AI400X3M

DDN · DDN EXAScaler · Fully Reproducible

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.

I/O benchmarks (e.g., storage micro-benchmarks such as IOR, mdtest, and fio, as well as customer applications) are performed in the production environment using configurations that match planned customer deployments.

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 system was first deployed in 2019, with a storage subsystem upgrade completed in 2021. It is used by both internal and external users for benchmarking purposes.

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 storage software used in this submission is DDN EXAScaler, a commercially available Lustre-based parallel file system. EXAScaler can be purchased directly from DDN and is deployed in production HPC and AI environments worldwide.

Product information:

This deployment consists of DDN EXAScaler running on dedicated Lustre metadata and object storage servers connected to DDN SFA storage appliances. The storage subsystem uses enterprise NVMe media protected by de-clustered RAID 6.

Additional deployment details:

  • Storage software: DDN EXAScaler (Lustre-based parallel file system)
  • Storage controllers: DDN SFA storage appliances
  • Storage media: NVMe SSDs
  • Data protection: De-clustered RAID 6
  • Metadata and object storage targets: Lustre MDTs and OSTs
  • High availability: Lustre failover/failback configuration
  • Controller cache: Battery-backed mirrored write-back cache (16 GB per controller)
  • Power protection: Redundant power supplies
  • Client access: Lustre clients connected via a high-speed network fabric
  • Kubernetes integration: DDN EXAScaler CSI (EXA-CSI) driver available for containerized workloads

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.

# Client Lustre tunings.

clush -w "$SLURM_JOB_NODELIST" sudo lctl set_param \

osc.*.max_pages_per_rpc=4M \

osc.*.max_rpcs_in_flight=32 \

osc.*.checksums=0 \

ldlm.namespaces.*.lru_max_age=5000

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.

The system is deployed as a production-grade storage environment and includes the following resiliency and availability features:

  • Redundant power infrastructure
  • De-clustered RAID 6 protection across all Lustre OSTs and MDTs
  • Lustre high availability (HA) with failover and failback capabilities to ensure continuous service during component failures

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 IO500 benchmark was executed through the SLURM workload manager. A SLURM job was submitted to allocate the required client nodes and launch the benchmark across the cluster.

Each client node was configured with multiple Lustre mount points (eight mount points per client) to support the benchmark configuration. Singularity containers were used to provide a consistent runtime environment and to simplify the management of the multiple mount points required by the benchmark.

After the client environment was prepared and the file system was mounted on all participating nodes, the standard IO500 benchmark suite was executed. Benchmark results were collected and analyzed according to the IO500 methodology.

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

Lustre servers and clients use only the standard Linux page cache in RAM. No additional software caching layers, SSD caching tiers, or application-level caching mechanisms were employed during benchmark execution.

The storage controllers are equipped with battery-backed, mirrored write-back cache (WBC). Each controller provides 16 GB of protected cache capacity. The write-back cache is a standard feature of the production storage system and was enabled during benchmark execution.

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.

All data is stored on persistent NVMe storage. Once a write operation is committed by a client, the data is immediately visible and accessible to all other clients through the Lustre file system, consistent with normal Lustre semantics.

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

  • Did you validate that the data was persistently stored?

Yes