This paper describes PlinyCompute, a system for development of high-performance, data-
intensive, distributed computing tools and libraries.\emphIn the large, PlinyCompute …

Mobile applications based on geospatial data are nowadays extensively used to support
people's daily activities. Despite the potential overlap among nearby users' geospatial data …

High-performance real-time monitoring of the stock market data stream is one of the
challenging use cases of stream processing systems. By monitoring real-time stock price …

Several systems, most notably TensorFlow and PyTorch, have revolutionized how we
practice machine learning (ML). They allow an ML practitioner to create complex models …

In this paper we experimentally analyze the convergence behavior of CoCoA and show, that
the number of workers required to achieve the highest convergence rate at any point in time …

In recent years ML models have been growing rapidly in terms of number of parameters. For
example, the large transformer model GPT-1 debuted with an impressive 117 million …

Parallel training of a machine learning model on a computerized system is described.
Computing tasks of a system can be assigned to multiple workers of the system. Training …

Parallel training of a machine learning model on a computerized system may be provided.
Computing tasks can be assigned to multiple workers of a system. A method may include …

Distributed systems have become increasingly more common. In these systems, multiple
nodes communicate with each other, typically over a network, which requires serialization of …

The shark crisis in the island of La Reunion requires innovative measures to secure sensible
surfing and recreation shores. The CRA research center is developing a detection algorithm …