of Computational Biology and the Center for Applied Math at Cornell. She received her PhD in Computer Science from UC Berkeley. She works in the field of high-performance computing for large-scale computational sciences (in particular, computational biology). She is interested in the development of algorithms and software infrastructures on parallel machines to accelerate data processing without sacrificing programming productivity and to make high-performance computing more accessible.

This seminar is supported by CROPPS and the School of Integrative Plant Science (SIPS)