Data decomposition in Monte Carlo neutron transport simulations using global view arrays

Dun, Nan; Fujita, Hideaki; Tramm, John R.; Chien, Andrew A.; Siegel, A.

doi:10.1177/1094342015577681

Cited by 12 publications

(5 citation statements)

References 25 publications

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“…Atlas [47] is a library for large-scale weather/climate modeling with exascale ambitions. OpenMC [62] is a software package for Monte Carlo particle transport simulations that should scale to exaflops. Lawrence et al [107] argue that new libraries and tools are needed for weather/climate models to make effective use of future exascale systems.…”

Section: Scientific Computingmentioning

confidence: 99%

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The Landscape of Exascale Research

et al. 2020

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The next generation of supercomputers will break the exascale barrier. Soon we will have systems capable of at least one quintillion (billion billion) floating-point operations per second (10 18 FLOPS). Tremendous amounts of work have been invested into identifying and overcoming the challenges of the exascale era. In this work, we present an overview of these efforts and provide insight into the important trends, developments, and exciting research opportunities in exascale computing. We use a three-stage approach in which we (1) discuss various exascale landmark studies, (2) use data-driven techniques to analyze the large collection of related literature, and (3) discuss eight research areas in depth based on influential articles. Overall, we observe that great advancements have been made in tackling the two primary exascale challenges: energy efficiency and fault tolerance. However, as we look forward, we still foresee two major concerns: the lack of suitable programming tools and the growing gap between processor performance and data bandwidth (i.e., memory, storage, networks). Although we will certainly reach exascale soon, without additional research, these issues could potentially limit the applicability of exascale computing.

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Section: Scientific Computingmentioning

confidence: 99%

“…Dongarra et al [55] describe a hierarchical QR factorization algorithm that scales better than existing QR factorization implementations. Dun et al [62] prepare OpenMC for exascale computing by incorporating global view arrays.…”

Section: Scalablementioning

confidence: 99%

The Landscape of Exascale Research

et al. 2020

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“…The ability to create multi-version array and partially materialize them, enables flexible recovery across versions. GVR has been used to demonstrate flexible multi-version rollback, forward error correction, and other creative recovery schemes [19,22]. Demonstrations include higherror rates, and results show modest runtime cost (< 1%) and programming effort in full-scale molecular dynamics, Monte Carlo, adaptive mesh, and indirect linear solver applications [12,13].…”

Section: Global View Resilience (Gvr)mentioning

confidence: 99%

Resilience for Stencil Computations with Latent Errors

Fang

Cavelan

Robert

et al. 2017

2017 46th International Conference on Parallel Processing (ICPP)

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Projections and measurements of error rates in near-exascale and exascale systems suggest a dramatic growth, due to extreme scale (10 9 cores), concurrency, software complexity, and deep submicron transistor scaling. Such a growth makes resilience a critical concern, and may increase the incidence of errors that "escape", silently corrupting application state. Such errors can often be revealed by application software tests but with long latencies, and thus are known as latent errors. We explore how to efficiently recover from latent errors, with an approach called application-based focused recovery (ABFR). Specifically we present a case study of stencil computations, a widely useful computational structure, showing how ABFR focuses recovery effort where needed, using intelligent testing and pruning to reduce recovery effort, and enables recovery effort to be overlapped with application computation. We analyze and characterize the ABFR approach on stencils, creating a performance model parameterized by error rate and detection interval (latency). We compare projections from the model to experimental results with the Chombo stencil application, validating the model and showing that ABFR on stencil can achieve a significant reductions in error recovery cost (up to 400x) and recovery latency (up to 4x). Such reductions enable efficient execution at scale with high latent error rates.

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“…This limited the simulation scaling, as each process maintained a tally array sized for the entire application in its memory. By introducing the distributed array using GVR, OpenMC can take advantage of globally shared data and gain scalability (Dun et al, 2014). Tally data is region-based and accumulated (i.e.…”

Section: Deep: Federate Primary Application Data Structuresmentioning

confidence: 99%

Exploring versioned distributed arrays for resilience in scientific applications

Chien

Balaji

Dun

et al. 2016

The International Journal of High Performance Computing Applica

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Exascale studies project reliability challenges for future HPC systems. We present the Global View Resilience (GVR) system, a library for portable resilience. GVR begins with a subset of the Global Arrays interface, and adds new capabilities to create versions, name versions, and compute on version data. Applications can focus versioning where and when it is most productive, and customize for each application structure independently. This control is portable, and its embedding in application source makes it natural to express and easy to maintain. The ability to name multiple versions and ''partially materialize'' them efficiently makes ambitious forward-recovery based on ''data slices'' across versions or data structures both easy to express and efficient. Using several large applications (OpenMC, preconditioned conjugate gradient (PCG) solver, ddcMD, and Chombo), we evaluate the programming effort to add resilience. The required changes are small (\ 2% lines of code (LOC)), localized and machine-independent, and perhaps most important, require no software architecture changes. We also measure the overhead of adding GVR versioning and show that overheads \ 2% are generally achieved. This overhead suggests that GVR can be implemented in large-scale codes and support portable error recovery with modest investment and runtime impact. Our results are drawn from both IBM BG/Q and Cray XC30 experiments, demonstrating portability. We also present two case studies of flexible error recovery, illustrating how GVR can be used for multi-version rollback recovery, and several different forward-recovery schemes. GVR's multi-version enables applications to survive latent errors (silent data corruption) with significant detection latency, and forward recovery can make that recovery extremely efficient. Our results suggest that GVR is scalable, portable, and efficient. GVR interfaces are flexible, supporting a variety of recovery schemes, and altogether GVR embodies a gentleslope path to tolerate growing error rates in future extreme-scale systems.

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Data decomposition in Monte Carlo neutron transport simulations using global view arrays

Cited by 12 publications

References 25 publications

The Landscape of Exascale Research

The Landscape of Exascale Research

Resilience for Stencil Computations with Latent Errors

Exploring versioned distributed arrays for resilience in scientific applications

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