Rethinking algorithm-based fault tolerance with a cooperative software-hardware approach

  • Authors:

  • Dong Li;Zizhong Chen;Panruo Wu;Jeffrey S. Vetter

  • Affiliations:

  • Oak Ridge National Laboratory;University of California, Riverside;University of California, Riverside;Oak Ridge National Laboratory and Georgia Institute of Technology

  • Venue:
  • SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Algorithm-based fault tolerance (ABFT) is a highly efficient resilience solution for many widely-used scientific computing kernels. However, in the context of the resilience ecosystem, ABFT is completely opaque to any underlying hardware resilience mechanisms. As a result, some data structures are over-protected by ABFT and hardware, which leads to redundant costs in terms of performance and energy. In this paper, we rethink ABFT using an integrated view including both software and hardware with the goal of improving performance and energy efficiency of ABFT-enabled applications. In particular, we study how to coordinate ABFT and error-correcting code (ECC) for main memory, and investigate the impact of this coordination on performance, energy, and resilience for ABFT-enabled applications. Scaling tests and analysis indicate that our approach saves up to 25% for system energy (and up to 40% for dynamic memory energy) with up to 18% performance improvement over traditional approaches of ABFT with ECC.