Session
Schedule FOSDEM 2022
HPC, Big Data, and Data Science

Bringing together open source scientific software development for HPC and beginners

<p>In the scope of the National Research Data Infrastructure Germany (NFDI) we develop and maintain a knowledge-base with guides and best practices about scientific software development – availabe at https://nfdi4ing.pages.rwth-aachen.de/knowledge-base/.</p> <p>With the knowledge-base we pursue the goal to make sustainable software development possible for everybody. Most importantly, we want to encourage people without strong computer science background to apply fundamental best practices, e.g., version control, from the start. We believe this is necessary since many engineers – and not computer scientists – write scientific code. Starting from version control, the topics range to fully automated experiments using CI/CD workflows. Many of the existing examples concern OpenFOAM development due to the knowledge-base’s heritage in TU Darmstadt’s CRC 1194. Since then, we also are working with the Lehrstuhl für Hydromechanik und Hydrosystemmodellierung (LH²) as well as with the Institute of Wasserbau and the Institute of Fluidsystemtechnik at TU Darmstadt.</p> <p>While curating the content, which we maintain in a Gitlab repository using the HUGO website generator, we realized that one of the most important additions that we provide is an actively maintained and simple glossary. Especially for people not yet very familiar with the technologies used, the pure number of terms can be intimidating and confusing. Our glossary helps by providing brief explanations of relevant terms used throughout the articles. An addition for which we modified the actual HUGO template is that we provide a taxonomy of articles rather than a pure folder structure to ease the navigation.</p> <p>The knowledge-base is an on-going effort in which we appreciate feedback and contributions. In the presentation, we will take a closer look on the different materials provided for people developing software, people using software, and how people can actively contribute to the knowledge base through our peer-review process and material creation.</p>

Additional information

Type devroom

More sessions

2/5/22
HPC, Big Data, and Data Science
Olena Kutsenko
D.hpc
<p>Working with Big Data means that we need tools to organise and understand the data. And you don’t have to be a developer to search, aggregate and visualise your data. Whether you need an affordable business analytics tool or you want to analyse log data in near real time, OpenSearch can help you. And all of it through a visual interface of OpenSearch Dashboards.</p> <p>After listening to this talk you’ll understand the basics of working with an OpenSearch cluster and different use cases ...
2/5/22
HPC, Big Data, and Data Science
Max Meldrum
D.hpc
<p>In this talk, I will present Arcon, a Rust-native streaming runtime that integrates seamlessly with the Apache Arrow ecosystem. The Arcon philosophy is streaming first, similarly to systems such as Apache Flink and Timely Dataflow. However, unlike all existing systems, Arcon features great flexibility when it comes to its application state. Arcon's TSS query language allows extracting and operating on state snapshots consistently based on application-time constraints and interfacing with ...
2/5/22
HPC, Big Data, and Data Science
D.hpc
<p>Any conversation about Big Data would be incomplete without talking about Apache Kafka and Apache Flink: the winning open source combination for high-volume streaming data pipelines.</p> <p>In this talk we'll explore how moving from long running batches to streaming data changes the game completely. We'll show how to build a streaming data pipeline, starting with Apache Kafka for storing and transmitting high throughput and low latency messages. Then we'll add Apache Flink, a distributed ...
2/5/22
HPC, Big Data, and Data Science
John Garbutt
D.hpc
<p>Why build #4 on the Green500 using OpenStack? It makes it easier to manage. Cambridge University started using OpenStack in 2015. Since mid 2020, all new hardware is controlled using OpenStack. Compute nodes, GPU nodes, Lustre nodes, Ceph nodes, almost everything. OpenStack allows large baremetal slurm clusters and dedicated TRE (trusted research environments) to share the same images. Is this a cloud native supercomputer?</p>
2/5/22
HPC, Big Data, and Data Science
Christian Kniep
D.hpc
<p>This short talk will disect the container ecosystem for HPC in four segments and discusses what to look out for, what is already settled and how to navigate containers in 2022.</p>
2/5/22
HPC, Big Data, and Data Science
D.hpc
<p>Optimizing CPU management improves cluster performance and security, but is daunting to almost everyone. CPU management may seem complex, but it can be explained in such a way that even your inner toddler will comprehend. With this talk, we will give a path to success.</p> <p>You may have a multi-socket node cluster where your AI/ML workloads care about the proximity of your CPUs to GPUs. You may be running scientific workloads where you want to pin in cores within containers instead of just ...
2/5/22
HPC, Big Data, and Data Science
Trevor Grant
D.hpc
<p>Working with big data matrices is challenging, Kubernetes allows users to elastically scale, but can only have a pod as large as a node, which may not be large enough to fit the matrix in memory. While Kubernetes allows for other paradigms on top of it which allows pods to coordinate on individual jobs, setting them up and making them play nice with ML platforms is not straightforward. Using Apache Spark and Apache Mahout we can work with matrices of any dimension and distribute them across ...