2022

• Patrick Damme, Marius Birkenbach, Constantinos Bitsakos, Matthias Boehm, Philippe Bonnet, Florina Ciorba, Mark Dokter, Pawel Dowgiallo, Ahmed Eleliemy, Christian Faerber, Georgios Goumas, Dirk Habich, Niclas Hedam, Marlies Hofer, Wenjun Huang, Kevin Innerebner, Vasileios Karakostas, Roman Kern, Tomaž Kosar, Alexander Krause, Daniel Krems, Andreas Laber, Wolfgang Lehner, Eric Mier, Marcus Paradies, Bernhard Peischl, Gabrielle Poerwawinata, Stratos Psomadakis, Tilmann Rabl, Piotr Ratuszniak, Pedro Silva, Nikolai Skuppin, Andreas Starzacher, Benjamin Steinwender, Ilin Tolovski, Pınar Tözün, Wojciech Ulatowski, Aristotelis Vontzalidis, Yuanyuan Wang, Izajasz Wrosz, Aleš Zamuda, Ce Zhang, Xiao Xiang Zhu: “DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines.” (Conference on Innovative Data Systems Research, CIDR, 2022) [Reproducibility Scripts for Daphne CIDR2022 Submission]
• Ali Mohammed, Jonas H. Müller Kornörfer, Ahmed Eleliemy, Florina M. Ciorba: “Automated Scheduling Algorithm Selection and Chunk Parameter Calculation in OpenMP” (IEEE Transactions on Parallel and Distributed Systems)
• Benson Lawrence, Papke Leon, Rabl Tilmann: “PerMA-Bench: Benchmarking Persistent Memory Access” (Proceedings of the Very Large Data Base Endowment (VLDB) Endowment)
• Andreas Laber, Martin Gebser, Konstantin Schekotihin, Yao Yang: “Predicting Ion Beam Tuning in Semiconductor Manufacturing” (IEEE Electron Devices Society)
• Robert Bayer, Jon Voigt Tøttrup, and Pınar Tözün: “TPCx-AI on NVIDIA Jetsons” (Proceedings of the Fourteenth TPC Technology Conference on Performance Evaluation & Benchmarking)
• Chloe Averti, Vasileios Karakostas, Nikhita Kunati, Georgios Goumas, Michael Swif: “DaxVM: Stressing the Limits of Memory as a File Interface”(MICRO 2022 – 55th IEEE/ACM International Synopsium on Microarchitecture)
• Jonas H. Müller Korndörfer, Ahmed Eleliemy, Ali Mohammed, Florina M. Ciorba: “LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications” (IEEE Transactions on Parallel and Distributed Systems, Vol. 33, April 2022)
• Nils Strassenburg, Ilin Tolovski, Tilmann Rabl: “Efficiently Managing Deep Learning Models in a Distributed Environment” (25th International Conference on Extending Database Technology (EDBT))
• Tobias Maltenberger, Till Lehmann, Lawrence Benson, Tilmann Rabl: “Evaluating In-Memory Hash Joins on Persistent Memory” (25th International Conference on Extending Database Technology (EDBT))
• Lawrence Benson, Tilmann Rabl: “Darwin: Scale-In Stream Processing” (Conference on Innovative Data Systems Research, CIDR 22)
• Tobias Maltenberger, Ivan Ilic, Ilin Tolovski, Tilmann Rabl: “Evaluating Multi-GPU Sorting with Modern Interconnects” (Proceedings of the 2022 International Conference on Management of Data (SIGMOD ’22))
• Mikkel Møller Andersen, Pınar Tözün: “Micro-architectural Analysis of a Learned Index” (Proceedings of the International Workshop on Exploiting Artificial Intelligence Techniques for Data Management)
• Utku Sirin, Pınar Tözün, Danica Porobic, Ahmad Yasin, Anastasia Ailamaki: “Micro-architectural analysis of in-memory OLTP: Revisited” (Springer, The VLDB Journal – The International Journal on Very Large Data Bases)
• Simon Lund, Philippe Bonnet, Klaus Jensen, Javier Gonzalez: “I/O Interface Independence with xNVMe” (Proceedings of the 15th ACM International Systems and Storage Conference)
• Aleš Zamuda: “Speeding up Vectorized Benchmarking of Optimization Algorithms” (Austrian-Slovenian HPC Meeting 2022 – ASHPC)
• Andreas Laber, Martin Gebser, Konstantin Schekotihin, Yao Yang: “Predicting Ion Beam Tuning in Semiconductor Manufacturing” (IEEE Electron Devices Society)

2021

• Xiao Xiang Zhu, Chunping Qiu, Jingliang Hu, Yilei Shi, Yuanyuan Wang, Michael Schmitta, Hannes Taubenböck: “The urban morphology on our planet – Global perspectives from space” (Remote Sensing of Environment, Elsevier Inc., 2021)
• Jonas H. Müller Kornörfer, Ahmed Eleliemy, Ali Mohammed, Florina M. Ciorba: “LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications” (IEEE Transactions on Parallel and Distributed Systems, 2021)
• Nataša Ošep Ferš, Aleš Zamuda: “Considering a Fear and Greed Index in Bitcoin Price Prediction Through Long Short-Term Memory”(Proceedings of the 30th International Electrotechnical and Computer Science Conference ERK, IEEE Slovenia Section, 2021)
• Ahmed Eleliemy, Florina M. Ciorba: “Don’t Compete, Let’s Cooperate: A Cooperative Scheduling Approach” (Platform for Advancing Scientific Computing Conference, 2021)
• Ali Mohammed, Ahmed Eleliemy, Jonas H. Müller Korndörfer, Rubén M. Cabezón, Florina M. Ciorba: “Single- and Two-Level Dynamic Load Balancing of Scientific Applications” (Platform for Advancing Scientific Computing Conference, 2021)
• Björn Daase, Lars Jonas Bollmeier, Lawrence Benson, Tilmann Rabl: “Maximizing Persistent Memory Bandwidth Utilization for OLAP Workloads” (International Conference on Management of Data, ACM SIGMOD/PODS, 2021)
• Alberto Lerner, Philippe Bonnet: “Not your Grandpa’s SSD: The Era of Co-Designed Storage Devices” (International Conference on Management of Data, ACM SIGMOD/PODS, 2021)
• Jiawei Jiang, Shaoduo Gan, Yue Liu, Fanlin Wang, Gustavo Alonso, Ana Klimovic, Ankit Singla, Wentao Wu, Ce Zhang: “Towards Demystifying Serverless Machine Learning Training” (International Conference on Management of Data, ACM SIGMOD/PODS, 2021)
• Aleš Zamuda: “Parallelization of benchmarking using HPC: text summarization in natural language processing (NLP), glider piloting in deep-sea missions, and search algorithms in computational intelligence (CI)” (Austrian-Slovenian HPC Meeting 2021 – ASHPC21)
• Lawrence Benson, Hendrik Makait, Tilmann Rabl: “Viper: An Efficient Hybrid PMem-DRAM Key-Value Store” (Proceedings of the Very Large Data Base Endowment (VLDB) Endowment, Vol. 14, No. 9, 2021)
• Jaeyoung Do, Ivan Luiz Picoli, David Lomet, Philippe Bonnet: “Better Database Cost/Performance via Batched I/O on Programmable SSD” (Conference on Very Large Data Bases, VLDB, Journal, 2021)
• Shaoduo Gan, Xiangru Lian, Rui Wang, Jianbin Chang, Chengjun Liu, Hongmei Shi, Shengzhuo Zhang, Xianghong Li, Tengxu Sun, Jiawei Jiang, Binhang Yuan, Sen Yang, Ji Liu, Ce Zhang: “BAGUA: Scaling up Distributed Learning with System Relaxations” (PVLDB, 2021)
• Yang Li, Yu Shen, Wentao Zhang, Jiawei Jiang, Bolin Ding, Yaliang Li, Jingren Zhou, Zhi Yang, Wentao Wu, Ce Zhang, Bin Cui: “VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition” (PVLDB, 2021)
• Ahmed Eleliemy, Florina M. Ciorba: “A Resourceful Coordination Approach for Multilevel Scheduling” (International Conference on High Performance Computing & Simulation, HPCS, 2021)
• Leonel Aguilar, David Dao, Shaoduo Gan, Nezihe Merve Gurel, Nora Hollenstein, Jiawei Jiang, Bojan Karlas, Thomas Lemmin, Tian Li, Yang Li, Susie Rao, Johannes Rausch, Cedric Renggli, Luka Riminik, Maurice Weber, Shuai Zhang, Zhikuan Zhao, Kevin Schawinski, Wentao Wu, Ce Zhang: “Ease.ML: A Lifecycle Management System for MLDev and MLOps” (Conference on Innovative Data Systems Research, CIDR, 2021)
• Maximilian Böther, Otto Kißig, Lawrence Benson, Tilmann Rabl: “Drop It In Like It’s Hot: An Analysis of Persistent Memory as a Drop-in Replacement for NVMe SSDs” (International Workshop on Data Management on New Hardware – DAMON’21)
• Nina Ihde, Paula Marten, Ahmed Eleliemy, Gabrielle Poerwawinata, Pedro Silva, Ilin Tolovski, Florina M. Ciorba, and Tilmann Rabl: “A Survey of Big Data, High Performance Computing, and Machine Learning Benchmarks” (Technology Conference on Performance Evaluation & Benchmarking, TPC, published by Springer, 2021)
  
• Miao Xupeng, Gürel Nezihe Merve, Ce Zhang, Wentao Han Zhichao, Li Bo, Min Wei, Rao Susie, Ren Hansheng, Shan Yinan, Shao Yingxia, Wang Yujie, Wu Fan, Xue Hui, Yang Yaming, Zhang Zitao, Zhao Yang, Zhang Shuai, Wang Yujing, Cui Bin: “DeGNN: Improving Graph Neural Networks with Graph Decomposition” (ACM – Association for Cumputer Machinery, Singapore)
• Rausch Johannes, Martinez Octavio, Bissig Fabian, Zhang Ce, Feuerriegel Stefan: “DocParser: Hierarchical Document Structure Parsing from Renderings” (Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD ’21))

 

2020

• Li Peng, Rao Xi, Jennifer Blase, Xu Chu, Yue Zhang, Ce Zhang: “CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks” (ETH Zurich, Institute for Computing Platforms)

 

Winter 2022/23

These deliverables have been submitted but are not yet accepted, they might be revised after the next review period.

D1.4 2^nd Annual Project Report

In D1.4 DAPHNE project team describe the progress made until project month 24 and here particularly the work done in project year 2. This report presents an overview of the type and purpose of the document, its revision history, the strategic objectives of DAPHNE project and the work carried out in project year 2 to reach these objectives.

D4.2 DSL Runtime Prototype

In this deliverable we demonstrate the use of the DAPHNE runtime by sharing a snapshot of the DAPHNE prototype, and provide an example scenario of running simple DaphneDSL scripts on the local runtime as well as on the distributed runtime.

D5.2 Prototype of Pipelines and Task Scheduling Mechanisms

This document describes the use of the pipeline and task scheduling mechanisms currently supported in the DAPHNE system. As discussed in previous deliverable reports [D4.1, D5.1], the DAPHNE project team continuously refines, extends, and adds scheduling mechanisms in DAPHNE. Nevertheless, extensibility is at the heart of the DAPHNE system.

D6.2 Prototype and Overview of Managed Storage Tiers and Near-Data Processing

This deliverable describes the design of the Delilah prototype for offloading eBPF code on a computational storage platform. We detail its implementation on the Daisy computational storage platform and discuss its initial performance evaluation.

Click here for direct access to download the demo

D7.2 Prototype and Overview HW Accelerator Support and Performance Models

This document describes a snapshot of our DAPHNE prototype regarding HW acceleration as a follow-up of deliverable D7.1. In particular, we introduce three example scenarios as the main drivers for the current prototype and describe how to execute these examples with enhanced hardware support.

D9.2 Initial Benchmark Concept and Definition

In D9.2, HPI presents the initial concept and definition of the benchmark for the DAPHNE system.

Summer 2022

D2.2 Refined System Architecture

D2.2 is based on D2.1 Initial System Architecture (see below) and illustrated how this initial system architecture is retained. On top of that we have integrated earlier lessons learned and provide more details on several components.

D7.1 Design of integration HW accelerators

In D7.1, the Daphne project team reports on the planned overall design of integration HW accelerators as well as details on accelerated operations and primitives, as well as its compiler and runtime support.

Winter 2021/2022

D1.3 First Annual Project Report

This report summarizes the work carried out throughout the first year of the Daphne project, as well as the progress and initial results obtained so far. In detail, we share the overall work and progress towards the objectives, status of groups of work packages, and impact.
Additionally, this report also includes an update of the exploitation and dissemination plan, as well as the research data management plan.

D3.1 Language Design Specification

This report on the language design specification summarizes the design of DAPHNE’s domain-specific language (DSL) and related user-facing application programming interfaces (APIs).

D3.2 Compiler Prototype

This document shares a snapshot of this prototype, and describes an example scenario of running regression (and similarly classification, clustering, dimensionality reduction, and graph processing) algorithms on real datasets. The initial open source release of the DAPHNE prototype system is planned for end of March 2022.

D4.1 DSL Runtime Design

In this report the initial design of the Daphne runtime system and its prototype implementation are addressed. Moreover, the support for distribution primitives and the integration with existing frameworks are outlined.

D5.1 Scheduler Design for Pipelines and Tasks

This report describes the Daphne system based scheduling context and decisions as well as the preliminary design of the Daphne scheduler for pipelines and tasks.

D6.1 Computational Storage Capabilities

This report explains how compute and storage interact within storage devices and across a cluster, examining  the recent evolutions in storage architectures and programming abstractions.

Summer 2021

D2.1 Initial System Architecture

This report on the initial system architecture summarizes the preliminary design of the Daphne system infrastructure.

D8.1 Initial Pipeline Definition of all Use Cases

This report describes the five use case pipelines of earth observation, semiconductor manufacturing, material degradation and automotive vehicle development, whereas one is focussing on ejector geometry observation and the latter on virtual prototype development.