Architecting a 35 PB distributed parallel file system for science
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The document discusses the architecture and engineering of a 35 petabyte distributed parallel file system used in high-performance computing (HPC) at the National Energy Research Scientific Computing Center. It highlights the evolution of the author's career in HPC, the significance of the Perlmutter supercomputer, and the technical intricacies of storage and I/O processes essential for scientific applications. The document addresses challenges, software, and performance metrics associated with the system's file management and data processing capabilities.
Architecting a 35 PB distributed parallel file system for science
- 1. Architecting a 35 PB distributed parallel file system for science (formerly) Storage and I/O Software Engineer at NERSC, Berkeley Lab, US (currently) HPC DevOps Engineer at Seqera Labs, Barcelona Speck&Tech #53 Trento - May 29, 2023 Alberto Chiusole
- 2. - (2014-2017) BSc in Information and Business Organization Eng. - U. of Trento - 5 months exchange student at Technical University of Denmark, Copenhagen - (2017-2019) MSc in Data Science and Scientific Computing - U. of Trieste - (2017-2020) HPC Sysadmin and Scientific software developer - eXact Lab, Trieste - 3 months at CERN, Geneva, to work on Master’s thesis - Comparison between CephFS at CERN and Lustre FS at eXact lab - Presented at ISC High Performance in Frankfurt, July 2019 - (2020-2022) Storage and I/O Software Engineer - NERSC, Berkeley Lab, Cal., US - Worked on Perlmutter and its Lustre FS, first full-flash only 35 PB parallel FS - (2023 - now) HPC DevOps Engineer - Seqera Labs (remote) https://www.linkedin.com/in/albertochiusole/ https://bit.ly/Alberto-Chiusole-Scholar 2 How I ended up working on Supercomputers
- 3. High Performance Computing (HPC) empowers breakthroughs - Supercomputers run parallel applications to solve complex problems - Applications come from all kind of sciences - astrophysics, nuclear physics, molecular design, computational fluid dynamics, nuclear warheads status simulation, climate and weather forecasts, COVID vaccines (!), to name a few - Different from grid computing (nodes in HPC are more tightly coupled) - At massive scale several complex problems appear - Extremely expensive setups - Certain labs are a matter of national security (think Men in black) Let’s step back: why would anyone need such a FS? 3
- 4. So.. how do we get there? The hardware HPC is a combination of advanced hardware and specialized software 4
- 5. A Namesake for Remarkable Contributions Perlmutter is the newest supercomputer at NERSC (Berkeley Lab, California, US) Named after Saul Perlmutter, Nobel prize in Physics (2011) for his 1988 discovery that the universe is expanding. He confirmed his observations by running thousands of simulations at NERSC, and his research team is believed to have been the first to use supercomputers to analyze and validate observational data in cosmology. 5
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- 7. The hardware (Perlmutter) - Hardware is made of several racks of “blades” - CPU, GPU and now FPGA-enhanced nodes - Fast network interconnection - On PM: Cray (HPE) Slingshot 11 - Single-digit µs latency (~1-2 µs, <10 under heavy load) - Optimized for HPC: offload into silicon - Mix of Ethernet and InfiniBand protocols over fiber - InfiniBand cheaper for same performance - Liquid cooled units (note the colored pipes) - Requires maintenance (& downtime) to change liquid - Fast and large file systems - Different tiers, for different time-scales 7 Special tiles!
- 8. The software landscape - Linux-only world (mainly Red Hat, some SUSE, few Ubuntu, some custom) - https://top500.org/statistics/list/ - Parallel programming - OpenMP for intra-node comm., Message Passing Interface (MPI) for extra-node comm. - Fortran kingdom! - And C.. rarely C++. Python is gaining traction for data analysis and ML/AI steps - Job schedulers to allocate resources to users - Slurm (most popular), PBS, Torque, LSF, Moab, Grid Engine, etc - User requests a certain “portion” of the cluster for their jobs - Jobs are placed in a queue and wait for enough resources to start - The scheduler prepares the environment, collects logs, wraps up when jobs are done 8
- 9. - 💿 Storage usage, I/O and data transfer - Write the least to disk; write smartly (will see soon); avoid I/O bottlenecks - 📐 Data locality - Keep data as much as possible inside the node/rack - ⚡ Power usage - Servers use a lot of energy resources - Perlmutter (US): 2.5 MW at full power – Fugaku (JP): 29.9 MW - 830 households at max power (3 KW in Italy) - 🥶 Cooling - Location of data center is important - Berkeley Lab benefits from the always cool temp of the Bay Area (19 C max year) - Water is needed: can’t place DCs in deserts Some of the challenges 9
- 10. What is I/O? - Input/Output: everything that works with data and its storage - At large scale you need multiple discs/drives and servers to store data - Synchronization and consistency issues - Two processes writing to a single file (strong or eventual consistency?) - A process reading a file just written by another process (cache invalidation) - A process writing to/reading from a file in a disc that crashed (fault tolerance) - Duplicating files to increase aggregate read bandwidth - Data locality: a temporary file may be written to a local FS rather than parallel FS - Optimizing I/O is crucial - CPUs work at the order of the ns (10-9 s), network/NVMe work at most at µs (10-6 s) - Reducing network phase improves overall compute walltime considerably 10
- 11. Different file system scopes The slower the drive, the higher the capacity - Memory/NVMe drives are blazingly fast, but they are expensive - Scratch file systems should only be used for temporary storage (are purged often) 11 - Data used in the same month should be moved to HDD - Archive data should be moved to tape (it’s like VHS!) - Movement of data may be enforced or automatic (like S3 → Glacier)
- 12. - PM ships with the first all-flash file system in HPC - 3,480 Samsung PM1733 PCIe NVMe drives (15.36 TB each) - 3.5 GB/s seq. read, 3.2 seq. write speed by specifications - 35 PB of usable POSIX storage (as in 'df -h') - Directly integrated in the Slingshot compute network - No need for LNet routers Perlmutter scratch file system 12
- 13. - PM ships with the first all-flash file system in HPC - 3,480 Samsung PM1733 PCIe NVMe drives (15.36 TB each) - 3.5 GB/s seq. read, 3.2 seq. write speed by specifications - 35 PB of usable POSIX storage (as in 'df -h') - Directly integrated in the Slingshot compute network - No need for LNet routers - Enough to backup The Lord of The Rings trilogy 2.7M times - Or 152k times for the extended cut in 4k Ultra HD Perlmutter scratch file system 13
- 14. Metadata servers (MDS) - Store the directory structure, file names, inode locations inside OSS, etc - Decide the file layout on OSSs (striping, etc) - “Metadata” I/O, not bandwidth I/O Object storage servers (OSS) - Store chunks of data as binary - Write 1 MiB stripes over OSS (like a RAID-0) On PM: 16 MDS, 274 OSS Parallel and distributed FS: Lustre 14
- 15. Inside ClusterStor E1000 MDS/OSS unit in the rack: twin servers - Single-socket AMD Rome (128x PCIe Gen4 lanes) - Allows switchless design - 48 lanes for 24x NVMes, 32 lanes for 2x NICs - Each server responsible for 12 NVMe drives, can take over the other half if needed - GridRAID (HPE) + ldiskfs to maximize performance - OSS = 8+2+1 RAID6 (GridRAID) - MDS = 11-way RAID10 (mdraid) 15
- 16. Common HPC software used 16 Several tools are available to ease the coding for HPC: often intertwined MPI is the bread and butter for multi-node communication MPI-IO is its I/O layer, which helps managing files and transferring data - File preallocation, offset management, etc HDF5 uses MPI/MPI-IO to perform parallel I/O NetCDF uses HDF5 as its storage format IOR: benchmarking tool capable of generating synthetic I/O like HPC applications
- 17. Perlmutter: excellent performance end-to-end 17 41 GB/s read 27 GB/s write 1400 kIOPS read 29 kIOPS write 48 GB/s read 42 GB/s write 43 GB/s read 31 GB/s write 42 GB/s read 38 GB/s write 9,600 kIOPS read 1,600 kIOPS write “Software distance” from drives IOR: 88.4%(w) / 97.2%(r) NVMe block bandwidth (8+2 RAID on writes) 5.33%(w) / 15.1%(r) NVMe block IOPS (read-modify-write penalty RAID6)
- 18. Metadata performance of Perlmutter Using IOR in a “production” run - 230 clients x 6 procs/client = 1380 procs - 1.6 M files/s created In a “full-scale” run - 1382 clients x 2 procs/client = 2764 procs - 1.3 M files/s deleted Way smoother User Experience than previous HDD-based Cori file system 18
- 19. Some surprises found during performance evaluation SSDs slow down with age - Like “HDD fragmentation” - -10% write bandwidth after 5x capacity written to an OST - A fstrim is enough to fix it - 5x OST size: 665 TB - 2.2-2.9 PB daily expected writes - 5x writes ~ 60/80 days - The longer you wait, the longer fstrim takes - Performed nightly to keep performance up 19
- 20. Thanks! Questions? By the way, I use arch 20 PS: is hiring! seqera.io/careers This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract DE-AC02-05CH11231. This research used resources and data generated from resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.