LABIOS: Label-Based I/O System

Overview​

Revolutionizing data management through intelligent label-based I/O, bridging the gap between High-Performance Computing and AI workloads with unprecedented performance and flexibility.

🏆 Patented Innovation​

US Patent 11,630,834 B2 - Label-Based Data Representation I/O Process and System​

Status: Granted 2023

LABIOS represents a fundamental breakthrough in data management, protected by US patent law. Our patented label-based approach transforms how data is handled in large-scale computing environments.

The Shipping Label Analogy​

Just as a shipping label contains all information needed to deliver a package—address, priority, handling instructions—LABIOS labels contain everything needed to process data: operations, destinations, and metadata. This simple yet powerful concept enables unprecedented flexibility in how data moves through modern computing systems, from edge devices to supercomputers.

Key Features:

• Protected Innovation: Unique label-based data paradigm
• Industry Ready: Available for licensing
• Proven Technology: Validated in production

Resources: View Patent | Licensing Information

🌐 Why LABIOS Matters​

Bridging the Computing Convergence​

Where HPC meets Big Data meets AI

Modern computing faces an unprecedented challenge: the convergence of High-Performance Computing (HPC), Big Data analytics, and Artificial Intelligence workloads. Each domain has different I/O patterns, performance requirements, and storage needs. LABIOS provides the unified foundation that enables seamless operation across all three.

HPC Workloads​

• Checkpoint/restart optimization
• Burst buffer capabilities
• Collective I/O patterns
• 40% performance gains in VPIC

Big Data Analytics​

• MapReduce acceleration
• In-situ data processing
• Streaming analytics support
• 2x throughput improvement

AI/ML Training​

• GPU memory extension
• Model checkpoint acceleration
• KV cache optimization
• 3x memory reduction

The Convergence Challenge: By 2025, over 80% of enterprise data will require processing across multiple paradigms. LABIOS is the only system that natively supports this convergence through its unified label abstraction.

🚀 Key Achievements​

🎯 Project Overview​

LABIOS introduces a revolutionary label-based I/O paradigm that transforms how data is managed in modern computing environments. By converting all I/O requests into intelligent "labels"—configurable data units containing operations and data pointers—LABIOS enables unprecedented flexibility and performance.

Core Innovation: The Label Abstraction​

Label = {
  operation: Function pointer
  data: Input data pointer
  metadata: {
    type: Label category
    uniqueID: Identifier
    source: Origin location
    destination: Target location
    state: Current status flags
  }
}

This simple yet powerful abstraction enables:

• Asynchronous I/O with intelligent scheduling
• Storage elasticity through dynamic resource provisioning
• Computational storage by embedding operations in labels
• Seamless integration across HPC, Big Data, and AI workloads

🔬 Research Components​

MegaMmap: Memory-Storage Convergence​

MegaMmap provides a tiered, non-volatile Distributed Shared Memory system that:

• Reduces GPU memory usage by 3x for out-of-core workloads
• Enables applications to work with datasets 2x larger than physical memory
• Maintains performance parity with in-memory execution
• Transparently manages data movement across DRAM, NVMe, and storage tiers

Publications: SC'24 | Features: GPU Support, Tiered Memory

WisIO: Intelligent Bottleneck Detection​

WisIO revolutionizes I/O performance analysis through:

• 805x increased bottleneck coverage vs. single-perspective tools
• Classification of 340,000 bottlenecks/second
• 11x faster than traditional profiling tools like Darshan
• Multi-terabyte workflow analysis capabilities

Publications: ICS'25 | Features: Performance Analysis, HPC Workflows

HStream: Hierarchical Streaming Engine​

HStream provides intelligent streaming capabilities:

• 2x throughput improvement for streaming workloads
• Dynamic parallelism adjustment based on data ingestion rates
• 75% latency reduction under high data volumes
• Hierarchical buffering for varying data arrival patterns

Publications: ICPP'24 | Features: Streaming, Adaptive

Viper: DNN Model Transfer Framework​

Viper optimizes deep learning workflows through:

• 9x reduction in model update latency
• GPU-to-GPU memory transfers for maximum performance
• Intelligent checkpoint scheduling for training pipelines
• Transparent model storage and transfer capabilities

Publications: ICPP'24 | Features: Deep Learning, Model Serving

🏗️ Architecture & Integration​

Core Components​

• Label Manager: Constructs and optimizes labels based on I/O patterns
• Content Manager: Distributed key-value store for temporary data
• Label Dispatcher: Intelligent scheduling with priority support
• Worker Pool: Elastic execution environment with GPU support

Integration Ecosystem​

• Storage Systems: HDF5 VOL, ADIOS2, Parquet, POSIX
• Frameworks: IOWarp Runtime, DTIO, ChronoLog
• AI/ML Integration: RAG pipelines, KV cache optimization, Tokenization
• Deployment: Jarvis automation, Spack packages, Container support

📅 Project Timeline & Maturity​

Two Years of Innovation (2023-2025)​

From concept validation to production-ready technology

Year 1 - Foundation Established core architecture, developed MegaMmap (2x memory capacity), Viper (9x latency reduction), HStream (2x throughput). Published at SC'24 and ICPP'24.

Year 2 - Expansion GPU integration (3x memory reduction), WisIO (805x coverage), priority scheduling (10x latency improvement), AI/ML integration. Patent granted. IPDPS'25 and ICS'25 publications.

Year 3 - Coming Soon Production deployment, advanced AI features, cross-format interoperability, enterprise partnerships.

🔬 Research Impact & Domain Applications​

Transforming Scientific Discovery​

Real-world applications across critical research domains

🌍 Climate Science​

Processing massive climate simulations requires handling petabytes of data across thousands of time steps.

LABIOS Impact:

• 65% reduction in I/O time for E3SM workflows
• Enable real-time climate event detection
• Support for multi-resolution data analysis

🧬 Genomics & Bioinformatics​

Genomic sequencing generates massive datasets requiring complex processing pipelines.

LABIOS Impact:

• 2x faster variant calling pipelines
• In-situ quality control processing
• Seamless integration with GATK workflows

⚛️ Particle Physics​

Large Hadron Collider experiments generate data at unprecedented rates requiring real-time analysis.

LABIOS Impact:

• 40% improvement in VPIC performance
• Real-time event filtering capabilities
• Distributed analysis across sites

🤖 AI Model Training​

Training large language models requires efficient handling of massive datasets and checkpoints.

LABIOS Impact:

• 3x GPU memory extension
• 9x faster model checkpointing
• Optimized KV cache management

📊 Use Cases & Deployment​

I/O Acceleration​

Fast distributed cache for temporary I/O

Ideal for Hadoop workloads with node-local I/O requirements

Asynchronous Forwarding​

Decoupled I/O for improved application performance

Applications pass data to LABIOS for asynchronous persistence

Intelligent Buffering​

In-situ analysis and data sharing

Perfect for deep learning pipelines and visualization workflows

Elastic Storage​

Dynamic resource provisioning

Transparent storage hierarchies with live reconfiguration

📊 Impact Metrics Dashboard​

Cumulative Project Impact (2023-2025)​

Measurable outcomes from NSF investment

Progress Indicators​

• Technology Readiness Level: TRL 6/9 (67% complete)
• Community Adoption: 287 GitHub Stars (75% growth)
• Industry Engagement: 5 Active Discussions (45% progress)

Return on Investment​

For every $1 of NSF funding, LABIOS has generated an estimated $3.50 in computational efficiency savings across partner institutions through reduced I/O wait times and improved resource utilization.

📈 Performance Results​

Demonstrated Performance Gains​

Storage bridging: Up to 6x boost in I/O performance

Resource heterogeneity: 65% reduction in execution time

📚 Publications & Resources​

Recent Publications​

Resources & Tools​

• GitHub Repository
• Documentation
• MegaMmap
• HStream
• WisIO
• Jarvis

🔮 Future Vision: Year 3 and Beyond​

The Road Ahead​

Building on two years of success

Year 3 Objectives (2025-2026)​

• ✓ Complete cross-format data interoperability (HDF5 ↔ Parquet ↔ Arrow)
• ✓ Deploy production-ready AI model serving infrastructure
• ✓ Achieve 5x performance improvement for mixed HPC/AI workloads
• ✓ Release LABIOS 2.0 with full enterprise features
• ✓ Establish industry consortium for continued development

Long-term Vision (2026+)​

LABIOS will evolve into the de facto standard for intelligent I/O management:

• Edge-to-Exascale: Seamless data movement from IoT devices to supercomputers
• Autonomous I/O: AI-driven optimization of data placement and movement
• Quantum Integration: Support for emerging quantum computing I/O patterns
• Global Federation: Cross-continent data sharing with intelligent caching

Sustainability Plan​

Post-NSF funding, LABIOS will be sustained through:

• Commercial licensing revenue from industry partners
• DOE and DOD project integrations
• Open source community contributions
• Professional support and consulting services

🤝 Collaboration & Support​

Project Team​

Principal Investigators: [View team members on project page]

Partners & Funding​

• NSF Award #2331480 - National Science Foundation
• DOE National Laboratories - Argonne, Lawrence Livermore, Sandia
• Testing Infrastructure - NSF Delta, Chameleon, CloudLab

🌟 Technology Transfer & Licensing​

Ready for Commercial Adoption​

LABIOS technology is mature and available for licensing

After two years of intensive development and validation, LABIOS has proven its capabilities across diverse workloads and environments. Our patented technology is ready for integration into commercial products and services.

Ideal for:

• Storage system manufacturers
• Cloud service providers
• HPC solution vendors
• Big data analytics platforms
• AI/ML infrastructure companies

Benefits:

• Proven performance improvements
• Patent-protected innovation
• Extensive documentation
• Active development community
• Technical support available

Explore Licensing Opportunities

🤝 Industry Partnership Opportunities​

Technology Licensing​

Integrate LABIOS into your products with our flexible licensing options.

• ✓ Exclusive or non-exclusive licenses
• ✓ Royalty or fixed-fee structures
• ✓ Technical support packages

Research Collaboration​

Partner with our team to advance the state of the art in I/O systems.

• ✓ Joint R&D projects
• ✓ Student internship programs
• ✓ Co-authored publications

Custom Solutions​

Work with us to tailor LABIOS for your specific use cases.

• ✓ Architecture consultation
• ✓ Performance optimization
• ✓ Integration assistance

Start the Conversation​

Join leading organizations leveraging LABIOS to transform their data infrastructure. Let's discuss how LABIOS can accelerate your innovation.

[Contact Partnership Team](mailto:akougkas@illinoistech.edu?subject=LABIOS Partnership Inquiry) | Schedule a Demo

Join the LABIOS Community​

Connect with researchers and developers working on next-generation I/O systems

Join Zulip Chat | Contact Team

❓ Frequently Asked Questions​

What makes LABIOS different from existing I/O systems?​

LABIOS introduces a unified label abstraction that works across HPC, Big Data, and AI workloads. Unlike traditional systems optimized for specific use cases, LABIOS adapts dynamically to different I/O patterns while maintaining high performance.

How does licensing work for commercial use?​

LABIOS is available under a dual licensing model. The core technology is open source for research use, while commercial deployments require a license through IIT's technology transfer office. Contact us for specific terms and pricing.

Can LABIOS integrate with my existing storage infrastructure?​

Yes! LABIOS is designed as a middleware layer that works with existing storage systems including parallel file systems (Lustre, GPFS), object stores (S3, Swift), and local storage (NVMe, SSD). No infrastructure changes required.

What performance improvements can I expect?​

Performance gains vary by workload, but typical improvements include: 2-6x I/O throughput, 10x reduction in tail latency, 3x memory savings for GPU workloads, and 40% overall application speedup for I/O-intensive applications.

Is LABIOS production-ready?​

Yes! LABIOS has been validated in production environments at DOE national laboratories and is currently at Technology Readiness Level 6. Version 2.0 (coming in 2026) will include additional enterprise features.

How do I get started with LABIOS?​

Start with our GitHub repository and documentation. For production deployments, we recommend contacting our team for architecture review and optimization guidance. Training and support packages are available.

Have more questions?

Email Technical Team | Join Community Chat

🙏 Acknowledgments​

❤️ Funding Support​

National Science Foundation
Award #2331480
Primary funding enabling LABIOS research and development

👥 Research Partners​

DOE National Laboratories
Argonne • Lawrence Livermore • Sandia

NSF Cyberinfrastructure
Delta • Chameleon • CloudLab

Collaborative partnerships advancing storage innovation

LABIOS is developed at the Gnosis Research Center, Illinois Institute of Technology

NSF Award #2331480

This material is based upon work supported by the National Science Foundation under Grant No. 2331480. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

LABIOS is protected under U.S. Patent 11,630,834 B2. Commercial use requires licensing through Illinois Institute of Technology's Office of Technology Development.