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SUMMARY:Choosing the Right File Storage for Scientific HPC: Lustre\, CephF
 S\, or BeeGFS? - Kritik Sachdeva\, Deepa
DTSTART;TZID=Europe/Paris:20261125T120000
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DTSTAMP:20260930T112545Z
UID:pretalx-compute-paris-2026-9KXWM7@pretalx.com
DESCRIPTION:Scientific computing teams face a high-stakes decision when de
 signing storage infrastructure: which open-source parallel filesystem fits
  their workloads? With growing diversity — from large-scale simulations 
 and climate modeling to genomics\, AI-assisted research\, and multi-discip
 linary analysis — the wrong choice can mean performance bottlenecks\, he
 avy operational overhead\, or unnecessary cost.\nThis talk offers a ground
 ed comparison of three widely used open-source parallel filesystems:\n\n- 
 Lustre — the long-established leader in traditional HPC\, known for high
  sequential throughput and strong MPI-IO support\, typically deployed for 
 high-performance scratch.\n- CephFS — the POSIX file interface of Ceph\,
  the unified software-defined storage platform\; CephFS shares the same RA
 DOS cluster as Ceph's object (RGW) and block (RBD) interfaces\, making it 
 attractive where file\, object\, and block are needed from one system\, pa
 rticularly in cloud-native and Kubernetes (Rook) environments.\n- BeeGFS 
 — a lightweight parallel filesystem valued for simpler deployment and ma
 nagement in mid-to-large scientific clusters.\n\nThe session works through
  the key decision factors:\n\n- Performance characteristics across scienti
 fic I/O patterns (sequential vs. mixed).\n- Scalability approaches and met
 adata handling.\n- Operational complexity — deployment\, monitoring\, an
 d maintenance effort.\n- Hardware flexibility and integration with modern 
 tooling (Slurm\, Rook\, Kubernetes).\n- Suitability for hybrid architectur
 es — for example\, sites pairing Lustre for high-performance scratch wit
 h Ceph for home directory\, and long-term storage.\n\nRather than crowning
  a single winner\, the talk gives attendees a clear decision framework —
  mapping workload type\, scale\, operational capacity\, and hardware const
 raints to the file storage that best fits — drawn from production usage 
 patterns in scientific computing.
LOCATION:Room 106
URL:https://pretalx.com/compute-paris-2026/talk/9KXWM7/
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