Marko Kvakić is a meteorologist by training, and his first experience with high performance computing was during his studies at the Geophysics Department of the Faculty of Science in Zagreb, where he focused on the numerical simulation of weather using meteorological models.
He holds a PhD from the University of Bordeaux, France, and his research experience involves computer simulations applied to climatology and biogeochemistry.
Marko works at the University of Zagreb University Computing Centre SRCE, in the Advanced Computing Department, where he provides support to scientists who want to harness the power of supercomputing in their machine learning applications.
He is also continuously working on working on HPC popularization and developing HPC learning materials with his colleagues.
RAPIDS: GPU-powered data science
Data science and machine learning is one of the world’s largest compute segment, in which small improvements in accuracy can translate into huge returns. To build the best models, data scientists work to train, evaluate, iterate, and retrain for highly accurate results and performant models.
Want to learn how to engineer features and fit trees at lightning speed? This workshop will focus on performing data processing and machine learning using RAPIDS: a GPU-powered framework that enables massively parallel computations in typical data science workflows. Based on the familiar scikit-learn & pandas API, RAPIDS allows for an easy transition to GPU-powered systems and provides significant speed-ups in data processing and model development. With RAPIDS, processes that took days take minutes, making it easier and faster to build and deploy value-generating models.
The workshop will include a hands-on demonstration of RAPIDS capabilities applied to a publicly available dataset (NYC Taxi Fares) and be performed on the University Computing Centre’s (SRCE) new cloud service “Vrančić”, with a GPU server housing four A100 GPUs.