In-Transit Data Transport Strategy Analysis
In-Transit Data Transport Strategies for Coupled AI-Simulation Workflow Patterns
This analysis focuses on optimizing data transport within coupled AI-Simulation HPC workflows, particularly for the Aurora supercomputer. We evaluate two common patterns: one-to-one co-located and many-to-one distributed, assessing various backends like node-local memory, Redis, DragonHPC, and Lustre file system to identify optimal strategies for different data sizes and scales.
Executive Impact: Key Metrics
1.5x
Performance Improvement (One-to-One, Node-Local)
512 nodes
Nodes Tested (Max)
10x Higher
Data Transport Overhead (File System, 512 nodes)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Workflow Optimization
Benchmarking & Tools
The study highlights the increasing complexity of coupled AI-Simulation workflows in HPC, emphasizing the need for robust data transport strategies to avoid bottlenecks. Traditional file-based I/O is often insufficient for modern data volumes and low-latency demands, necessitating in-situ and in-transit approaches.
SimAI-Bench is introduced as a flexible framework for emulating and benchmarking AI-coupled HPC workflows. It provides mini-apps to model key task execution and data transfer patterns, enabling performance analysis of various data transport backends and deployment strategies on large-scale systems like Aurora.
One-to-One Workflow Data Flow
Simulation Component
→
In-Transit Data Staging
→
AI Training Component
→
Steering Feedback
1x Iteration Time
Node-Local Overhead (One-to-One, 32MB)
| Backend | Performance Characteristics |
|---|---|
| Node-local |
|
| DragonHPC |
|
| Redis |
|
| Filesystem (Lustre) |
|
10x Iteration Time
File System Overhead (One-to-One, 512 Nodes, 32MB)
Many-to-One Pattern: Scaling Challenge
The many-to-one workflow pattern, where multiple simulations feed a single AI model, presents significant data transport bottlenecks as the ensemble size grows. The centralized AI component needs to read data non-locally from all simulations, making distributed data access a critical factor for performance. This contrasts with the co-located one-to-one pattern where data exchange is primarily node-local.
| Backend | Key Finding |
|---|---|
| Redis |
|
| DragonHPC |
|
| Filesystem (Lustre) |
|
Optimize Your AI-Simulation Workflow ROI
Estimate the potential annual savings and reclaimed hours by optimizing data transport in your HPC AI-Simulation workflows. Tailor the inputs to reflect your enterprise's operational scale and see the impact of efficient data staging.
Annual Savings
$0
Hours Reclaimed Annually
0
Your AI-Simulation Workflow Optimization Roadmap
A phased approach to integrate efficient in-transit data transport and maximize your HPC investment.
Phase 1: Workflow Analysis & Benchmarking
Utilize SimAI-Bench to analyze existing AI-Simulation workflows, identify data transport patterns, and benchmark current performance bottlenecks. This involves profiling data movement and computational components.
Phase 2: Backend Prototyping & Optimization
Experiment with different data transport backends (node-local, DragonHPC, Redis, file systems) using SimAI-Bench mini-apps. Optimize configurations for specific workflow patterns (one-to-one, many-to-one) and data characteristics.
Phase 3: Integration & Deployment
Integrate the optimized data transport strategies into your production HPC AI-Simulation workflows. Leverage SimAI-Bench's modular design for seamless integration with existing workflow managers and PyTorch/dpnp frameworks.
Phase 4: Continuous Performance Monitoring
Establish a continuous monitoring framework to track data transport performance and workflow efficiency. Adapt and refine strategies as data volumes, model complexities, and HPC architectures evolve.
Transform Your HPC AI-Simulation Workflows
Ready to unlock peak performance and efficiency for your AI-driven scientific campaigns? Our experts can help you design and implement optimal in-transit data transport strategies tailored to your unique needs.
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