Context Engineering Paradigm Shift
From storage optimization to context orchestration. IOWarp treats data + metadata + computational state as unified context for autonomous agents.
The Fundamental Paradigm Shift
For decades, scientific computing optimized for data throughput and storage purity. But in the era of autonomous scientific AI, this optimization target is fundamentally misaligned. Agents don't need raw bytes—they need context. The paradigm must shift from optimizing storage systems for human scientists to orchestrating context for autonomous agents.
The Data Paradox
175 zettabytes by 2025, yet AI is "data-starved." 80% of scientific data is unstructured or siloed. Missing semantic relationships. Format fragmentation. IOWarp bridges this gap by treating data, metadata, and computational state as unified context—enabling agents to reason, not just retrieve.
Research Evolution
Hermes
Multi-tiered storage optimization, automated data movement
LABIOS
Label-based I/O, semantic-aware placement
ChronoLog
Time-series aware storage
MegaMmap
Memory-storage blending (SC'24 paper)
IOWarp
Full context orchestration for agentic AI
Context Engineering
New discipline for autonomous discovery
Key Research Contributions
MegaMmap (SC'24)
Memory-storage blending architecture enabling seamless data movement between DRAM and storage tiers.
Hermes
Multi-tiered buffering system with ML-driven prefetching for HPC workloads.
LABIOS
Label-based asynchronous I/O system enabling semantic-aware data placement and retrieval.
GenomIO
Genomics I/O optimization framework for variant analysis and genome-wide association studies.
ChronoLog
Distributed time-series storage system optimized for temporal queries and sensor data fusion.
Recognition
Demo Highlights
IOWarp Publications (15)
Jarvis: Towards a Shared, User-Friendly, and Reproducible I/O Infrastructure
2024Uncover the Overhead and Resource Usage for Handling KV Cache Overflow in LLM Inference
2024MegaMmap: Blurring the Boundary Between Memory and Storage for Data-Intensive Workloads
2024DFTracer: An Analysis-Friendly Data Flow Tracer for AI-Driven Workflows
2024To Derive or Not to Derive: I/O Libraries Take Charge of Derived Quantities Computation
2024DaYu: Optimizing Distributed Scientific Workflows by Decoding Dataflow Semantics and Dynamics
2024An Evaluation of DAOS for Simulation and Deep Learning HPCWorkloads
2024Viper: A High-Performance I/O Framework for Transparently Updating, Storing, and Transferring Deep Neural Network Models
2024HStream: A hierarchical data streaming engine for high-throughput scientific applications
2024TunIO: An AI-powered Framework for Optimizing HPC I/O
2024Data Flow Lifecycles for Optimizing Workflow Coordination
2023IOMax: Maximizing Out-of-Core I/O Analysis Performance on HPC Systems
2023An Evaluation of DAOS for Simulation and Deep Learning HPC Workloads
2023LabStor: A modular and extensible platform for developing high-performance, customized I/O stacks in userspace
2022Stimulus: Accelerate Data Management for Scientific AI applications in HPC
2022Showing IOWarp project publications. For the full GRC research portfolio (25+ papers), visit grc.iit.edu/publications.
Active Research Areas
Context Engineering
Novel approaches to representing and orchestrating context for autonomous agents. Hierarchical memory systems, semantic indexing, and context-aware prefetching.
Agent Orchestration
Multi-agent coordination frameworks, shared context protocols, and workflow optimization for scientific discovery pipelines.
Storage Systems
Multi-tier storage optimization, CXL/RDMA integration, and intelligent data placement strategies for petabyte-scale workloads.
Domain Applications
Specialized optimizations for climate science, materials discovery, genomics, seismology, and astrophysics applications.
Collaboration Opportunities
Open to academic, industry, and government partnerships. We welcome collaborations on context engineering, agentic science infrastructure, and domain-specific applications.
Interested in contributing? We offer research internships, visiting scholar positions, and collaborative grant opportunities.