Step emulsification is an attractive method for production of monodisperse drops. Its main advantage is the ability to parallelize many step emulsifier nozzles to achieve high production rates. However, step emulsification is sensitive to any obstructions at the nozzle exit. At high production rates, drops can accumulate at nozzle exits, disturb the formation of subsequent drops and impair monodispersity. As a result, parallelized step emulsifier devices typically do not work at maximum productivity. Here a design is introduced that parallelizes hundreds of step emulsifier nozzles, and effectively removes drops from the nozzle exits. The drop clearance is achieved by an open collecting channel, and is aided by buoyancy. Importantly, this clearance method avoids the use of a continuous phase flow for drop clearance and hence no shear is applied on the forming drops. The method works well for a wide range of drops, sizing from 30 to 1000 μm at production rates of 0.03 and 10 L per hour and achieved by 400 and 120 parallelized nozzles respectively.
more »
« less
Fast 3D Scattering Matrix Solver for Complex Media using Augmented Partial Factorization
We realize full-wave single-shot computations of the polarization-resolved scattering matrices of 3D complex media using the “augmented partial factorization” method. Our parallelized solver achieves three orders of magnitude speed-up compared to parallelized iterative FDFD method.
more »
« less
- Award ID(s):
- 2146021
- PAR ID:
- 10497747
- Publisher / Repository:
- Optica Publishing Group
- Date Published:
- ISBN:
- 978-1-957171-29-6
- Page Range / eLocation ID:
- JW4A.61
- Format(s):
- Medium: X
- Location:
- Tacoma, Washington
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Bringmann, Karl; Grohe, Martin; Puppis, Gabriele; Svensson, Ola (Ed.)Average linkage Hierarchical Agglomerative Clustering (HAC) is an extensively studied and applied method for hierarchical clustering. Recent applications to massive datasets have driven significant interest in near-linear-time and efficient parallel algorithms for average linkage HAC. We provide hardness results that rule out such algorithms. On the sequential side, we establish a runtime lower bound of n^{3/2-ε} on n node graphs for sequential combinatorial algorithms under standard fine-grained complexity assumptions. This essentially matches the best-known running time for average linkage HAC. On the parallel side, we prove that average linkage HAC likely cannot be parallelized even on simple graphs by showing that it is CC-hard on trees of diameter 4. On the possibility side, we demonstrate that average linkage HAC can be efficiently parallelized (i.e., it is in NC) on paths and can be solved in near-linear time when the height of the output cluster hierarchy is small.more » « less
-
Speckle patterns have been used widely in imaging techniques such as ghost imaging, dynamic speckle illumination microscopy, structured illumination microscopy, and photoacoustic fluctuation imaging. Recent advances in the ability to control the statistical properties of speckles has enabled the customization of speckle patterns for specific imaging applications. In this work, we design and create special speckle patterns for parallelized nonlinear pattern-illumination microscopy based on fluorescence photoswitching. We present a proof-of-principle experimental demonstration where we obtain a spatial resolution three times higher than the diffraction limit of the illumination optics in our setup. Furthermore, we show that tailored speckles vastly outperform standard speckles. Our work establishes that customized speckles are a potent tool in parallelized super-resolution microscopy.more » « less
-
Abstract. Lagrangian particle tracking schemes allow a wide range of flow and transport processes to be simulated accurately, but a major challenge is numerically implementing the inter-particle interactions in an efficient manner. This article develops a multi-dimensional, parallelized domain decomposition (DDC) strategy for mass-transfer particle tracking (MTPT) methods in which particles exchange mass dynamically. We show that this can be efficiently parallelized by employing large numbers of CPU cores to accelerate run times. In order to validate the approach and our theoretical predictions we focus our efforts on a well-known benchmark problem with pure diffusion, where analytical solutions in any number of dimensions are well established. In this work, we investigate different procedures for “tiling” the domain in two and three dimensions (2-D and 3-D), as this type of formal DDC construction is currently limited to 1-D. An optimal tiling is prescribed based on physical problem parameters and the number of available CPU cores, as each tiling provides distinct results in both accuracy and run time. We further extend the most efficient technique to 3-D for comparison, leading to an analytical discussion of the effect of dimensionality on strategies for implementing DDC schemes. Increasing computational resources (cores) within the DDC method produces a trade-off between inter-node communication and on-node work.For an optimally subdivided diffusion problem, the 2-D parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to around 2700 cores, reducing a 5 h simulation to 8 s, while the 3-D algorithm maintains appreciable speedup up to 1700 cores.more » « less
-
Abstract pyspeckitis a toolkit and library for spectroscopic analysis in Python. We describe thepyspeckitpackage and highlight some of its capabilities, such as interactively fitting a model to data, akin to the historically widely-usedsplotfunction inIRAF.pyspeckitemploys the Levenberg–Marquardt optimization method via thempfitandlmfitimplementations, and important assumptions regarding error estimation are described here. Wrappers to usepymcandemceeas optimizers are provided. A parallelized wrapper to fit lines in spectral cubes is included. As part of theastropyaffiliated package ecosystem,pyspeckitis open source and open development, and welcomes input and collaboration from the community.more » « less
An official website of the United States government
