HPC, Big Data, and Data Science

This is The Way- A Crash Course on the Intricacies of Managing CPUs in K8s

From homogenous single-socket to heterogenous multi-socket clusters
<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 a pod level. You may have a single-socket server where you want to save a single core outside of Kubernetes for a daemon dedicated to mining bitcoin, without affecting your other jobs (please do not do this). We will cover these and more, helping you understand the intricacies of CPU management within the kubelet and what Kuberenetes can and cannot currently do. We will also cover how you can help escalate the visibility of use cases not currently covered within Kubernetes.</p>
Many clusters do not have CPU management because it is difficult to do correctly without impacting performance. While there are static and dynamic pinning abilities, we will consolidate the use cases and remove the pain it takes for users to deploy Kubernetes. This talk will help the audience gain an insight into features that can be used for resource management and orchestration of containerized applications with focus on CPU management. Users that have performance sensitive workloads such as AI/ML, Telco, 5G and Networking workloads will benefit from this talk. This talk will also help CTOs, system architects, developers and engineers in their planning to develop, test, or optimize their deployments in a Kubernetes environment. We also hope to use this forum to find other community members with specific needs in our desire to make the kubelet more flexible with regard to CPU management.

Weitere Infos

Format devroom

Weitere Sessions

05.02.22
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 ...
05.02.22
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 ...
05.02.22
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 ...
05.02.22
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>
05.02.22
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>
05.02.22
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 ...
06.02.22
HPC, Big Data, and Data Science
Philipp M. Dau
D.hpc
<p>Many scientific disciplines have benefitted from the availability of big datasets to develop algorithm supported solutions. Recently, this trend has penetrated the fields of crime and police research. The presentation highlights use cases of big data computation and HPC for typical datasets in crime science: crime records, emergency call data, and police GPS data. The focus lies on spatiotemporal applications (i.e., geocoding, map matching, spatial and temporal algorithms). The datasets come ...