MOGON-NHR

Johannes Gutenberg University Mainz · N/A GekkoFS · Fully Reproducible

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Fully Reproducible

How the reproducibility levels are defined

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.

Since the end of 2021 the Johannes Gutenberg University Mainz (JGU) is part of the national high-performance computing consortium NHR Süd-West. It consists—aside from Mainz—of the University of Kaiserslautern-Landau (RPTU), the Goethe University Frankfurt, and the Saarland University.

On March 13th the new HPC-Cluster at the JGU was inaugurated. The high-performance computer MOGON NHR Süd-West is available for scientists throughout Germany for complex computing operations and the analysis of large amounts of data. With 500 compute nodes, 75.000 CPU-cores and a main memory of 186 TB, as well as a file server with 8.000 TB (8 Petabyte) data-intensive system and model simulations can be performed. Every node comes with two AMD processors (AMD EPYC 7713) that offer 64 cores each. Beyond that, 10 compute nodes are equipped with 4 A100 for specialized HPC workloads. The compute nodes are interconnected via HDR InfiniBand. The use of HDR InfiniBand, a particular network interface standard, offers an especially high transmission rate.

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.

The high-performance computer MOGON NHR Süd-West is available for scientists throughout Germany.

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).

The GekkoFS burst buffer file system is available to any user. It can aggregate the local I/O capacity and performance of each compute node in an HPC cluster to produce a high-performance storage space that can be accessed in a distributed manner.
This storage space allows HPC applications and simulations to run in isolation from each other with regards to I/O, which reduces interferences and improves performance.

GekkoFS can be used by non-privileged users within their compute job without assistance from administrators accelerating their I/O.

GekkoFS is fully open-source with the latest release v0.9.2:

The following publications are available:

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.

GekkoFS was using 64 server nodes and 10 client nodes (non-overlapping). Each client and node has one NIC available with a total of 10 client endpoints. Each server node offers 1 NVMe SSD. The necessary software dependencies were compiled beforehand by a non-privileged user. The code used is from a pre-released v0.9.3 and will be included when v0.9.3 is released.

The executed code is available here GekkoFS Gitlab Commit or tag `reproducibility-io500-isc24`.

Environment variables were given to the io500.sh script which are required for GekkoFS interception clients to work:

  • LIBGKFS_LOG_OUTPUT=/dev/shm/vef_gkfs_client.log
  • LIBGKFS_LOG=errors,warnings,info
  • LD_PRELOAD=/lustre/project/nhr-admire/vef/gekkofs/build/src/client/libgkfs_intercept.so
  • LIBGKFS_HOSTS_FILE=/lustre/project/nhr-admire/vef/run/io500/gkfs_hostfile
  • LIBGKFS_PROXY_PID_FILE=/dev/shm/vef_gkfs_proxy.pid"

Finally, clients and daemons were pinned to a single socket via `taskset`.

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.

GekkoFS does support replication and erasure encoding (not in the main release). Replication was not enabled for IO500.

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 benchmark was run during an interactive job with no batch script. GekkoFS dependencies and the file system itself were built prior to execution. The file system was started before running the benchmark and contained no data. The benchmark was run during production operation of MOGON-NHR. However, all used IO500 nodes were exclusive to the user. The file system was only accessed by the IO500 benchmark.

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

GekkoFS Servers use the existing node-local file system (XFS in our case) and therefore the Linux page cache was used.

All metadata and data operations were performed synchronously from client to server without any caching or batching involved. Two GekkoFS cache mechanisms were enabled:

  • Write size cache: This mode only lets the client periodically update the file size to the server (set to 1000 write operations) or when fsync() or close() is called.
  • Readdir cache: On opening a directory, the directory entries and their metadata are transferred and stored on the client until the directory is closed. This avoids consecutive stat RPC during `ls`. 

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.

GekkoFS servers use existing node-local storage and the corresponding file system. It was therefore the primary storage for IO500.

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"?

The benchmark was run several times (3+) with consistent results that were within expectations for the hardware available. The results were below the physical capabilities of the deployed hardware both concerning storage and network performance. 

Because all operations in GekkoFS are synchronous, all data was written to node-local storage on the server before the client received the result. This also applied to metadata.