ClusterCockpit installation manual

Introduction

ClusterCockpit requires the following components:

• A node agent running on all compute nodes that measures required metrics and forwards all data to a time series metrics database. ClusterCockpit provides its own node agent cc-metric-collector. This is the recommended setup, but ClusterCockpit can also be integrated with other node agents, e.g. collectd, prometheus or telegraf. In this case you have to use it with the accompanying time series database.
• A metric time series database. ClusterCockpit provides its own solution cc-metric-store, that is the recommended solution. There is also metric store support for Prometheus and InfluxDB. InfluxDB is currently barely tested. Usually only one instance of the time series database is required.
• The api and web interface backend cc-backend. Only one instance of cc-backend is required. This will provide the HTTP server at the desired monitoring URL for serving the web interface.
• A SQL database. It is recommended to use the builtin sqlite database for ClusterCockpit. You can setup LiteStream as a service which performs a continuous replication of the sqlite database to multiple storage backends. Optionally cc-backend also supports MariaDB/MySQL as SQL database backends.
• A batch job scheduler adapter that provides the job meta information to cc-backend. This is done by using the provided REST api for starting and stopping jobs. For Slurm there is a Python based solution (cc-slurm-sync ) maintained by PC2 Paderborn is available. For HTCondor there also exists cc-condor-sync.

Server Hardware

cc-backend is threaded and therefore profits from multiple cores. It does not require a lot of memory. It is possible to run it in a virtual machine. For best performance the ./var folder of cc-backend which contains the sqlite database file and the file based job archive should be located on a fast storage device, ideally a NVMe SSD. The sqlite database file and the job archive will grow over time (if you are not removing old jobs using a retention policy). Our setup covering five clusters over 4 years take 50GB for the sqlite database and around 700GB for the job archive. cc-metric-store is also threaded and requires a fixed amount of main memory. How much depends on your configuration, but 128GB should be enough for most setups. We run cc-backend and cc-metric-store on the same server as systemd services.

Planning and initial configuration

We recommended the following order for planning and configuring a ClusterCockpit installation:

1. Setup your metric list: With two exceptions you are in general free which metrics you want choose. Those exceptions are: mem_bw for main memory bandwidth and ‘flops_any’ for flop throughput (double precision flops are upscaled to single precision rates). You can find a discussion of useful metrics and their naming here. This metric list is an integral component for the configuration of all ClusterCockpit components.
2. Configure and deploy cc-metric-store.
3. Configure and deploy cc-metric-collector. For a detailed description on how to setup cc-metric-collector have a look at /docs/tutorials/prod-ccmc/
4. Configure and deploy cc-backend
5. Setup batch job scheduler adapter

Common problems

Up front here is a list with common issues people are facing when installing ClusterCockpit for the first time.

Inconsistent metric names across components

At the moment you need to configure the metric list in every component separately. In cc-metric-collector the metrics that are send to the cc-metric-store are determined by the collector configuration and possible renaming in the router configuration. For cc-metric-store in config.json you need to specify a metric list in-order to configure the native metric frequency and how a metric is aggregated. Metrics that are send to cc-metric-store and do not appear in its configuration are silently dropped! In cc-backend for every cluster you need to create a cluster.json configuration in the job-archive. There you setup which metrics are shown in the web-frontend including many additional properties for the metrics. For running jobs cc-backend will query cc-metric-store for exactly those metric names and if there is no match there will be an error.

We provide a json schema based specification as part of the job meta and metric schema. This specification recommends a minimal set of metrics and we suggest to use the metric names provided there. While it is up to you if you want to adhere to the metric names suggested in the schema, there are two exceptions: mem_bw (main memory bandwidth) and flops_any (total flop rate with DP flops scaled to SP flops) are required for the roofline plots to work.

Inconsistent device naming between cc-metric-collector and batch job scheduler adapter

The batch job scheduler adapter (e.g. cc-slurm-sync) provides a list of resources that are used by the job. cc-backend will query cc-metric-store with exactly those resource ids for getting all metrics for a job. As a consequence if cc-metric-collector uses another systematic the metrics will not be found.

If you have GPU accelerators cc-slurm-sync should use the PCI-E device addresses as ids. The option use_pci_info_as_type_id for the nvidia and rocm-smi collectors in the collector configuration must be set to true. To validate and debug problems you can use the cc-metric-store debug endpoint:

curl -H "Authorization: Bearer $JWT" -D - "http://localhost:8080/api/debug"

This will return the current state of cc-metric-store. You can search for a hostname and there scroll for all topology leaf nodes that are available.

Missing nodes in subcluster node lists

ClusterCockpit supports multiple subclusters as part of a cluster. A subcluster in this context is a homogeneous hardware partition with a dedicated metric and device configuration. cc-backend dynamically matches the nodes a job runs on to subcluster node list to figure out on which subcluster a job is running. If nodes are missing in a subcluster node list this fails and the metric list used may be wrong.