Filamentation and forward Brillouin scatter of entire smoothed and aberrated laser beams

Still, C. H.; Berger, R. L.; Langdon, A. B.; Hinkel, D. E.; Suter, L. J.; Williams, E. A.

doi:10.1063/1.874055

Cited by 73 publications

(40 citation statements)

References 43 publications

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“…Such a pattern is representative of applications that perform many parallel Fast Fourier transform, e.g. pF3D [25], PARATEC, NAMD [26], and VASP [27]. Using the configuration presented in Table I, an MPI process's communicating partners are separated by multiples of 384 , i.e.…”

Section: Many To Many Pattern (M2m)mentioning

confidence: 99%

Maximizing Throughput on a Dragonfly Network

Jain

Bhatelé

et al. 2014

SC14: International Conference for High Performance Computing, Networking, Storage and Analysis

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Abstract-Interconnection networks are a critical resource for large supercomputers. The dragonfly topology, which provides a low network diameter and large bisection bandwidth, is being explored as a promising option for building multi-Petaflop/s and Exaflop/s systems. Unlike the extensively studied torus networks, the best choices of message routing and job placement strategies for the dragonfly topology are not well understood. This paper aims at analyzing the behavior of an interconnect based on the dragonfly topology for various routing strategies, job placement policies, and application communication patterns. Our study is based on a novel model that predicts traffic on individual links for direct, indirect, and adaptive routing strategies. We analyze results for individual communication patterns and some common parallel job workloads. The predictions presented in this paper are for a 100+ Petaflop/s prototype machine with 92,160 highradix routers and 8.8 million cores.

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Section: Many To Many Pattern (M2m)mentioning

confidence: 99%

Maximizing Throughput on a Dragonfly Network

Jain

Bhatelé

et al. 2014

SC14: International Conference for High Performance Computing, Networking, Storage and Analysis

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“…pF3D [17] is a multi-physics code used for studying laser plasma-interactions in the National Ignition Facility (NIF) experiments at LLNL. pF3D is a communication-heavy application and has been shown to benefit significantly from task mapping on Blue Gene/P [6].…”

Section: Results With Pf3dmentioning

confidence: 99%

Predicting application performance using supervised learning on communication features

Jain

Bhatelé

Robson

et al. 2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

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Task mapping on torus networks has traditionally focused on either reducing the maximum dilation or average number of hops per byte for messages in an application. These metrics make simplified assumptions about the cause of network congestion, and do not provide accurate correlation with execution time. Hence, these metrics cannot be used to reasonably predict or compare application performance for different mappings. In this paper, we attempt to model the performance of an application using communication data, such as the communication graph and network hardware counters. We use supervised learning algorithms, such as randomized decision trees, to correlate performance with prior and new metrics. We propose new hybrid metrics that provide high correlation with application performance, and may be useful for accurate performance prediction. For three different communication patterns and a production application, we demonstrate a very strong correlation between the proposed metrics and the execution time of these codes.

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“…pF3D [39] is a highly scalable parallel application used to study laserplasma interactions in experiments at the National Ignition Facility at LLNL. This code is routinely run on high-end supercomputers, such as the IBM Blue Gene/Q and Cray XE6 at very high processor counts.…”

Section: Laser-plasma Interaction Simulationsmentioning

confidence: 99%

Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time

Isaacs

Bremer

Jusufi

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Logical timeline and clustered logical timeline views from Ravel, a tool for visualizing parallel execution traces. Events are represented by boxes, colored by their wall-clock delay. The use of logical time reveals communication patterns and leverages developers' understanding of their program's structure. We use the logical time structure to cluster on any metric, which allow us to represent large-scale traces using explorable clusters while still depicting messages with full timelines for a subset of processes.Abstract-With the continuous rise in complexity of modern supercomputers, optimizing the performance of large-scale parallel programs is becoming increasingly challenging. Simultaneously, the growth in scale magnifies the impact of even minor inefficiencies -potentially millions of compute hours and megawatts in power consumption can be wasted on avoidable mistakes or sub-optimal algorithms. This makes performance analysis and optimization critical elements in the software development process. One of the most common forms of performance analysis is to study execution traces, which record a history of per-process events and interprocess messages in a parallel application. Trace visualizations allow users to browse this event history and search for insights into the observed performance behavior. However, current visualizations are difficult to understand even for small process counts and do not scale gracefully beyond a few hundred processes. Organizing events in time leads to a virtually unintelligible conglomerate of interleaved events and moderately high process counts overtax even the largest display. As an alternative, we present a new trace visualization approach based on transforming the event history into logical time inferred directly from happened-before relationships. This emphasizes the code's structural behavior, which is much more familiar to the application developer. The original timing data, or other information, is then encoded through color, leading to a more intuitive visualization. Furthermore, we use the discrete nature of logical timelines to cluster processes according to their local behavior leading to a scalable visualization of even long traces on large process counts. We demonstrate our system using two case studies on large-scale parallel codes.

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Filamentation and forward Brillouin scatter of entire smoothed and aberrated laser beams

Abstract: Laser-plasma interactions are sensitive to both the fine-scale speckle and the larger scale envelope intensity of the beam.

Cited by 73 publications

References 43 publications

Maximizing Throughput on a Dragonfly Network

Maximizing Throughput on a Dragonfly Network

Predicting application performance using supervised learning on communication features

Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time

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