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Designing a Scalable Fault Tolerance Model for High Performance Computational Chemistry: A Case Study with Coupled

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This study introduces an in-memory data redundancy approach for computational chemistry simulations, enhancing fault tolerance. This method significantly reduces overhead compared to traditional checkpointing, crucial for large-scale supercomputing.

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Area of Science:

  • Computational chemistry
  • High-performance computing
  • Fault tolerance

Background:

  • Modern supercomputers enable complex computational chemistry simulations.
  • Increasing system scale reduces Mean Time Between Failures (MTBF), necessitating advanced fault tolerance.
  • Traditional disk-based checkpointing is inefficient and adds significant overhead.

Purpose of the Study:

  • To design and implement a fault-tolerant version of the Coupled Cluster (CC) method within NWChem.
  • To address the challenges of reduced MTBF in extreme-scale computing.
  • To improve the efficiency of fault tolerance in computational chemistry.

Main Methods:

  • Utilized in-memory data redundancy for fault tolerance.
  • Developed an efficient data storage model for consistent data copies.
  • Implemented a robust recovery process for simulated faults.

Main Results:

  • The proposed design shows minimal overhead in normal operation.
  • Fault tolerance implementation incurs negligible overhead during simulated failures.
  • The method is efficient for large-scale computational chemistry applications.

Conclusions:

  • In-memory data redundancy offers an efficient fault tolerance solution for computational chemistry.
  • This approach is vital for maintaining simulation integrity on unreliable extreme-scale systems.
  • The developed method presents a viable alternative to traditional checkpointing.