Experiences with parallel N-body simulation

Liu, Pangfeng; Bhatt, Sandeep N.

doi:10.1145/181014.181081

Cited by 32 publications

(26 citation statements)

References 25 publications

(42 reference statements)

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“…We also implemented the ORB version in a manner similar to that reported in [5,12], and found no significant performance differences with our costzones approach. Instead, using costzones allowed us to make easier comparisons with the CC-SAS implementation of the N-Body problem.…”

Section: N-body Problemmentioning

confidence: 89%

A Comparison of Three Programming Models for Adaptive Applications on the Origin2000

Shan¹,

Singh²,

Oliker³

et al. 2000

ACM/IEEE SC 2000 Conference (SC'00)

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Adaptive applications have computational workloads and communication patterns which change unpredictably at runtime, requiring dynamic load balancing to achieve scalable performance on parallel machines. Efficient parallel implementations of such adaptive applications is therefore a challenging task. In this paper, we compare the performance of and the programming effort required for two major classes of adaptive applications under three leading parallel programming models on an SGI Origin2000 system, a machine which supports all three models efficiently. Results indicate that the three models deliver comparable performance; however, the implementations differ significantly beyond merely using explicit messages versus implicit loads/stores even though the basic parallel algorithms are similar. Compared with the message-passing (using MPI) and SHMEM programming models, the cache-coherent shared address space (CC-SAS) model provides substantial ease of programming at both the conceptual and program orchestration levels, often accompanied by performance gains. However, CC-SAS currently has portability limitations and may suffer from poor spatial locality of physically distributed shared data on large numbers of processors.

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Section: N-body Problemmentioning

confidence: 89%

A Comparison of Three Programming Models for Adaptive Applications on the Origin2000

Shan¹,

Singh²,

Oliker³

et al. 2000

ACM/IEEE SC 2000 Conference (SC'00)

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“…In order to address computationally intensive problems, NOWs have been used in a wide range of fields including large-scale databases, scientific computations, computer graphics, multimedia, wave propagation predictions, and telecommunication systems simulations [18,62,54,56,2,39,71,59]. …”

Section: Related Workmentioning

confidence: 99%

“…Here, the movement of a set of particles is simulated under the influence of gravitational, electrostatic and Vander Waals attractions [69,10,36]. Two prevalent forms of the N -body problem known as the Barnes-Hut and the Fast multi-pole (FMM) methods have been implemented using message passing and shared-memory architectures [11,31,54,74,47,57,37]. In this context, computation-partitioning and workload-balancing scheduling approaches have been proposed in [69].…”

Section: Related Workmentioning

confidence: 99%

Radio-wave propagation prediction using ray-tracing techniques on a network of workstations (NOW)

Chen¹,

Delis

Bertoni³

2004

Journal of Parallel and Distributed Computing

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Ray-tracing based radio wave propagation prediction models play an important role in the design of contemporary wireless networks as they may now take into account diverse physical phenomena including reflections, diffractions, and diffuse scattering. However, such models are computationally expensive even for moderately complex geographic environments. In this paper, we propose a computational framework that functions on a network of workstations (NOW) and helps speed up the lengthy prediction process. In ray-tracing based radio propagation prediction models, orders of diffractions are usually processed in a stage-by-stage fashion. In addition, various source points (transmitters, diffraction corners, or diffuse scattering points) and different ray-paths require different processing times. To address these widely varying needs, we propose a combination of the phase-parallel and manager/workers paradigms as the underpinning framework. The phase-parallel component is used to coordinate different computation stages, while the manager/workers paradigm is used to balance workloads among nodes within each stage. The original computation is partitioned into multiple small tasks based on either raypath-level or source-point-level granularity. Dynamic load-balancing scheduling schemes are employed to allocate the resulting tasks to the workers.We also address issues regarding main memory consumption, intermediate data assembly, and final prediction generation. We implement our proposed computational model on a NOW configuration by using the message passing interface (MPI) standard. Our experiments with real and synthetic building and terrain databases show that, when no constraint is imposed on the main memory consumption, the proposed prediction model performs very well and achieves nearly linear speedups under various workload. When main memory consumption is a concern, our model still delivers very promising performance rates provided that the complexity of the involved computation is high, so that the extra computation and communication overhead introduced by the proposed model do not dominate the original computation. The accuracy of prediction results and the achievable speedup rates can be significantly improved when 3D building and terrain databases are used and/or diffuse scattering effect is taken into account.Indexing Terms: Ray-tracing based radio propagation prediction model, Radio wave prediction model on Network of Workstations (NOW), Data or control domain decomposition, Dynamic workload-balancing scheduling schemes.

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“…Historically, the first decomposition proposed for Nbody algorithms is the Orthogonal Recursive Bisection (ORB) for the Barnes-Hut algorithm [8,14] (which can be extended to the FMM): the computation space is recursively halved along one dimension. Other decompositions have then been introduced: they rely directly on the octree space decomposition thanks to space-filling curves and cost functions.…”

Section: Introductionmentioning

confidence: 99%

“…The costzones decomposition has been shown to be more efficient that ORB on common address space architecture in [13] (multi-thread mode) and similar decompositions, based on Morton or Hilbert ordering, have also been prefered to ORB on distributed memory architecture with message passing [12,15] (multi-process mode). Moreover, in order to obtain efficient parallelization in message passing environment, communications have to be overlaped with computation, small messages have to be aggregated into bigger ones (large-grain communications), communications have to ordered so as to avoid contention, and sender-driven communications have to be prefered to receiver-initiated communications [7,8,12,15].…”

Section: Introductionmentioning

confidence: 99%