What does fault tolerant Deep Learning need from MPI?

Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed DL implementations which use MPI require days of training (model learning) time on commonly studied datasets. Long running DL applications become susceptible to faults - requiring development of a fault tolerant system infrastructure, in addition to fault tolerant DL algorithms. This raises an important question: What is needed from MPI for de- signing fault tolerant DL implementations? In this paper, we address this problem for permanent faults. We motivate the need for a fault tolerant MPI specification by an in-depth consideration of recent innovations in DL algorithms and their properties, which drive the need for specific fault tolerance features. We present an in-depth discussion on the suitability of different parallelism types (model, data and hybrid); a need (or lack thereof) for check-pointing of any critical data structures; and most importantly, consideration for several fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI and their applicability to fault tolerant DL implementations. We leverage a distributed memory implementation of Caffe, currently available under the Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies demonstrates the effectiveness of the proposed fault tolerant DL implementation using OpenMPI based ULFM

Resilience of Parallel Applications

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.Future exascale systems are predicted to be formed by millions of cores. This is a great opportunity for HPC applications, however, it is also a hazard for the completion of their execution. Even if one computation node presents a failure every one century, a machine with 100.000 nodes will encounter a failure every 9 hours. Thus, HPC applications need to make use of fault tolerance techniques to ensure they successfully finish their execution. This PhD thesis is focused on fault tolerance solutions for generic parallel applications, more specifically in checkpointing solutions. We have extended CPPC, an MPI application-level portable checkpointing tool developed in our research group, to work with OpenMP applications, and hybrid MPI-OpenMP applications. Currently, we are working on transparently obtaining resilient MPI applications, that is, applications that are able to recover themselves from failures without stopping their execution.European Cooperation in Science and Technology. COSTThis research was supported by the Ministry of Economy and Competitiveness of Spain and FEDER funds of the EU (Project TIN2013-42148-P, and the predoctoral grant of Nuria Losada ref. BES-2014-068066) and by EU under the COST Program Action IC1305: Network for Sustainable Ultrascale Computing (NESUS)

Assessing resilient versus stop-and-restart fault-tolerant solutions in MPI applications

This is a post-peer-review, pre-copyedit version of an article published in Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-016-1863-z[Abstract] The Message Passing Interface (MPI) standard is the most popular parallel programming model for distributed systems. However, it lacks fault-tolerance support and, traditionally, failures are addressed with stop-and-restart checkpointing solutions. The proposal of User Level Failure Mitigation (ULFM) for the inclusion of resilience capabilities in the MPI standard provides new opportunities in this field, allowing the implementation of resilient MPI applications, i.e., applications that are able to detect and react to failures without stopping their execution. This work compares the performance of a traditional stop-and-restart checkpointing solution with its equivalent resilience proposal. Both approaches are built on top of ComPiler for Portable Checkpoiting (CPPC) an application-level checkpointing tool for MPI applications, and they allow to transparently obtain fault-tolerant MPI applications from generic MPI Single Program Multiple Data (SPMD). The evaluation is focused on the scalability of the two solutions, comparing both proposals using up to 3072 cores.Ministerio de Economía y Competitividad; TIN2013-42148-PMinisterio de Economía y Competitividad; BES-2014-068066Galicia.Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013/05