E. Cormier-Michel

FastBit: interactively searching massive data

As scientific instruments and computer simulations produce more and more data, the task of locating the essential information to gain insight becomes increasingly difficult. FastBit is an efficient software tool to address this challenge. In this article, we present a summary of the key underlying technologies, namely bitmap compression, encoding, and binning. Together these techniques enable FastBit to answer structured (SQL) queries orders of magnitude faster than popular database systems. To illustrate how FastBit is used in applications, we present three examples involving a high-energy physics experiment, a combustion simulation, and an accelerator simulation. In each case, FastBit significantly reduces the response time and enables interactive exploration on terabytes of data.

Numerical methods for instability mitigation in the modeling of laser wakefield accelerators in a Lorentz-boosted frame

Modeling of laser-plasma wakefield accelerators in an optimal frame of reference [1] has been shown to produce orders of magnitude speed-up of calculations from first principles. Obtaining these speedups required mitigation of a high-frequency instability that otherwise limits effectiveness. In this paper, methods are presented which mitigated the observed instability, including an electromagnetic solver with tunable coefficients, its extension to accommodate Perfectly Matched Layers and Friedmans damping algorithms, as well as an efficient large bandwidth digital filter. It is observed that choosing the frame of the wake as the frame of reference allows for higher levels of filtering or damping than is possible in other frames for the same accuracy. Detailed testing also revealed the existence of a singular time step at which the instability level is minimized, independently of numerical dispersion. A combination of the techniques presented in this paper prove to be very efficient at controlling the instability, allowing for efficient direct modeling of 10GeV class laser plasma accelerator stages. The methods developed in this paper may have broader application, to other Lorentz-boosted simulations and Particle-In-Cell simulations in general.

High performance multivariate visual data exploration for extremely large data

One of the central challenges in modern science is the need to quickly derive knowledge and understanding from large, complex collections of data. We present a new approach that deals with this challenge by combining and extending techniques from high performance visual data analysis and scientific data management. This approach is demonstrated within the context of gaining insight from complex, time-varying datasets produced by a laser wakefield accelerator simulation. Our approach leverages histogram-based parallel coordinates for both visual information display as well as a vehicle for guiding a data mining operation. Data extraction and subsetting are implemented with state-of-the-art index/query technology. This approach, while applied here to accelerator science, is generally applicable to a broad set of science applications, and is implemented in a production-quality visual data analysis infrastructure. We conduct a detailed performance analysis and demonstrate good scalability on a distributed memory Cray XT4 system.

Numerical modeling of laser tunneling ionization in explicit particle-in-cell codes

Methods for the calculation of laser tunneling ionization in explicit particle-in-cell codes used for modeling laser-plasma interactions are compared and validated against theoretical predictions. Improved accuracy is obtained by using the direct current form for the ionization rate. Multi level ionization in a single time step and energy conservation have been considered during the ionization process. The effects of grid resolution and number of macro-particles per cell are examined. Implementation of the ionization algorithm in two different particle-in-cell codes is compared for the case of ionization-based electron injection in a laser-plasma accelerator.

Computational studies and optimization of wakefield accelerators

Laser- and particle beam-driven plasma wakefield accelerators produce accelerating fields thousands of times higher than radio-frequency accelerators, offering compactness and ultrafast bunches to extend the frontiers of high energy physics and to enable laboratory-scale radiation sources. Large-scale kinetic simulations provide essential understanding of accelerator physics to advance beam performance and stability and show and predict the physics behind recent demonstration of narrow energy spread bunches. Benchmarking between codes is establishing validity of the models used and, by testing new reduced models, is extending the reach of simulations to cover upcoming meter-scale multi-GeV experiments. This includes new models that exploit Lorentz boosted simulation frames to speed calculations. Simulations of experiments showed that recently demonstrated plasma gradient injection of electrons can be used as an injector to increase beam quality by orders of magnitude. Simulations are now also modeling accelerator stages of tens of GeV, staging of modules, and new positron sources to design next-generation experiments and to use in applications in high energy physics and light sources.

Characteristics of an envelope model for laser-plasma accelerator simulation

Simulation of laser-plasma accelerator (LPA) experiments is computationally intensive due to the disparate length scales involved. Current experiments extend hundreds of laser wavelengths transversely and many thousands in the propagation direction, making explicit PIC simulations enormously expensive and requiring massively parallel execution in 3D. Simulating the next generation of LPA experiments is expected to increase the computational requirements yet further, by a factor of 1000. We can substantially improve the performance of LPA simulations by modeling the envelope evolution of the laser field rather than the field itself. This allows for much coarser grids, since we need only resolve the plasma wavelength and not the laser wavelength, and therefore larger timesteps can be used. Thus an envelope model can result in savings of several orders of magnitude in computational resources. By propagating the laser envelope in a Galilean frame moving at the speed of light, dispersive errors can be avoided and simulations over long distances become possible. The primary limitation to this envelope model is when the laser pulse develops large frequency shifts, and thus the slowly-varying envelope assumption is no longer valid. Here we describe the model and its implementation, and show rigorous benchmarks for the algorithm, establishing second-order convergence and correct laser group velocity. We also demonstrate simulations of LPA phenomena such as self-focusing and meter-scale acceleration stages using the model.

Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data

Knowledge discovery from large and complex scientific data is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. The combination and close integration of methods from scientific visualization, information visualization, automated data analysis, and other enabling technologies -such as efficient data management- supports knowledge discovery from multi-dimensional scientific data. This paper surveys two distinct applications in developmental biology and accelerator physics, illustrating the effectiveness of the described approach.

Automatic Beam Path Analysis of Laser Wakefield Particle Acceleration Data

Numerical simulations of laser wakefield particle accelerators play a key role in the understanding of the complex acceleration process and in the design of expensive experimental facilities. As the size and complexity of simulation output grows, an increasingly acute challenge is the practical need for computational techniques that aid in scientific knowledge discovery. To that end, we present a set of data-understanding algorithms that work in concert in a pipeline fashion to automatically locate and analyze high energy particle bunches undergoing acceleration in very large simulation datasets. These techniques work cooperatively by first identifying features of interest in individual timesteps, then integrating features across timesteps, and based on the information derived perform analysis of temporally dynamic features. This combination of techniques supports accurate detection of particle beams enabling a deeper level of scientific understanding of physical phenomena than has been possible before. By combining efficient data analysis algorithms and state-of-the-art data management we enable high-performance analysis of extremely large particle datasets in 3D. We demonstrate the usefulness of our methods for a variety of 2D and 3D datasets and discuss the performance of our analysis pipeline.

New Developments in the Simulation of Advanced Accelerator Concepts

Improved computational methods are essential to the diverse and rapidly developing field of advanced accelerator concepts. We present an overview of some computational algorithms for laser‐plasma concepts and high‐brightness photocathode electron sources. In particular, we discuss algorithms for reduced laser‐plasma models that can be orders of magnitude faster than their higher‐fidelity counterparts, as well as important on‐going efforts to include relevant additional physics that has been previously neglected. As an example of the former, we present 2D laser wakefield accelerator simulations in an optimal Lorentz frame, demonstrating >10 GeV energy gain of externally injected electrons over a 2 m interaction length, showing good agreement with predictions from scaled simulations and theory, with a speedup factor of ∼2,000 as compared to standard particle‐in‐cell.

Recent results and future challenges for large scale Particle-In-Cell simulations of plasma-based accelerator concepts

The concept and designs of plasma-based advanced accelerators for high energy physics and photon science are modelled in the SciDAC COMPASS project with a suite of Particle-In-Cell codes and simulation techniques including the full electromagnetic model, the envelope model, the boosted frame approach and the quasi-static model. In this paper, we report the progress of the development of these models and techniques and present recent results achieved with large-scale parallel PIC simulations. The simulation needs for modelling the plasma-based advanced accelerator at the energy frontier is discussed and a path towards this goal is outlined.