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Multimachine Stability01:25

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Related Experiment Videos

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server.

Qirong Ho1, James Cipar1, Henggang Cui2

  • 1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.

Advances in Neural Information Processing Systems
|November 18, 2014
PubMed
Summary
This summary is machine-generated.

We introduce a Stale Synchronous Parallel (SSP) model for distributed machine learning (ML). This approach speeds up ML algorithm convergence by allowing workers to use cached data, reducing idle time.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Distributed machine learning (ML) systems often face performance bottlenecks due to synchronization overhead.
  • Traditional synchronous and asynchronous models present trade-offs between worker utilization and algorithm correctness.

Purpose of the Study:

  • To propose a novel parameter server system utilizing the Stale Synchronous Parallel (SSP) model for distributed ML.
  • To enhance computational worker efficiency and ensure ML algorithm correctness in distributed environments.

Main Methods:

  • Implementation of a parameter server system with a shared interface for ML model parameters.
  • Adoption of the Stale Synchronous Parallel (SSP) computation model, allowing workers to access cached, potentially stale parameter values.
  • Theoretical proof of correctness for the SSP model in the context of ML algorithms.

Main Results:

  • The SSP model significantly increases the proportion of time computational workers spend on useful computation.
  • Empirical results demonstrate faster algorithm convergence for ML problems under the SSP model compared to synchronous and asynchronous schemes.
  • The SSP model provides correctness guarantees by limiting the maximum age of stale parameter values.

Conclusions:

  • The proposed parameter server system with the SSP model offers an effective solution for optimizing distributed ML performance.
  • SSP enhances computational efficiency and accelerates convergence without compromising ML algorithm correctness.
  • This approach represents a significant improvement over existing fully-synchronous and asynchronous distributed ML paradigms.