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Bodo DataFrames: HPC-based Drop-in Pandas Replacement
Pandas is a popular library for data scientists but it struggles with large datasets; programs either become too slow or run out of memory. In this talk, Ehsan Totoni (Bodo.ai) will introduce Bodo DataFrames as a drop-in replacement for the pandas library that uses high performance computing (HPC) based techniques such as Message Passing Interface (MPI) and JIT compilation for acceleration and scaling. Ehsan will give an overview of its architecture and explain how it avoids the problems of pandas (while keeping user code the same), go over concrete examples, and finally discuss current limitations. This talk is for pandas users who would like to run their code on larger data while avoiding frustrating code rewrites to other APIs.
About the Speaker
Ehsan Totoni is an entrepreneur, computer science researcher, and software engineer working on democratization of High Performance Computing (HPC) for data engineering, data science, and AI/ML. He received his PhD in Computer Science from the University of Illinois at Urbana-Champaign, working on various aspects of HPC and Parallel Computing. Ehsan then worked as a research scientist at Intel Labs and Carnegie Mellon University, focusing on programming systems to address the gap between programmer productivity and computing performance. He co-founded Bodo.ai in 2019 and is advancing Bodo’s mission of bringing HPC to all data applications.
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