DeepIO

deepio represents an exciting new frontier in my research, where we’re tackling one of the most pressing challenges in modern high-performance computing: optimizing data management for ai-driven scientific workflows. leading this innovative project, we’re reimagining how scientific computing systems handle the complex interplay between ai training and inference operations.

research vision 💡

the convergence of traditional hpc with ai has created unique challenges that existing storage systems weren’t designed to handle. our vision is to develop a comprehensive framework that:

• optimizes model exchange: revolutionizing how dnn models move between training and inference tasks
• maximizes performance: achieving up to 6.7x reduction in training times through intelligent i/o optimization
• enables intelligence: incorporating adaptive scheduling and smart caching strategies
• ensures scalability: supporting distributed multi-producer, multi-consumer patterns efficiently

core innovations 🔧

under my leadership, we’ve developed several groundbreaking technologies:

1. dlio benchmark

• novel i/o benchmark for scientific deep learning applications
• emulates complex data access patterns in ai workflows
• enables systematic identification of i/o bottlenecks
• demonstrates up to 6.7x improvement in training performance

2. stimulus framework

• stimpack: unified representation for scientific data formats
• stimops: optimized data ingestion routines
• 2x-5.3x performance improvement on summit supercomputer
• seamless integration with popular ai frameworks

3. viper i/o framework

• adaptive checkpoint scheduling for optimal model updates
• memory-first model transfer engine
• advanced publish-subscribe notification system
• significant reduction in model update latency

4. unboxkv analysis tool

• fine-grained analysis of kv caching in transformer models
• performance optimization for large language model inference
• advanced batching strategy optimization
• memory access pattern analysis

technical architecture

the deepio ecosystem consists of several integrated components:

• i/o profiling layer: advanced tooling for understanding ai workload characteristics
• optimization engine: ml-driven decision making for data placement and movement
• storage interface: high-performance data access and caching system
• monitoring system: real-time performance analysis and adaptation

impact on scientific ai 🌍

our innovations are already showing significant impact:

• performance: up to 6.7x reduction in training times
• efficiency: 2x-5.3x improvement in data processing speed
• scalability: successfully demonstrated on leadership computing facilities
• accessibility: enabling more complex ai workflows in scientific computing

research directions 🎯

we’re actively exploring several exciting frontiers:

• advanced caching strategies for transformer models
• ml-driven i/o optimization techniques
• novel data representation formats for ai workloads
• distributed model synchronization protocols

project resources 🛠️

• framework: coming soon
• documentation: in development
• benchmarks: dlio suite available upon request
• analysis tools: unboxkv toolset in testing phase

team & collaboration 👥

this ambitious project brings together experts in:

• high-performance computing
• deep learning systems
• storage architecture
• scientific computing

future roadmap

our ongoing development focuses on:

• expanding dlio benchmark capabilities
• enhancing stimulus framework features
• optimizing viper for new ai architectures
• developing advanced kv caching strategies

acknowledgements 🙏

this cutting-edge research is made possible through support from our research partners and the dedication of our talented team of graduate students and postdoctoral researchers.

Interested in collaborating or learning more about our AI-driven storage solutions? Feel free to reach out!