Sol
Arizona State University · Dell Scratch · 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.
Arizona State University operates campus HPC systems funded through
periodic university investments that establish a base amount of free
computing capacity and sufficient headroom in the system's interconnect
fabric, shared parallel filesystem, liquid cooling capacity, rack space,
and power distribution to accommodate researcher-purchased "condo"
nodes. So-called condo nodes are researcher-purchased compute nodes
incorporated into a shared campus cluster, prioritizing their owner's
compute jobs, with any unused cycles offered opportunistically to the
rest of campus.
By 2020 Sol's predecessor had outgrown its network fabric and shared
filesystem (both had been upgraded more than once during the system's
lifetime), and most of the system's GPU capacity resided in
researcher-purchased "condo" nodes. The opportunistic capacity provided
by GPU-equipped condo nodes ultimately proved insufficient in meeting
the demands of GPU-hungry ASU researchers. Thus, Sol was built to
provide
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 storage is physically deployed into the production data center starting in April 2021 and will last through the life of the system that is maintainable.
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).
General purpose academic cluster at an R1 University, allowing the use of open-source and paid software.
The storage software used during the benchmark is BeeGFS which is publically available to be downloaded (https://www.beegfs.io/)
The mpi software used during the benchmark is provided by NVIDIA which is publically available to be downloaded (https://docs.nvidia.com/networking/display/HPCXv29/NVIDIA+HPC-X+Software+Toolkit)
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.
We used HPCx 2.13 for mpi to run the benchmark over InfiniBand RDMA with the 10 compute nodes. We did not modify the file system configuration when running the benchmark as we had already tuned it when we deployed the system into production.
The BeeGFS configuration files were initially modified from the default to provide more throughput and client connections based on our cluster size.
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.
All systems are redundant from the power having split PDUs and being on separate power feeds. The storage has a raid 6 to protect against disk failures and is set into groups of 10 disks. Each of the groups could lose up to 2 disks without any failure to the system or being unavailable.
The metadata and storage servers are set up in "BeeGFS Buddy Mirroring". For the metadata serves this acts as failover redundancy between the two systems if one was to become unavailable. For the storage servers, this sets them up as storage targets for the compute nodes, and are able to move between them if one of the 4 targets is not available.
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?
Directly connected into the compute nodes with ssh and then used mpiexec with HPCx 2.13. There is a slurm scheduler on the supercomputer but we determined it was easier to run the benchmark directly from the compute nodes.
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
There is caching on the storage server metadata/data in RAM that would have been utilized when running the benchmark multiple times through the different iterations that we had done.
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.
The primary scratch storage of 4PiB itself was used for the reading of the data.
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"?
We had run the io500 test multiple times with slight variations to the mpirun to produce the best outcome with similar scores. The data was stored in separate directories and files each time to make sure we could compare runs and outcomes of each modification to the mpirun.