DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines

Patrick Damme, Matthias Boehm, Mark Dokter, Kevin Innerebner, Roman Kern

Research output: Contribution to conferencePaperpeer-review


Integrated data analysis (IDA) pipelines---that combine data management (DM) and query processing, high-performance computing (HPC), and machine learning (ML) training and scoring---become increasingly common in practice. Interestingly, systems of these areas share many compilation and runtime techniques, and the used---increasingly heterogeneous---hardware infrastructure converges as well. Yet, the programming paradigms, cluster resource management, data formats and representations, as well as execution strategies differ substantially. DAPHNE is an open and extensible system infrastructure for such IDA pipelines, including language abstractions, compilation and runtime techniques, multi-level scheduling, hardware (HW) accelerators, and computational storage for increasing productivity and eliminating unnecessary overheads. In this paper, we make a case for IDA pipelines, describe the overall DAPHNE system architecture, its key components, and the design of a vectorized execution engine for computational storage, HW accelerators, as well as local and distributed operations. Preliminary experiments that compare DAPHNE with MonetDB, Pandas, DuckDB, and TensorFlow show promising results
Original languageEnglish
Number of pages12
Publication statusPublished - 2022
Event12th Conference on Innovative Data Systems Research: CIDR 2022 - Hybrider Event, United States
Duration: 9 Jan 202212 Jan 2022


Conference12th Conference on Innovative Data Systems Research
Abbreviated titleCIDR 2022
Country/TerritoryUnited States
CityHybrider Event

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