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Author: Matthew Joseph O'Brien Publisher: ISBN: 9781321212600 Category : Languages : en Pages :
Book Description
In this dissertation, we present the parallel algorithms necessary to run domain decomposedMonte Carlo particle transport on large numbers of processors (millions of processors). Previous algorithms were not scalable, and the parallel overhead became more computationally costlythan the numerical simulation. The main algorithms we consider are:* Domain decomposition of constructive solid geometry: enables extremely largecalculations in which the background geometry is too large to fit in the memory of asingle computational node.* Load Balancing: keeps the workload per processor as even as possible so the calculationruns efficiently.* Global Particle Find: if particles are on the wrong processor, globally resolve theirlocations to the correct processor based on particle coordinate and background domain.* Visualizing constructive solid geometry, sourcing particles, deciding that particlestreaming communication is completed and spatial redecomposition.These algorithms are some of the most important parallel algorithms required for domaindecomposed Monte Carlo particle transport. We demonstrate that our previous algorithmswere not scalable, prove that our new algorithms are scalable, and run some of the algorithmsup to 2 million MPI processes on the Sequoia supercomputer.
Author: Matthew Joseph O'Brien Publisher: ISBN: 9781321212600 Category : Languages : en Pages :
Book Description
In this dissertation, we present the parallel algorithms necessary to run domain decomposedMonte Carlo particle transport on large numbers of processors (millions of processors). Previous algorithms were not scalable, and the parallel overhead became more computationally costlythan the numerical simulation. The main algorithms we consider are:* Domain decomposition of constructive solid geometry: enables extremely largecalculations in which the background geometry is too large to fit in the memory of asingle computational node.* Load Balancing: keeps the workload per processor as even as possible so the calculationruns efficiently.* Global Particle Find: if particles are on the wrong processor, globally resolve theirlocations to the correct processor based on particle coordinate and background domain.* Visualizing constructive solid geometry, sourcing particles, deciding that particlestreaming communication is completed and spatial redecomposition.These algorithms are some of the most important parallel algorithms required for domaindecomposed Monte Carlo particle transport. We demonstrate that our previous algorithmswere not scalable, prove that our new algorithms are scalable, and run some of the algorithmsup to 2 million MPI processes on the Sequoia supercomputer.
Author: Publisher: ISBN: Category : Languages : en Pages : 190
Book Description
In this dissertation, we present the parallel algorithms necessary to run domain decomposed Monte Carlo particle transport on large numbers of processors (millions of processors). Previous algorithms were not scalable, and the parallel overhead became more computationally costly than the numerical simulation.
Author: Publisher: ISBN: Category : Languages : en Pages : 16
Book Description
In order to run Monte Carlo particle transport calculations on new supercomputers with hundreds of thousands or millions of processors, care must be taken to implement scalable algorithms. This means that the algorithms must continue to perform well as the processor count increases. In this paper, we examine the scalability of:(1) globally resolving the particle locations on the correct processor, (2) deciding that particle streaming communication has finished, and (3) efficiently coupling neighbor domains together with different replication levels. We have run domain decomposed Monte Carlo particle transport on up to 221 = 2,097,152 MPI processes on the IBM BG/Q Sequoia supercomputer and observed scalable results that agree with our theoretical predictions. These calculations were carefully constructed to have the same amount of work on every processor, i.e. the calculation is already load balanced. We also examine load imbalanced calculations where each domain's replication level is proportional to its particle workload. In this case we show how to efficiently couple together adjacent domains to maintain within workgroup load balance and minimize memory usage.
Author: Nicholas Edward Horelik Publisher: ISBN: Category : Languages : en Pages : 158
Book Description
Monte Carlo (MC) neutral particle transport methods have long been considered the gold-standard for nuclear simulations, but high computational cost has limited their use significantly. However, as we move towards higher-fidelity nuclear reactor analyses the method has become competitive with traditional deterministic transport algorithms for the same level of accuracy, especially considering the inherent parallelism of the method and the ever-increasing concurrency of modern high performance computers. Yet before such analysis can be practical, several algorithmic challenges must be addressed, particularly in regards to the memory requirements of the method. In this thesis, a robust domain decomposition algorithm is proposed to alleviate this, along with models and analysis to support its use for full-scale reactor analysis. Algorithms were implemented in the full-physics Monte Carlo code OpenMC, and tested for a highly-detailed PWR benchmark: BEAVRS. The proposed domain decomposition implementation incorporates efficient algorithms for scalable inter-domain particle communication in a manner that is reproducible with any pseudo-random number seed. Algorithms are also proposed to scalably manage material and tally data with on-the-fly allocation during simulation, along with numerous optimizations required for scalability as the domain mesh is refined and divided among thousands of compute processes. The algorithms were tested on two supercomputers, namely the Mira Blue Gene/Q and the Titan XK7, demonstrating good performance with realistic tallies and materials requiring over a terabyte of aggregate memory. Performance models were also developed to more accurately predict the network and load imbalance penalties that arise from communicating particles between distributed compute nodes tracking different spatial domains. These were evaluated using machine properties and tallied particle movement characteristics, and empirically validated with observed timing results from the new implementation. Network penalties were shown to be almost negligible with per-process particle counts as low as 1000, and load imbalance penalties higher than a factor of four were not observed or predicted for finer domain meshes relevant to reactor analysis. Load balancing strategies were also explored, and intra-domain replication was shown to be very effective at improving parallel efficiencies without adding significant complexity to the algorithm or burden to the user. Performance of the strategy was quantified with a performance model, and shown to agree well with observed timings. Imbalances were shown to be almost completely removed for the finest domain meshes. Finally, full-core studies were carried out to demonstrate the efficacy of domain-decomposed Monte Carlo in tackling the full scope of the problem. A detailed mesh required for a robust depletion treatment was used, and good performance was demonstrated for depletion tallies with 206 nuclides. The largest runs scored six reaction rates for each nuclide in 51M regions for a total aggregate memory requirement of 1.4TB, and particle tracking rates were consistent with those observed for smaller non-domain- decomposed runs with equivalent tally complexity. These types of runs were previously not achievable with traditional Monte Carlo methods, and can be accomplished with domain decomposition with between 1.4x and 1.75x overhead with simple load balancing.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
One forthcoming challenge in the area of high-performance computing is having the ability to run large-scale problems while coping with less memory per compute node. In this work, they investigate a novel data decomposition method that would allow Monte Carlo transport calculations to be performed on systems with limited memory per compute node. In this method, each compute node remotely retrieves a small set of geometry and cross-section data as needed and remotely accumulates local tallies when crossing the boundary of the local spatial domain. initial results demonstrate that while the method does allow large problems to be run in a memory-limited environment, achieving scalability may be difficult due to inefficiencies in the current implementation of RMA operations.
Author: Paul Kollath Romano Publisher: ISBN: Category : Languages : en Pages : 199
Book Description
Monte Carlo particle transport methods are being considered as a viable option for high-fidelity simulation of nuclear reactors. While Monte Carlo methods offer several potential advantages over deterministic methods, there are a number of algorithmic shortcomings that would prevent their immediate adoption for full-core analyses. In this thesis, algorithms are proposed both to ameliorate the degradation in parallal efficiency typically observed for large numbers of processors and to offer a means of decomposing large tally data that will be needed for reactor analysis. A nearest-neighbor fission bank algorithm was proposed and subsequently implemented in the OpenMC Monte Carlo code. A theoretical analysis of the communication pattern shows that the expected cost is O([square root]N) whereas traditional fission bank algorithms are O(N) at best. The algorithm was tested on two supercomputers, the Intrepid Blue Gene/P and the Titan Cray XK7, and demonstrated nearly linear parallel scaling up to 163,840 processor cores on a full-core benchmark problem. An algorithm for reducing network communication arising from tally reduction was analyzed and implemented in OpenMC. The proposed algorithm groups only particle histories on a single processor into batches for tally purposes - in doing so it prevents all network communication for tallies until the very end of the simulation. The algorithm was tested, again on a full-core benchmark, and shown to reduce network communication substantially. A model was developed to predict the impact of load imbalances on the performance of domain decomposed simulations. The analysis demonstrated that load imbalances in domain decomposed simulations arise from two distinct phenomena: non-uniform particle densities and non-uniform spatial leakage. The dominant performance penalty for domain decomposition was shown to come from these physical effects rather than insufficient network bandwidth or high latency. The model predictions were verified with measured data from simulations in OpenMC on a full-core benchmark problem. Finally, a novel algorithm for decomposing large tally data was proposed, analyzed, and implemented/tested in OpenMC. The algorithm relies on disjoint sets of compute processes and tally servers. The analysis showed that for a range of parameters relevant to LWR analysis, the tally server algorithm should perform with minimal overhead. Tests were performed on Intrepid and Titan and demonstrated that the algorithm did indeed perform well over a wide range of parameters.
Author: S. Bassini Publisher: IOS Press ISBN: 1614998434 Category : Computers Languages : en Pages : 852
Book Description
The most powerful computers work by harnessing the combined computational power of millions of processors, and exploiting the full potential of such large-scale systems is something which becomes more difficult with each succeeding generation of parallel computers. Alternative architectures and computer paradigms are increasingly being investigated in an attempt to address these difficulties. Added to this, the pervasive presence of heterogeneous and parallel devices in consumer products such as mobile phones, tablets, personal computers and servers also demands efficient programming environments and applications aimed at small-scale parallel systems as opposed to large-scale supercomputers. This book presents a selection of papers presented at the conference: Parallel Computing (ParCo2017), held in Bologna, Italy, on 12 to 15 September 2017. The conference included contributions about alternative approaches to achieving High Performance Computing (HPC) to potentially surpass exa- and zetascale performances, as well as papers on the application of quantum computers and FPGA processors. These developments are aimed at making available systems better capable of solving intensive computational scientific/engineering problems such as climate models, security applications and classic NP-problems, some of which cannot currently be managed by even the most powerful supercomputers available. New areas of application, such as robotics, AI and learning systems, data science, the Internet of Things (IoT), and in-car systems and autonomous vehicles were also covered. As always, ParCo2017 attracted a large number of notable contributions covering present and future developments in parallel computing, and the book will be of interest to all those working in the field.
Author: Publisher: ISBN: Category : Languages : en Pages : 17
Book Description
Domain decomposition has been implemented in a Constructive Solid Geometry (CSG) Monte Carlo neutron transport code. Previous methods to parallelize a CSG code relied entirely on particle parallelism; but in our approach we distribute the geometry as well as the particles across processors. This enables calculations whose geometric description is larger than what could fit in memory of a single processor, thus it must be distributed across processors. In addition to enabling very large calculations, we show that domain decomposition can speed up calculations compared to particle parallelism alone. We also show results of a calculation of the proposed Laser Inertial-Confinement Fusion-Fission Energy (LIFE) facility, which has 5.6 million CSG parts.
Author: I. Lux Publisher: CRC Press ISBN: 1351083287 Category : Science Languages : en Pages : 530
Book Description
With this book we try to reach several more-or-less unattainable goals namely: To compromise in a single book all the most important achievements of Monte Carlo calculations for solving neutron and photon transport problems. To present a book which discusses the same topics in the three levels known from the literature and gives us useful information for both beginners and experienced readers. It lists both well-established old techniques and also newest findings.
Author: Matthew Joseph O'Brien
Author: Matthew Joseph O'Brien
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Author: 
Author: Nicholas Edward Horelik
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Author: Paul Kollath Romano
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Author: S. Bassini
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Author: I. Lux