Songshan
Institute of Computing Technology Chinese Academy of Sciences and National Supercomputing Center in Zhengzhou · Institute of Computing Technology Chinese Academy of Sciences HiStore · Proprietary
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 submitted system HiStore targets for system research purpose for high-performance storage system. It can support HPC and AI applications, such as data analytics and deep learning training.
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.
Deployment timeframe: March 1 2024 to May 2 2024
The system is used for research purpose and currently can only be used to internal users in ICT CAS. The most regular users are system researchers and postgraduate students at ICT CAS.
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).
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This submitted system HiStore is currently a research prototype. It distributes metadata across storage nodes like DAOS and allows parallel writes to different locations of the same file. Therefore, the bandwidth of the system increases linearly with the number of nodes. It is capable of supporting various types of HPC and AI applications. It also supports benchmarking and high-performance storage system research.
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It is not publicly available yet.
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No, it is currently a prototype and for research only.
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We don't have a open-source repo as the system is not publicly available.
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We develop storage system software ourselves. Each storage server has one Hygon C86 7151 processor (16-cores per processor), attached with 256 GB memory, 32 HDDs and 4 SSDs. Storage servers are connected by CX6 and Infiniband switch.
We use custom pfind to increase parallelism. The pfind script is implemented as follows:
1. The pfind packages the find request into a structure and passes it to the HiStore client library through ioctl syscall, as our client provides services in the user mode through syscall intercept;
2. The find request is offloaded to all servers in parallel and the client waits for the result to be returned;
3. When the server receives the find request, the master thread will let all threads search the metadata partition of each thread at the same time and merge them back to the master thread to return;
4. Finally the client merges the results of all servers and return them.For IO500 benchmark, we mainly made two optimizations: one is to partition metadata by file path, and the other is to implement an interface that only counts the number without transmitting file metadata.
Other useful information can be found from: HiStore · GitHub
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.
All the IO500 benchmark necessary steps are included in the submitted io500.sh script, ini config file, and job script. Anyone else can reproduce the result if he can prepare the same software and hardware environment.
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.
HiStore curently keeps only one copy of data and doesn't provide any fault rolerance mechanisms. So each storage server is the single point of failure. However, as the metadata and data are distributed across nodes and indexed by consistent hashing on clients, failure of one node would not affect the other nodes' availability.
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 storage service (including data and metadata service) of the submitted system is lauched by scritpts. The clients are loaded as library by io500 script.
During the IO500 benchmark execution, the nodes are entirely dedicated to running the benchmark while other jobs in the supercomputer share the networking environment (i.e., switches) with our system.
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
Client side: no caching
Storage server side: we caches data in DRAM and fsync will flush all data.
no other caching
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 submitted system HiStore is a stand-alone storage system directly running on physical servers. The data is stored in the HDDs and SSDs on the storage servers directly.
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"?
Yes, we run the benchmark multiple times and achieve almost same scores.
Yes, we have validated the results from different perspectives of hardware capability including CPU, memory, HDD, SSD and networking. All the results are reasonable.
We ensure the fsync will flush all data to SSDs or HDDs to follow the persistence rules.