EReinit: Scalable and efficient fault‐tolerance for bulk‐synchronous MPI applications. (14th August 2018)

• Record Type: Journal Article
• Authors: Chakraborty, Sourav; Laguna, Ignacio; Emani, Murali; Mohror, Kathryn; Panda, Dhabaleswar K.; Schulz, Martin; Subramoni, Hari
• Other Names: Jin Hai guestEditor.; Shen Xipeng guestEditor.; Lovas Robert guestEditor.; Liao Xiaofei guestEditor.; Skjellum Anthony guestEditor.; Bangalore Purushotham V. guestEditor.; Grant Ryan E. guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-14
• Subjects: fault tolerance -- high‐performance computing -- MPI -- resilience; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4863
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Scientists from many different fields have been developing Bulk‐Synchronous MPI applications to simulate and study a wide variety of scientific phenomena. Since failure rates are expected to increase in larger‐scale future HPC systems, providing efficient fault‐tolerance mechanisms for this class of applications is paramount. The global‐restart model has been proposed to decrease the time of failure recovery in Bulk‐Synchronous applications by allowing a fast reinitialization of MPI. However, the current implementations of this model have several drawbacks: they lack efficiency; their scalability have not been shown; and they require the use of the MPI profiling interface, which precludes the use of tools. In this paper, we present EReinit, an implementation of the global‐restart model that addresses these problems. Our key idea and optimization is the co‐design of basic fault‐tolerance mechanisms such as failure detection, notification, and recovery between MPI and the resource manager in contrast to current approaches on which these mechanisms are implemented in MPI only. We demonstrate EReinit in three HPC programs and show that it is up to four times more efficient than existing solutions at 4, 096 processes.