Enterprise AI Analysis
A Query Engine for Scientific Data Exploration using Theory, Simulation, and Artificial Intelligence Models
Modern scientific discovery is undergoing a profound transformation, driven by the convergence of high-performance computing (HPC) and artificial intelligence (AI). The Intelligent Data Search (IDS) framework addresses the critical challenge of interactively exploring massive, multi-modal scientific datasets with integrated computational models. By leveraging a scalable in-memory datastore and a unified query engine, IDS enables scientists to compose expressive queries that combine keyword searches, set-theoretic operations, and linear-algebraic methods with complex computational models like simulations and AI inferences. Its architecture, built on the Cray Graph Engine (CGE) and featuring a globally distributed, multi-tier cache, significantly reduces computational latency and workflow fragmentation, demonstrating 5-15x end-to-end performance improvement in drug discovery workflows over petascale datasets.
Executive Impact & Strategic Value
The Intelligent Data Search (IDS) framework delivers substantial strategic value by accelerating scientific discovery, enabling complex computational workflows, and providing unprecedented scale and efficiency for enterprise AI initiatives.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
IDS Framework & Architecture
Query Planning & AI Optimization
Global Distributed Cache
The Intelligent Data Search (IDS) Framework
The IDS framework is a scalable, massively parallel processing database built upon the Cray Graph Engine (CGE). It acts as a 3-in-1 feature store, vector store, and knowledge graph host, capable of managing diverse data types including documents, images, 3D point clouds, genomic sequences, and vector embeddings. This unified approach enables scientists to query extensive datasets with keyword, set-theoretic, and linear-algebraic methods. IDS also integrates a repository of computational models, including domain-specific algorithms, open-source software, pre-trained AI models, and traditional HPC simulation codes, all orchestrated by an intelligent query planner.
Query Planning & AI Optimization
A core feature of IDS is its advanced query planning and optimization for AI-based User-Defined Functions (UDFs). It employs UDF profiling to track execution times and rejection rates, enabling dynamic query optimization. The system uses solution re-balancing to distribute workloads efficiently across ranks, taking into account individual rank throughput, especially critical for UDF expressions that can vary widely in computational requirements. For AI/ML pipelines, IDS reorders chained conditional operators, prioritizing those with lower estimated evaluation times and higher solution elimination potential, ensuring optimal execution across heterogeneous resources.
Global Shared Client-Side Cache
IDS introduces a globally distributed, multi-tier client-side cache to accelerate computations by stashing intermediate and simulation outputs. This cache leverages aggregate client-side resources (DRAM, NVMe, network) and RDMA-based access, significantly reducing computational latency and enabling reuse of results from prior simulations and queries. The Cache Manager orchestrates cache operations, enforces policies, and manages metadata to ensure optimal performance, providing a unified framework for data sharing and adapting to heterogeneous HPC environments through OpenFAM.
NCNPR Drug Discovery Workflow
Find related proteins (Uniprot P29274)
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Retrieve sequence & structural data
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Assemble candidate compounds
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Apply AI for DTBA & filter candidates
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Perform molecular docking
15x
End-to-End Performance Improvement with Caching
The global distributed cache in IDS delivers a significant 5-15x end-to-end performance improvement by stashing expensive simulation outputs, drastically cutting turnaround times for interactive scientific exploration.
| Selectivity | Compounds | Query Time (w/out caching) (s) | Query Time (with caching) (s) | Improvement Factor |
|---|---|---|---|---|
| 0.40 | 121 | 358.76 | 28.93 | 12.39x |
| 0.20 | 1129 | 3847.07 | 242.85 | 15.84x |
Case Study: Accelerating Drug Discovery at NCNPR
In collaboration with the National Center for Natural Products Research (NCNPR), IDS was applied to a critical drug re-purposing workflow. This involved integrating diverse data sources into a 100 billion-fact knowledge graph, utilizing AI models like AlphaFold for structure prediction, MolGAN for molecular generation, and HPC codes such as AutoDock Vina for molecular docking. The IDS framework allowed scientists to pose complex "what-if" and "what-could-be" queries, executing millions of similarity searches and thousands of AI inferences and HPC simulations over 30 TB of data. The global distributed cache was instrumental in stashing simulation outputs, reducing overall latency and enabling interactive, iterative exploration for novel compound screening.
Calculate Your Potential ROI with Enterprise AI
Estimate the impact of integrating advanced AI capabilities, like those demonstrated by IDS, into your enterprise workflows. See how efficiency gains translate into significant cost savings and reclaimed productivity.
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Your Enterprise AI Implementation Roadmap
Implementing a sophisticated query engine like IDS requires a structured approach. Our roadmap outlines key phases to ensure a seamless transition and maximize your return on investment.
Phase 1: Discovery & Strategy Session
Identify critical scientific data challenges, existing workflows, and strategic AI integration points. Define key performance indicators and align with business objectives.
Phase 2: Data Ingestion & Knowledge Graph Foundation
Ingest and integrate diverse, multi-modal datasets into a scalable knowledge graph. Establish data pipelines for continuous updates and ensure data quality and accessibility.
Phase 3: AI Model & UDF Integration
Integrate relevant AI models (e.g., AlphaFold for structure prediction) and domain-specific User-Defined Functions (UDFs) into the query engine for advanced analytics and inferences.
Phase 4: Distributed Cache & HPC Optimization
Implement and configure the global distributed cache to optimize query performance and reduce simulation latency. Fine-tune system for HPC environments and large-scale data processing.
Phase 5: Interactive Query Development & Iteration
Develop and refine expressive queries that leverage integrated models and data. Enable iterative, interactive data exploration for scientists and researchers, facilitating rapid hypothesis generation.
Ready to Transform Your Research?
Discover how an Intelligent Data Search framework can accelerate your scientific discoveries, reduce computational overhead, and empower your research teams with unparalleled data exploration capabilities.
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