Accelerating scientific codes by performance and accuracy modeling

Scientific software is often driven by multiple parameters that affect both accuracy and performance. Since finding the optimal configuration of these parameters is a highly complex task, it extremely common that the software is used suboptimally. In a typical scenario, accuracy requirements are imposed, and attained through suboptimal performance. In this paper, we present a methodology for the automatic selection of parameters for simulation codes, and a corresponding prototype tool. To be amenable to our methodology, the target code must expose the parameters affecting accuracy and performance, and there must be formulas available for error bounds and computational complexity of the underlying methods. As a case study, we consider the particle-particle particle-mesh method (PPPM) from the LAMMPS suite for molecular dynamics, and use our tool to identify configurations of the input parameters that achieve a given accuracy in the shortest execution time. When compared with the configurations suggested by expert users, the parameters selected by our tool yield reductions in the time-to-solution ranging between 10% and 60%. In other words, for the typical scenario where a fixed number of core-hours are granted and simulations of a fixed number of timesteps are to be run, usage of our tool may allow up to twice as many simulations. While we develop our ideas using LAMMPS as computational framework and use the PPPM method for dispersion as case study, the methodology is general and valid for a range of software tools and methods

Contextualizing Developmental Math Content into Introduction To Sociology In Community Colleges

Across community colleges in the United States, most students place into a developmental math course that they never pass. This can leave them without the math skills necessary to make informed decisions in major areas of social life and the college credential required for participation in growing sectors of our economy. One strategy for improving community college students’ pass rate in developmental math courses is the contextualization of developmental math content into the fabric of other courses. This article reviews an effort to contextualize developmental math content (i.e., elementary algebra) into Introduction to Sociology at Kingsborough Community College and Queensborough Community College, both of the City University of New York, during the spring 2016 semester. Data from a pre-test/post-test control-group design implemented across the two campuses reveals the significance of this strategy for some sociology students’ grasp of discrete mathematical skills and success in developmental math

A collection of three integration-free iPSCs derived from old male and female healthy subjects

Here, we present the characterization of three iPSC lines derived from dermal fibroblasts of old healthy subjects. Fibroblasts were reprogrammed using Sendai viral vectors encoding OCT4, SOX2, KLF4 and c-MYC. The iPSCs expressed endogenous pluripotency markers, could generate the three germ layers (ectoderm, mesoderm and endoderm), maintained a stable karyotype, and were free from Sendai vectors and reprogramming factors. These integration-free iPSCs can serve for establishing control cell cultures in studies searching for phenotypes and mechanisms that could potentially be dysregulated in degenerative diseases

HoughNet: Integrating Near and Long-Range Evidence for Bottom-Up Object Detection

© 2020, Springer Nature Switzerland AG.This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method. Inspired by the Generalized Hough Transform, HoughNet determines the presence of an object at a certain location by the sum of the votes cast on that location. Votes are collected from both near and long-distance locations based on a log-polar vote field. Thanks to this voting mechanism, HoughNet is able to integrate both near and long-range, class-conditional evidence for visual recognition, thereby generalizing and enhancing current object detection methodology, which typically relies on only local evidence. On the COCO dataset, HoughNet’s best model achieves 46.4 AP (and 65.1 AP50), performing on par with the state-of-the-art in bottom-up object detection and outperforming most major one-stage and two-stage methods. We further validate the effectiveness of our proposal in another task, namely, “labels to photo” image generation by integrating the voting module of HoughNet to two different GAN models and showing that the accuracy is significantly improved in both cases. Code is available at https://github.com/nerminsamet/houghnet

Effects of Pesticide Treatments on Nutrient Levels in Worker Honey Bees (\u3ci\u3eApis mellifera\u3c/i\u3e)

Honey bee colony loss continues to be an issue and no factor has been singled out as to the cause. In this study, we sought to determine whether two beekeeper-applied pesticide products, tau-fluvalinate and Fumagilin-B, and one agrochemical, chlorothalonil, impact the nutrient levels in honey bee workers in a natural colony environment. Treatments were performed in-hive and at three different periods (fall, spring, and summer) over the course of one year. Bees were sampled both at pre-treatment and two and four weeks post-treatment, weighed, and their protein and carbohydrate levels were determined using BCA and anthrone based biochemical assays, respectively. We report that, based on the pesticide concentrations tested, no significant negative impact of the pesticide products was observed on wet weight, protein levels, or carbohydrate levels of bees from treated colonies compared with bees from untreated control colonies

Effects of Pesticide Treatments on Nutrient Levels in Worker Honey Bees (\u3ci\u3eApis mellifera\u3c/i\u3e)

Honey bee colony loss continues to be an issue and no factor has been singled out as to the cause. In this study, we sought to determine whether two beekeeper-applied pesticide products, tau-fluvalinate and Fumagilin-B, and one agrochemical, chlorothalonil, impact the nutrient levels in honey bee workers in a natural colony environment. Treatments were performed in-hive and at three different periods (fall, spring, and summer) over the course of one year. Bees were sampled both at pre-treatment and two and four weeks post-treatment, weighed, and their protein and carbohydrate levels were determined using BCA and anthrone based biochemical assays, respectively. We report that, based on the pesticide concentrations tested, no significant negative impact of the pesticide products was observed on wet weight, protein levels, or carbohydrate levels of bees from treated colonies compared with bees from untreated control colonies

Foveated image processing for faster object detection and recognition in embedded systems using deep convolutional neural networks

Object detection and recognition algorithms using deep convolutional neural networks (CNNs) tend to be computationally intensive to implement. This presents a particular challenge for embedded systems, such as mobile robots, where the computational resources tend to be far less than for workstations. As an alternative to standard, uniformly sampled images, we propose the use of foveated image sampling here to reduce the size of images, which are faster to process in a CNN due to the reduced number of convolution operations. We evaluate object detection and recognition on the Microsoft COCO database, using foveated image sampling at different image sizes, ranging from 416×416 to 96×96 pixels, on an embedded GPU – an NVIDIA Jetson TX2 with 256 CUDA cores. The results show that it is possible to achieve a 4× speed-up in frame rates, from 3.59 FPS to 15.24 FPS, using 416×416 and 128×128 pixel images respectively. For foveated sampling, this image size reduction led to just a small decrease in recall performance in the foveal region, to 92.0% of the baseline performance with full-sized images, compared to a significant decrease to 50.1% of baseline recall performance in uniformly sampled images, demonstrating the advantage of foveated sampling