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Abstract:

For scientists and engineers, large-scale computer systems

are one of the most powerful tools to solve complex high-performance

computing (HPC) problems, such as large-scale machine learning,

cosmological simulation, climate change, water management, and vaccine

and drug design. With the ever-increasing computing power, such as the

new generation of exascale (one exaflop or a billion billion

calculations per second) supercomputers, the gap between computing

power and limited storage capacity and I/O bandwidth has become a

major challenge for scientists and engineers. This talk will introduce

predictable and reliable data reduction techniques for scaling HPC

Applications, including machine learning and scientific applications.

The talk will cover how we design and leverage the lossy compression

to advanced HPC and ML systems (e.g., GPU-based heterogeneous systems)

and improve the performance for large-scale data processing

applications (e.g., HPC simulations and ML model training).

Bio:

Sian Jin is a Ph.D. Candidate in the Department of Intelligent

Systems Engineering at Indiana University, under the supervision of

Prof. Dingwen Tao. He received his bachelor degree in physics from

Beijing Normal University in 2018. His research interest falls in

High-performance computing (HPC) data reduction & lossy compression

for improving the performance of scientific data analytics &

management, as well as for large-scale machine learning & deep

learning. Six of his Ph.D. studies have been published as first-author

papers in prestigious conferences, including SC, VLDB, ICDE, HPDC, and

IPDPS.

Zoom link for the talk: https://www.cs.wm.edu/zoom

This Event Appears On

• Arts & Sciences Events
• Computer Science Events