GPFS / Spectrum Scale for AI

Configure IBM Spectrum Scale (formerly GPFS) for enterprise AI workloads with policy-based data management, active file management, and multi-cluster federation.

Why Spectrum Scale for AI?

IBM Spectrum Scale is a parallel file system that excels in enterprise environments where data governance, multi-tenancy, and hybrid cloud connectivity are requirements alongside raw performance. It offers unique features like policy-driven automated tiering and built-in data lifecycle management.

Architecture Components

Policy-Based Tiering for ML Data

/* Move training data not accessed in 30 days to archive tier */
RULE 'tier-inactive-training-data' MIGRATE
  FROM POOL 'nvme-pool'
  TO POOL 'capacity-pool'
  WHERE PATH_NAME LIKE '%/training/%'
    AND DAYS(CURRENT_TIMESTAMP) - DAYS(ACCESS_TIME) > 30

/* Keep active model checkpoints on fast NVMe tier */
RULE 'keep-checkpoints-hot' MIGRATE
  FROM POOL 'capacity-pool'
  TO POOL 'nvme-pool'
  WHERE PATH_NAME LIKE '%/checkpoints/%'
    AND DAYS(CURRENT_TIMESTAMP) - DAYS(ACCESS_TIME) < 7

Active File Management (AFM)

AFM enables transparent caching of data between Spectrum Scale clusters or from external NFS sources. For AI, this enables a hub-and-spoke architecture where data is managed centrally but cached at GPU cluster locations for training performance.

Spectrum Scale vs Lustre for AI