Storage Architecture

From GPU HBM to Tape: Intelligent Data Tiering — Hierarchical storage management for petabyte-scale scientific data.

Format Hell Problem

Scientific computing suffers from extreme format fragmentation. Each domain has its own standards, and researchers spend 80%+ of their time on data format conversion.

Climate

  • • NetCDF, Zarr, GRIB
  • • Custom binary formats

Physics

  • • ROOT, HDF5
  • • Custom C++ formats
  • • Protocol Buffers

Genomics

  • • FASTA/FASTQ
  • • BAM/CRAM, VCF
  • • GFF/GTF

Materials

  • • VASP, Quantum ESPRESSO
  • • PDB, XYZ, DCD

CERN example: 50+ formats, 40% of code is format handling. CLIO Ingest normalizes all formats into a unified representation.

CLIO Ingest Architecture

Multi-Modal Ingestion

  • ▸ Simulation outputs
  • ▸ Sensor streams
  • ▸ Literature
  • ▸ Telemetry

Format Normalization

  • ▸ Plugin-based architecture
  • ▸ 14+ formats supported (expanding)
  • ▸ Canonical xarray-like representation

Semantic Enrichment

  • ▸ Units extraction
  • ▸ Coordinates metadata
  • ▸ Provenance tracking
  • ▸ Quality scoring

Dynamic Windowing

  • ▸ Temporal selection
  • ▸ Spatial filtering
  • ▸ Variable selection
  • ▸ Resolution adaptation

assemble_context() API Example

from iowarp.clio import assemble_context

# Multi-format data assembly
context = assemble_context(
    sources=[
        "/data/climate/cesm2/netcdf/temp_2024.nc",
        "/data/sensors/weather_station_001.csv",
        "/data/literature/climate_paper_2023.pdf"
    ],
    window={
        "temporal": ("2024-01-01", "2024-12-31"),
        "spatial": {"lat": (30, 50), "lon": (-120, -80)},
        "variables": ["temperature", "precipitation"]
    },
    enrichment={
        "units": True,
        "provenance": True,
        "quality_checks": ["conservation", "bounds"]
    }
)

# Returns unified xarray-like Dataset
print(context.dims)  # {'time': 365, 'lat': 240, 'lon': 480}
print(context.attrs['provenance'])  # Complete lineage

Storage Tiers

Tier Bandwidth Capacity Role Context Type
GPU HBM Coming Soon TB/s GBs Active computation Critical
NVMe SSDs GB/s TBs Working datasets Working
Parallel FS GB/s PBs Campaign storage Reference
Cloud/Tape Coming Soon MB/s EBs Long-term archive Archive

CLIO Transfer: Intelligent Data Movement

Isometric view of hierarchical storage tiers: GPU HBM, NVMe, Parallel File System, and Tape/Cloud Archive with latency labels

Agent-Aware Optimization

  • ▸ Prediction: Anticipate data needs
  • ▸ Preemptive staging: Move data before requests
  • ▸ Memory pressure management
  • ▸ Multi-agent deduplication

Hardware Acceleration

  • ▸ RDMA: Zero-copy networking
  • ▸ GPU Direct: Direct GPU-to-GPU transfer
  • ▸ CXL: Memory extension

Runtime Intelligence

  • ▸ Adaptive prefetching
  • ▸ Dynamic compression
  • ▸ Memory extension

MegaMmap Breakthrough (SC'24)

IOWarp's MegaMmap technology enables seamless memory-storage blending for petabyte-scale datasets, achieving breakthrough performance improvements.

Gray-Scott Simulation

20%
faster than MPI-based (OrangeFS, Assise, Hermes)
2x
resolution (L=3456, 1.5TB on 96GB/node)

KMeans Clustering

2.6x
less DRAM (12GB vs 32GB/node)
<10%
overhead

What This Enables

Seamless memory-storage blending for petabyte-scale datasets, enabling researchers to work with larger datasets than available DRAM.

Quality Assurance Framework

Physical Validation

  • ▸ Conservation laws
  • ▸ Dimensional analysis
  • ▸ Range checks

Provenance Tracking

Complete lineage for every context element: source, transformations, agents involved, timestamps, quality scores.

Confidence Scoring

  • ▸ Measurement uncertainty
  • ▸ Pipeline validation
  • ▸ Data completeness

Energy-Efficient Data Movement

Power-Optimized Placement

Intelligently places data in tiers based on access patterns and power consumption profiles.

Bandwidth-Aware Transfer

Optimizes transfer schedules to minimize energy consumption while meeting performance requirements.

Job-Aligned Staging

Coordinates data movement with HPC job scheduling to reduce idle time and energy waste.

Cross-Platform Deployment

On-Premises HPC

Native integration with Slurm, PBS, Lustre, GPFS

Cloud

AWS, Azure, GCP with S3-compatible storage

Hybrid

Seamless data movement between on-prem and cloud

Edge

Lightweight deployment for sensor networks and instruments