Tuning the CMS Coffea-casa facility for 200 Gbps Challenge

As a part of the IRIS-HEP “Analysis Grand Challenge” activities, the Coffea-casa AF team executed a “200 Gbps Challenge”. One of the goals of this challenge was to provide a setup for execution of a test notebook-style analysis on the facility that could process a 200 TB CMS NanoAOD dataset in 20 minutes. We describe the solutions we deployed at the facility to execute the challenge tasks. The facility was configured to provide 2000+ cores for quick turn-around, low-latency analysis. To reach the highest event processing rates we tested different scaling backends, both scaling over HTCondor and Kubernetes resources and using Dask and Taskvine schedulers. This configuration also allowed us to compare two different services for managing Dask clusters, Dask labextention, and Dask Gateway server, under extreme conditions. A robust set of XCache servers with a redirector were deployed in Kubernetes to cache the dataset to minimize wide-area network traffic. The XCache servers were backed with solid-state NVME drives deployed within the Kubernetes cluster nodes. All data access was authenticated using scitokens and was transparent to the user. To ensure we could track and measure data throughput precisely, we used our existing Prometheus monitoring stack to monitor the XCache pod throughput on the Kubernetes network layer. Using the rate query across all of the 8 XCache pods we were able to view a stacked cumulative graph of the total throughput for each XCache. This monitoring setup allowed us to ensure uniform data rates across all nodes while verifying we had reached the 200 Gbps benchmark.

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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701239
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Tuning the CMS Coffea-casa facility for 200 Gbps Challenge

C. Lundstedt, Oksana Shadura, Garhan Attebury, John Thiltges et al.
EPJ Web of Conferences
Distributed and Parallel Computing Systems
article

Tuning the CMS Coffea-casa facility for 200 Gbps Challenge

C. Lundstedt, Oksana Shadura, Garhan Attebury, John Thiltges, Derek Weitzel, Andrew Wightman, Sam Albin, Brian Bockelman, K. Bloom, B. López
article en

Abstract

As a part of the IRIS-HEP “Analysis Grand Challenge” activities, the Coffea-casa AF team executed a “200 Gbps Challenge”. One of the goals of this challenge was to provide a setup for execution of a test notebook-style analysis on the facility that could process a 200 TB CMS NanoAOD dataset in 20 minutes. We describe the solutions we deployed at the facility to execute the challenge tasks. The facility was configured to provide 2000+ cores for quick turn-around, low-latency analysis. To reach the highest event processing rates we tested different scaling backends, both scaling over HTCondor and Kubernetes resources and using Dask and Taskvine schedulers. This configuration also allowed us to compare two different services for managing Dask clusters, Dask labextention, and Dask Gateway server, under extreme conditions. A robust set of XCache servers with a redirector were deployed in Kubernetes to cache the dataset to minimize wide-area network traffic. The XCache servers were backed with solid-state NVME drives deployed within the Kubernetes cluster nodes. All data access was authenticated using scitokens and was transparent to the user. To ensure we could track and measure data throughput precisely, we used our existing Prometheus monitoring stack to monitor the XCache pod throughput on the Kubernetes network layer. Using the rate query across all of the 8 XCache pods we were able to view a stacked cumulative graph of the total throughput for each XCache. This monitoring setup allowed us to ensure uniform data rates across all nodes while verifying we had reached the 200 Gbps benchmark.

EPJ Web of ConferencesVol. 337
University of Notre Dame (US), University of Nebraska–Lincoln (US), Morgridge Institute for Research (US)
National Science Foundation
Openalex Percentile: Top 67%
Distributed and Parallel Computing Systems
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