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EDBT 2026 Demo / reviewers in the wild / expert

Luca Lavarini

dblp:252/5519 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
High-performance computing · 78% Parallel and multicore computing · 17% Reconfigurable computing and FPGAs · 5%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › performance engineering
performance portability		 1.422025
Productivity, Portability, Performance, and Reproducibility: Data-Centric Python · IEEE Trans. Parallel Distributed Syst. 2025
Productivity, portability, performance: data-centric Python · SC 2021
Compilers and program optimization
automatic code optimization		 0.312025
Productivity, Portability, Performance, and Reproducibility: Data-Centric Python · IEEE Trans. Parallel Distributed Syst. 2025
Reconfigurable computing and FPGAs
FPGA accelerator		 0.112021
Productivity, portability, performance: data-centric Python · SC 2021

Methods — techniques the papers use, named apart from their topics

automatic optimization · 2.2data-centric intermediate representation · 1.7HPC-oriented language extensions · 0.5
YearPublicationVenuePosition
2025 Productivity, Portability, Performance, and Reproducibility: Data-Centric Python
abstract
Python has become thede factolanguage for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High-Performance Computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. This work presents a workflow that retains Python’s high productivity while achieving portable performance across different architectures. The workflow’s key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We show performance results and scaling across CPU, GPU, FPGA, and the Piz Daint supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated Python, and up to 93.16% scaling efficiency on 512 nodes. Our benchmarks were reproduced in the Student Cluster Competition (SCC) during the Supercomputing Conference (SC) 2022. We present and discuss the student teams’ results.
Alexandros Nikolaos Ziogas, Timo Schneider, Tal Ben-Nun, Alexandru Calotoiu, Tiziano De Matteis, Johannes de Fine Licht, Luca Lavarini, Torsten Hoefler
 IEEE Trans. Parallel Distributed Syst.7
2021 Productivity, portability, performance: data-centric Python
abstract
Python has become the de facto language for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High Performance Computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. In this work, we present a workflow that retains Python's high productivity while achieving portable performance across different architectures. The workflow's key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We show performance results and scaling across CPU, GPU, FPGA, and the Piz Daint supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated Python, and up to 93.16% scaling efficiency on 512 nodes.
Alexandros Nikolaos Ziogas, Timo Schneider, Tal Ben-Nun, Alexandru Calotoiu, Tiziano De Matteis, Johannes de Fine Licht, Luca Lavarini, Torsten Hoefler
 SC7