Secure In-Storage Execution of VTK Workloads on Modern Parallel NFS Data Servers
Pushdown Architecture for VTK Workloads: Accelerating Data Analysis on pNFS
Unlock unprecedented performance for scientific visualization by analyzing data directly where it resides.
Executive Impact
This paper introduces a novel pushdown architecture that enables secure in-storage execution of VTK visualization pipelines directly on pNFS data servers. By offloading early stages of the pipeline (reading and filtering) to storage, the system significantly reduces data movement and achieves substantial performance improvements. Experiments with real-world scientific datasets demonstrate up to 6.1x speedup in end-to-end visualization runtime and up to 7.1x in data loading, thanks to early data filtering that drastically cuts data transfer volumes.
6.1x
End-to-End Speedup
7.1x
Data Loading Speedup
7
Orders of Magnitude Data Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Technology Overview
Performance Gains
Security & Integration
Pushdown Architecture
The core innovation is a pushdown architecture for pNFS-based storage systems. This system offloads initial VTK pipeline stages like reading and filtering directly to the data servers, leveraging FUSE for client-server communication and recent Linux kernel optimizations for efficient local data access. Offloaded code runs with user credentials, ensuring security.
Data Reduction & Speedups
By performing filtering at the storage layer, the architecture achieves data reduction ratios up to 7 orders of magnitude greater than standard compression. This translates to significant speedups, with end-to-end visualization runtime accelerated by up to 6.1x and data loading by up to 7.1x.
Secure & Seamless Integration
Security is maintained by running offloaded code with the originating user's credentials and enforcing proper filesystem permission checks. The system leverages existing VTK infrastructure and pNFS capabilities, ensuring seamless integration with current workflows while enhancing performance.
7.1x
Data Loading Speedup Achieved
By offloading early data filtering to pNFS data servers, the proposed architecture drastically reduces the volume of data transferred over the network, leading to significant improvements in data loading times.
Enterprise Process Flow
Client Writes Offload Info to FUSE Command File
→
Server Receives & Forks Child Process (User Credentials)
→
Child Process Executes VTK Pipeline (Read & Filter)
→
Writes Intermediate Results to FUSE Result File
→
Client Retrieves Results & Continues Pipeline
| Feature | Traditional Approach | Pushdown Architecture |
|---|---|---|
| Data Movement |
|
|
| Data Reduction |
|
|
| Security |
|
|
| Performance |
|
|
Case Study: Asteroid Impact Dataset
The asteroid impact dataset (xRage simulation) involved complex, adaptive mesh refinement, making selective array retrieval less effective. However, the pushdown architecture achieved significant data reduction by applying contour filters directly on the storage server. This resulted in 5.2x to 6.1x speedup for end-to-end runtime and 6.1x to 7.1x for data loading, outperforming compression methods due to the task-specific reduction capabilities.
6.1x
End-to-End Speedup
7.1x
Data Loading Speedup
Case Study: Cosmological Simulation Dataset
The Nyx cosmological simulation dataset featured high-entropy data, making traditional compression less effective (gzip 11% reduction, LZ4 no compression). Despite this, the pushdown architecture still delivered a 2.5x speedup by performing in-storage filtering. While lower than the asteroid dataset due to the Nyx dataset's compact uniform rectilinear grid, it demonstrates the effectiveness even with hard-to-compress data.
2.5x
End-to-End Speedup
Advanced ROI Calculator
Quantify the potential impact of optimizing your data pipelines.
Projected Annual Savings
Hours Reclaimed Annually
Your Implementation Roadmap
A clear path to integrating advanced data analysis within your enterprise.
Phase 1: Discovery & Assessment
Conduct a thorough analysis of your existing data workflows, identify bottlenecks, and define key visualization requirements.
Phase 2: Pilot Implementation
Deploy a pilot pushdown architecture for a specific VTK workload, gather performance metrics, and refine configuration.
Phase 3: Full-Scale Integration
Integrate the pushdown architecture across your pNFS environment and expand to other relevant scientific visualization pipelines.
Phase 4: Optimization & Expansion
Continuously monitor performance, optimize data partitioning, and explore extensions for broader in-storage data processing beyond VTK.
Ready to Transform Your Data Analysis?
Schedule a consultation with our experts to explore how in-storage execution can revolutionize your scientific visualization workflows.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat