A Generic Strategy for Node-Failure Resilience for Certain Iterative Linear Algebra Methods

Pachajoa, Carlos; Ernstbrunner, Robert; Gansterer, Wilfried N.

doi:10.1109/ftxs51974.2020.00010

Cited by 2 publications

(3 citation statements)

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“…When the full state of the failed process is reconstructed, the computation can proceed on the replacement node. There is a generic strategy for identifying the state of an iterative linear algebra solver and for reconstructing the state upon recovery [14]. However, like prior work [16,17], we focus on the preconditioned conjugate gradient (PCG) solver, which solves the linear equation 𝐴𝑥 = 𝑏 for a symmetric positive definite matrix 𝐴 𝑛×𝑛 (see Algorithm 1).…”

Section: In-memory Esr and Its Challengesmentioning

confidence: 99%

“…These variables should be chosen such that all other significant variables can be reconstructed from their values. A generic method for this state identification for iterative solvers is described in [14]. It is also possible to take advantage of concurrent data distributed between nodes to reconstruct the state [16].…”

Section: In-memory Esr and Its Challengesmentioning

confidence: 99%

“…Necessary conditions on solvers for the applicability of ESR [14,16,17] are: (1) The iterative algorithm performs a finite-term recurrence, and (2) The iterative algorithm involves a matrix-vector product. In particular, ESR was applied to the Preconditioned Conjugate Gradient (PCG) solver, solving the linear equation 𝐴𝑥 = 𝑏 for a symmetric positive definite matrix 𝐴 𝑛×𝑛 .…”

Section: Introductionmentioning

confidence: 99%

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NVM-ESR: Using Non-Volatile Memory in Exact State Reconstruction of Preconditioned Conjugate Gradient

Yehonatan¹,

Snir²,

Levin³

et al. 2022

Preprint

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HPC systems are a critical resource for scientific research and advanced industries. The demand for computational power and memory is increasing and ushers in the exascale era, in which supercomputers are designed to provide enormous computing power to meet these needs. These complex supercomputers consist of many compute nodes and are consequently expected to experience frequent faults and crashes. Exact state reconstruction (ESR) has been proposed as a mechanism to alleviate the impact of frequent failures on long-term computations. ESR has shown great potential in the context of iterative linear algebra solvers, a key building block in numerous scientific applications.Recent designs of supercomputers feature the emerging nonvolatile memory (NVM) technology. For example, the Exascale Aurora supercomputer is planned to integrate Intel Optane™ DCPMM. This work investigates how NVM can be used to improve ESR so that it can scale to future exascale systems such as Aurora and provide enhanced resilience.We propose the non-volatile memory ESR (NVM-ESR) mechanism. NVM-ESR demonstrates how NVM can be utilized in supercomputers for enabling efficient recovery from faults while requiring significantly smaller memory footprint and time overheads in comparison to ESR. We focus on the preconditioned conjugate gradient (PCG) iterative solver also studied in prior ESR research, because it is employed by the representative HPCG scientific benchmark.The source code used by this work, as well as the benchmarks and other relevant sources, are available at: https://github.com/ Scientific-Computing-Lab-NRCN/NVM-ESR.git.

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Section: In-memory Esr and Its Challengesmentioning

confidence: 99%

Section: In-memory Esr and Its Challengesmentioning

confidence: 99%