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Karsten Schwan, Greg Eisenhauer, Matt Wolf, and Ph.D. students
Georgia Institute of Technology, CERCS Research Center

Applications

• GTC, GTS, Pixie3D Fusion applications
• LAMMPS materials modeling code
• S3D combustion code

Problems

• Rapid output processing for timely science insights
• Large I/O output data volume
• Coupling to science users

Technology Basis

• ADIOS I/O interface
• EVPath data streaming middleware as ADIOS transport
• Location options for locating analytics processing: compute nodes, staging, remote, offline
• NNTI (Sandia) efficient transport for RDMA

Challenges

• Limited resources for I/O and analytics
• High I/O performance with additional online analytics
  • Require online data reduction
  • Require limiting use of disk subsystem
  • Require judicious data movement, analytics placement, and analytics scheduling

Pixie3D I/O Processing Pipeline

Flexible Placement and Execution for In-Situ Analytics

Technology Contributions

• ADIOS/EVPath I/O middleware
  • High performance data movement on IB and UGNI
  • Support diverse in situ analytics placement options
  • Higher-level API: meta-data rich, easy-to-use
• Flexible Placement
  • Metric-driven optimization, including for end-to-end performance/cost objectives
• Resource Containers:
  • Resource provisioning for analytics components

Result/Impact

• Extended ADIOS with new transport to support location-flexible in situ analytics
• Implemented in situ analytics for GTS, LAMMPS, Pixie3D, S3D
• Up to 30% end-to-end performance improvement of those applications through flexible placement
• Utilized DOE-provided NNTI RDMA transport for support of data staging

Managing I/O Resources with I/O Containers

Applications

• LAMMPS materials modeling code
• DOE Sandia applications
• SmartPointer Scientific Annotation Toolkit

Problems

• Poor staging resource allocations can cause dataflow bottleneck
• Complex computational models for analytics execution

Technology Basis

• ADIOS I/O interface
• EVPath data streaming for monitoring and control
• Multilevel management hierarchy
• Runtime resource management for I/O pipelines
• Scalable transactions for resilience (with DOE Sandia)

Challenges

• Limited resources for I/O and analytics
• Move offline analysis workflows online
  • Must support multiple computational models
  • Provide scalability for non-scalable analysis codes
  • Controlled data movement
  • Provide fault and performance isolation for analysis components and scientific applications

I/O Containers Overview

Managing I/O Resources with I/O Containers

Technology

• I/O Containers
  • Move offline workflows online to operate on data in-transit
  • Runtime resource management to balance resource usage amongst online analysis codes
• Doubly Distributed Transactions
  • Provide resilience for data movement and control operations in HPC environments

Result/Impact

• Extended ADIOS and DataTap to use I/O Containers middleware
• Increased end to end performance for online analysis pipelines
• Measured performance impact of implementing transactions in HPC environments