Leonardo

Universidad Carlos III de Madrid · ARCOS-UC3M Expand · Limited

System Purpose

Question asked

Please describe the purpose and general usage of the submitted system.  This would include the types of typical applications it supports (e.g., defense applications, molecular dynamics, benchmarking, system test, systems research), and the general use and purpose of the data generated by the applications running on it.

The European supercomputer Leonardo (https://leonardo-supercomputer.cineca.eu/), hosted and managed by Cineca, installed in  2022 in the new data center located in the Technopole of Bologna, is one of the three pre-exascale systems announced by EuroHPC Joint Undertaking.

For example, the Leonardo supercomputer is helping at the mitigation and management of risks due to extreme situations, natural events, earthquakes, tsunamis, volcanic events, flash flood, for the fight against pandemic and epidemic situations.

In addition, the Leonardo supercomputer is ranked 7th on the June 2024 Top500 list (https://www.top500.org/system/179067/).

Availability

Question asked

Please provide the deployment timeframe of the submitted system, or for on-demand cloud systems, the general period over which it is deployed and destroyed.

Please describe the availability of the system to users and who are its set of most regular users.

Since Expand is an ad-hoc (https://github.com/xpn-arcos/xpn) file system, it is deployed at the moment of executing the application that uses it in a short period of time.

Being an open source file system, it is available to any user who wants to use it. The most regular users are those who run data-intensive applications.

Storage System Software

Question asked

How is this storage software available? (e.g., commercially, open-source, not publicly available) Note that if the storage software is not open-source or commercially available, then a general description would be requested, but this would limit the submission’s reproducibility score.

Can anyone download/purchase this software?

List either product webpage or open-source repo of the exact software used in IO500.

Give any and all additional details of this specific storage deployment: (e.g., type of storage server product such IBM ESS or DDN SFA400X2, use of Ext4 or some other file system on each storage node, dual connected storage media to storage servers).

Expand is an ad-hoc file system that uses the local storage available on the compute nodes. This file system is designed to run data intensive applications. 

Expand is open source and is available from the https://github.com/xpn-arcos/xpn/releases/tag/v3.1.0 repository.

Runtime Environment

Question asked

State here that you provided all scripts/documentation that would allow someone else to reproduce your environment and attempt to achieve a similar IO500 score as the submitted system.

NOTE: provide all files/documentation/scripts that would enable a user to build your environment and deploy the custom scripts, software, or config files once they have obtained the appropriate storage system hardware and software. These may be included into the io500.tgz or into the extraCodes upload on the submission form.

Several examples include:

  • Commands used to set striping information (either for the entire system or for particular directories)
  • File system config and tuning information (or a reason why this is not available due to lack of root access, etc) on each node type (e.g., non-default config parameters on all three types of Lustre nodes: MDS, OSS, and client)
  • Any additional scripts utilized that impact IO500 execution beyond the io500 config file. For example, with IBM Spectrum Scale, the output of mmlsconfig, mmlscluster, mmlsfs, and mmlsfileset commands (and possibly even a dump of the configuration if possible). Each file system probably has similar type of config/tuning information that would need to be shared for a user to fully reproduce the environment.

To reproduce the results obtained in this benchmark it is necessary:

  1. Download the source code from the repository (https://github.com/xpn-arcos/xpn/releases/tag/v3.1.0).
  2. Follow the compilation instructions available in the Readme (https://github.com/xpn-arcos/xpn/blob/v3.1.0/README.md) from the repository
  3. Execute the job provided with the submission

Fault Tolerance Mechanisms

Question asked

Does your system have a single point of failure as defined by “Definition 7” of a Production System? Please describe all mechanisms that provide fault tolerance for the submitted storage system. Be specific to your submission, not general storage system capabilities.

  • Power (For example, can power failure of a node or rack bring down the storage system?)
  • Networking (For example, can failure of a single NIC bring down the storage system? Remember that any networking failure that brings down multiple nodes is acceptable)
  • Inter storage data and metadata server (For example, what type of data protection mechanism does the storage system use to keep operating during node failures, e.g., active-active servers, client-directed RAID, declustered RAID, erasure-coding, 3-way replication)
  • Intra storage data and metadata server storage media (For example, how does the storage eysstem protect against fiailure of a single storage device within a node, e.g., raid5)

Please list any additional information needed to determine whether this system has a single point of failure.

Expand offers initial fault tolerance support based on replication. However, it has not been used for the evaluation with this benchmark.

Execution

Question asked

Please provide a description of how the IO500 benchmark was executed, e.g., via system scheduler (e.g., SLURM) to run a job on the compute cluster, which initially ran a setup process to configure the client and file system, and then started the full benchmark.

During the IO500 benchmark execution was the system entirely dedicated to running the benchmark or were there other jobs running in the same cluster and storage system?

The SLURM queue management system was used for the execution of the benchmark.

This job was executed exclusively on the compute nodes assigned by SLURM to the job. However, other compute nodes of the supercomputer could have jobs running at the same time.

Caching

Question asked

Please describe all caching mechanisms in client/server that were utilized during the IO500 run. This could include caching in any storage medium (e.g., SSD, RAM).

A few examples would include:

  • Client data/metadata caching (in Linux page cache or in other memory cache)
  • Client side NVMe read-only data cache
  • Storage server metadata/data caching in RAM
  • Storage controller caching
  • RAID card caching

At this moment, Expand does not have cache mechanisms. However, we are working on its inclusion.

Data Source

Question asked

Is the submitted system a stand-alone storage system or an acceleration layer in front of another storage system that is the source of truth for all application data? This question relates to whether the submitted system is a burst buffer layered on primary storage or primary storage itself.

Expand is an ad-hoc file system that uses the local storage available on the compute nodes.

Trust

Question asked

Please describe any steps taken to ensure that the results are trustworthy.

  • Did you run the benchmark multiple times and get similar scores?
  • Did you validate the score is below the physical capabilities of the deployed hardware?
  • How did you validate that the storage system follows the IO500 rules regarding persistence, which states, "All data must be written to persistent storage within the measured time for the individual benchmark, e.g. if a file system caches data, it must ensure that data is persistently stored before acknowledging the close"?

Did you run the benchmark multiple times and get similar scores?

Yes


Did you validate the score is below the physical capabilities of the deployed hardware?

Yes


How did you validate that the storage system follows the IO500 rules regarding persistence, [...]

Expand has no cache mechanisms and dumps all data straight to the local storage system of the compute node.