Tesla K40 Research Articles

High-throughput Protein Sequence Alignment on Multi-core Systems

Rapid evolution in sequencing technologies results in generating data on an enormous scale. A focal and main challenge in analyzing data at such a large scale is the alignment of the DNA/Protein sequences, whereby reads are compared to the reference sequences. To find similar sequences, alignment algorithms are used to align a query sequence with the database. Alignment algorithms can be utilized to classify the source of a sequence, to discover similarities among the organisms, or to deduce a progenitor connection. A wide range of algorithms for alignment has been developed in recent years. In this paper, an accurate method of accelerating such algorithms using GPUs has been investigated. A Swiss-Prot database has been processed using GPU implemented Smith-Waterman Sequence Alignment Algorithm. The first step in the process generates the alignment scores but not the actual alignment. Various available alignment tools like ssearch2 are then utilized to align the output file generated during the first step. The performance of GPU-accelerated implementation as compared to other techniques is then evaluated for performance /throughput improvement. Swiss-Prot database was aligned using various alignment tools. NVIDIA TESLA K40 GPU is being utilized for generating the results for this research. This implementation achieves the performance of 44.3 Giga cell updates per second (GCUPS), which is 22.9 times better than its implementation on GTX 275. Performance is improved as the workload of sequences of equal length is equally distributed among all the threads on Multiprocessors of GPU.

Вычислительные аспекты математического моделирования гидробиологических процессов в мелководном водоеме

Статья посвящена изучению нелинейных эффектов в динамике популяции промысловой рыбы пиленгас Азовского моря при низкой и высокой ее численности с учетом эффекта Олли, конкуренции за ресурсы, таксиса, вылова, пространственного распределения биогенных веществ и детрита на основе многовидовой модели взаимодействия планктона и рыб. Дискретный аналог разработанной модельной задачи водной экологии, входящей в состав программного комплекса, получен на основе схем второго порядка точности с учетом частичной заполненности расчетных ячеек. Возникающая в процессе дискретизации система сеточных уравнений большой размерности была решена на основе модифицированного попеременно-треугольного метода, имеющего наибольшую скорость сходимости при условии асимптотической устойчивости разностных схем для параболических уравнений, эффективность которого была улучшена на основе уточненных спектральных оценок. Разработка эффективных параллельных алгоритмов численной реализации поставленной задачи биологической кинетики, ориентированных на многопроцессорную вычислительную систему (МВС) и графический ускоритель NVIDIA Tesla K80 с модификацией формата хранения данных, позволила анализировать процессы воспроизводства популяций биогидроценоза в режиме реального и ускоренного времени. Paper covers the research of nonlinear effects in population dynamics of the pelengas commercial fish of the Azov Sea taking into account the Allee effect, competition for resources, taxis, catching, spatial distribution of biogenic matter and detritus based on a multi-species model of plankton and fish interaction. Discrete analogue of developed model problem of water ecology, included in a software complex, were calculated using schemes of second order of accuracy taking into account the partial filling of computational cells. The system of grid equations of large dimension, arising at discretization, has been solved on the basis of adaptive modified alternately triangual variational method. Effective parallel algorithms were developed for numerical implementation of biological kinetics problem and oriented on multiprocessor computer system and NVIDIA Tesla K80 graphics accelerator with the data storage format modification. Due to it, the reproduction processes of biogeocenose populations have been analyzed in real and accelerated time.

GPU acceleration of Darwin read overlapper for de novo assembly of long DNA reads

BackgroundIn Overlap-Layout-Consensus (OLC) based de novo assembly, all reads must be compared with every other read to find overlaps. This makes the process rather slow and limits the practicality of using de novo assembly methods at a large scale in the field. Darwin is a fast and accurate read overlapper that can be used for de novo assembly of state-of-the-art third generation long DNA reads. Darwin is designed to be hardware-friendly and can be accelerated on specialized computer system hardware to achieve higher performance.ResultsThis work accelerates Darwin on GPUs. Using real Pacbio data, our GPU implementation on Tesla K40 has shown a speedup of 109x vs 8 CPU threads of an Intel Xeon machine and 24x vs 64 threads of IBM Power8 machine. The GPU implementation supports both linear and affine gap, scoring model. The results show that the GPU implementation can achieve the same high speedup for different scoring schemes.ConclusionsThe GPU implementation proposed in this work shows significant improvement in performance compared to the CPU version, thereby making it accessible for utilization as a practical read overlapper in a DNA assembly pipeline. Furthermore, our GPU acceleration can also be used for performing fast Smith-Waterman alignment between long DNA reads. GPU hardware has become commonly available in the field today, making the proposed acceleration accessible to a larger public. The implementation is available at https://github.com/Tongdongq/darwin-gpu.

EBrainII: a 3\xa0kW Realtime Custom 3D DRAM Integrated ASIC Implementation of a Biologically Plausible Model of a Human Scale Cortex

The Artificial Neural Networks (ANNs), like CNN/DNN and LSTM, are not biologically plausible. Despite their initial success, they cannot attain the cognitive capabilities enabled by the dynamic hierarchical associative memory systems of biological brains. The biologically plausible spiking brain models, e.g., cortex, basal ganglia, and amygdala, have a greater potential to achieve biological brain like cognitive capabilities. Bayesian Confidence Propagation Neural Network (BCPNN) is a biologically plausible spiking model of the cortex. A human-scale model of BCPNN in real-time requires 162 TFlop/s, 50 TBs of synaptic weight storage to be accessed with a bandwidth of 200 TBs. The spiking bandwidth is relatively modest at 250 GBs/s. A hand-optimized implementation of rodent scale BCPNN has been done on Tesla K80 GPUs require 3 kWs, we extrapolate from that a human scale network will require 3 MWs. These power numbers rule out such implementations for field deployment as cognition engines in embedded systems.The key innovation that this paper reports is that it is feasible and affordable to implement real-time BCPNN as a custom tiled application-specific integrated circuit (ASIC) in 28 nm technology with custom 3D DRAM - eBrainII - that consumes 3 kW for human scale and 12 watts for rodent scale. Such implementations eminently fulfill the demands for field deployment.

Graphics process unit accelerated lattice Boltzmann simulation of indoor air flow: Effects of sub-grid scale model in large-eddy simulation

In this present study, three-dimensional lattice Boltzmann method is implemented with the popular turbulence modeling method large-eddy simulation incorporating three different non-dynamic sub-grid scale models Smagorinsky, Vreman, and wall-adapting local eddy-viscosity for finding the inhomogeneous turbulent airflow patterns inside a model room with a partition. The large eddy simulation-lattice Boltzmann method code is validated with the experimental results of Posner’s model, where the model room having one partition at the bottom, one inlet, an outlet placed at top wall considered for the comparisons. The lattice Boltzmann method code is also validated without any sub-grid scale model with the results of lid-driven flow in a cubic cavity. The present numerical simulations are performed by the graphics process unit accelerated parallel programs using compute unified device architecture C platform. Double precession capable a Tesla k40 with 2880 compute unified device architecture cores NVIDIA graphics process unit card has been used for these simulations. Graphics processor units have gained popularity in recent years as a propitious platform for numerical simulation of fluid dynamics. In fact, faster computational task performance in graphics process units is one of the key factors for researchers to choose graphics process unit over conventional central processing units for the implementation of data-intensive numerical methods like lattice Boltzmann method. The effects of the sub-grid scale model have been evaluated in terms of the mean velocity profiles, streamlines as well as turbulence characteristics and found that there are significant differences in the results due to the different sub-grid scale models.

ZMCintegral-v5: Support for Integrations with the scanning of large parameter space on multi-GPUs

In this updated version of ZMCintegral, we have added the functionality of parameter scan for integrations with a large parameter space (up to 1010 points to be scanned). The Python API is kept the same as the previous ones and users have full flexibility to define their own integrands. The performance of the new functionality is tested for multi-nodes conditions. Program summaryProgram Title: ZMCintegralProgram Files doi:http://dx.doi.org/10.17632/p7wc7k6mpp.2Licensing provisions: Apache-2.0Programming language: PythonJournal reference of previous version: Comput. Phys. Commun. 248 (2020) 106962Does the new version supersede the previous version?: YesReasons for the new version: For many physical cases, one usually encounters integrations containing a large parameter space [1,2,3,4]. In these conditions, integrations of the same form (but with different parameters) need be evaluated for millions of times. However, our previous versions [5] mainly focus on a single integration of high dimension, hence lack the good performance for many integrations of relatively high dimensions (e.g., the scan of parameter space). Therefore, we have added the demanding functionality of parameter scan. For each point in the parameter space, an integration will be evaluated, and all these integrations will be allocated automatically.Summary of revisions:•Integration with parameter space. We use the six-dimensional integration (1)f(x1,x2,x3,x4)=(∏k=16∫01dyk)sin(∑j6yj+∑l=14xl),to demonstrate the functionality of parameter scan. Here, the component of each parameter point xl∈{x1,x2,x3,x4} takes values in list [0,1,2,⋯,99], thus forming a parameter space of size 1004 (i.e., 108 points to be scanned). The allocation of the thread and block configurations will be handled by the code automatically. •Test on multiple (three) nodes. The hardware conditions for the three nodes are Intel(R) Xeon(R) CPU E5-2620 v3@2.40 GHz CPU with 24 processors + 4 Nvidia Tesla K40m GPUs, Intel(R) Xeon(R) CPU E5-2680 V4@2.40 GHz CPU with 10 processors + 2 Nvidia Tesla K80 GPUs, and Intel(R) Xeon(R) Silver 4110 CPU@2.10 GHz CPU with 10 processors + 1 Nvidia Tesla V100 GPU. These three nodes are in a local area network. We perform the integration of Eq. (1) independently for 10 times. The results are shown in Table 1. Note that the accuracy of the integrations purely depends on the number of samples in direct Monte Carlo method.Nature of problem: ZMCintegral is an easy to use Python package for doing high dimensional integrations on distributed GPU clusters. With the Python libraries Numba [6] and Ray [7], as well as the NVIDIA CUDA [8], ZMCintegral provides a succinct Python interface for evaluating numerical integrations in physical problems. In this new version, we have mainly focused on one kind of problem where the integration contains a large parameter space (up to 1010 parameter points).Solution method: This new version of ZMCintegral contains two Python classes. One class (ZMCintegral_normal, similar to our previous version), which is for high-dimensional integration, uses stratified-sampling and heuristic-tree-search technics. The other class (ZMCintegral_functional, the new functionality) uses direct Monte Carlo method and distributes the tasks of integrations (with each containing a different parameter point) on various devices automatically. In ZMCintegral_functional, the integration is performed on a single GPU thread and each thread gives the result of the integration for one specific parameter point.Additional comments: If the integrations are high-dimensional (e.g. dimensionality of 8–12), we encourage users to use ZMCintegral_normal. If the integrations are middle-dimensional (e.g. dimensionality of 1-7) but have a large parameter space, we suggest users to try ZMCintegral_functional. The detailed instructions can be found here: [9]. Declaration of Competing InterestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment The authors are supported in part by the Major State Basic Research Development Program (973 Program) in China under Grant No. 2015CB856902 and by the National Natural Science Foundation of China (NSFC) under Grant No. 11535012. The Computations are performed at the GPU servers of department of modern physics at USTC. We are thankful for the valuable discussions with Prof. Qun Wang of department of modern physics at USTC.

Fully Automated Myocardial Strain Estimation from Cardiovascular MRI-tagged Images Using a Deep Learning Framework in the UK Biobank.

PurposeTo demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac MRI–tagged images.Materials and MethodsIn this retrospective cross-sectional study, 4508 cases from the U.K. Biobank were split randomly into 3244 training cases, 812 validation cases, and 452 test cases. Ground truth myocardial landmarks were defined and tracked by manual initialization and correction of deformable image registration using previously validated software with five readers. The fully automatic framework consisted of (a) a convolutional neural network (CNN) for localization and (b) a combination of a recurrent neural network (RNN) and a CNN to detect and track the myocardial landmarks through the image sequence for each slice. Radial and circumferential strain were then calculated from the motion of the landmarks and averaged on a slice basis.ResultsWithin the test set, myocardial end-systolic circumferential Green strain errors were −0.001 ± 0.025, −0.001 ± 0.021, and 0.004 ± 0.035 in the basal, mid-, and apical slices, respectively (mean ± standard deviation of differences between predicted and manual strain). The framework reproduced significant reductions in circumferential strain in participants with diabetes, hypertensive participants, and participants with a previous heart attack. Typical processing time was approximately 260 frames (approximately 13 slices) per second on a GPU with 12 GB RAM compared with 6–8 minutes per slice for the manual analysis.ConclusionThe fully automated combined RNN and CNN framework for analysis of myocardial strain enabled unbiased strain evaluation in a high-throughput workflow, with similar ability to distinguish impairment due to diabetes, hypertension, and previous heart attack.Published under a CC BY 4.0 license.Supplemental material is available for this article.

Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks.

Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty.

A Comparative Analysis of Data Augmentation Approaches for Magnetic Resonance Imaging (MRI) Scan Images of Brain Tumor.

Introduction:Machine Learning (ML) is a rapidly growing subfield of Artificial Intelligence (AI). It is used for different purposes in our daily life such as face recognition, speech recognition, text translation in different languages, weather prediction, and business prediction. In parallel, ML also plays an important role in the medical domain such as in medical imaging. ML has various algorithms that need to be trained with large volumes of data to produce a well-trained model for prediction.Aim:The aim of this study is to highlight the most suitable Data Augmentation (DA) technique(s) for medical imaging based on their results.Methods:DA refers to different approaches that are used to increase the size of datasets. In this study, eight DA approaches were used on publicly available low-grade glioma tumor datasets obtained from the Tumor Cancer Imaging Archive (TCIA) repository. The dataset included 1961 MRI brain scan images of low-grade glioma patients. You Only Look Once (YOLO) version 3 model was trained on the original dataset and the augmented datasets separately. A neural network training/testing ecosystem named as supervisely with Tesla K80 GPU was used for YOLO v3 model training on all datasets.Results:The results showed that the DA techniques rotate at 180o and rotate at 90o performed the best as data enhancement techniques for medical imaging.Conclusion:Rotation techniques are found significant to enhance the low volume of medical imaging datasets.

YOLO-ACN: Focusing on Small Target and Occluded Object Detection

To further improve the speed and accuracy of object detection, especially small targets and occluded objects, a novel and efficient detector named YOLO-ACN is presented. The detector model is inspired by the high detection accuracy and speed of YOLOv3, and it is improved by the addition of an attention mechanism, a CIoU (complete intersection over union) loss function, Soft-NMS (non-maximum suppression), and depthwise separable convolution. First, the attention mechanism is introduced in the channel and spatial dimensions in each residual block to focus on small targets. Second, CIoU loss is adopted to achieve accurate bounding box (BBox) regression. Besides, to filter out a more accurate BBox and avoid deleting occluded objects in dense images, the CIoU is applied in the Soft-NMS, and the Gaussian model in the Soft-NMS is employed to suppress the surrounding BBox. Third, to significantly reduce the parameters and improve the detection speed, standard convolution is replaced by depthwise separable convolution, and hard-swish activation function is utilized in deeper layers. On the MS COCO dataset and infrared pedestrian dataset KAIST, the quantitative experimental results show that compared with other state-of-the-art models, the proposed YOLO-ACN has high accuracy and speed in detecting small targets and occluded objects. YOLO-ACN reaches a mAP50 (mean average precision) of 53.8% and an APs (average precision for small objects) of 18.2% at a real-time speed of 22 ms on the MS COCO dataset, and the mAP for a single class on the KAIST dataset even reaches over 80% on an NVIDIA Tesla K40.

A Deep Learning-Based Approach for Breast BI-RADS Prediction on Shear Wave Elastography Images

Background: Breast cancer is the most common type of cancer among women. About one of every eight women is diagnosed with breast cancer during her lifetime. Malignant tissue is stiffer than normal and benign tissues. This stiffness could be evaluated by elastography. The American College of Radiology (ACR) has published a quality assurance tool named Breast Imaging-Reporting and Data System (BI-RADS) to standardize breast cancer reporting. Although it was originally designed to use with mammography, it now contains several features for various imaging modalities. Among technologies, shear wave elastography (SWE) has shown promising results in breast lesion classification. Objectives: In this paper, we present the capability of the convolutional neural network in the prediction of B-RADS using SWE images. Methods: A comprehensive dataset of SWE images of breast tissue was provided using Esaote MyLab™ 9 and Aupersonic Aixplorer systems. Two hundred images related to breasts with different BI-RADS stages were gathered from the Cancer Institute, Imam Khomeini Medical Center (UICC). The data augmentation with a factor of 10 was applied to the prepared dataset. Some patients had multiple lesions and for each lesion, one or two images were acquired and stored in DICOM standards. The gold standard for the evaluation of the proposed algorithm was a biopsy, which was performed on all the examined lesions. A novel convolutional neural network was applied to the dataset to extract the visual features of images. The architecture was based on Densenet architecture, which was modified for our purpose. We used the network in both pre-training and end-to-end training strategies and the results were compared. The network was pre-trained on the Imagenet dataset due to the lack of a sufficient dataset. On the other hand, with data augmentation, the network underwent a full training strategy. Finally, the classification layer was a softmax layer, which was used to decide on the benignity or malignancy of the lump. The training and testing procedures for tumor classification were employed with five-fold cross-validation. The entire dataset was randomly divided into five equal-sized subsets on the premise that multiple images acquired from the same patient were assigned to the same subset. Four subsets together were used for training and the remaining one for testing and this process was repeated five times such that each subset was used once as the test set. Results: The processing hardware had a 12 GB RAM, a GPU-based graphics card with 2496 CUDA cores (Tesla K80), and an Intel Xeon CPU. The network implementation was done in the Python environment with Tensorflow r1.12 and Keras 2.2.4. The results of the proposed methods were satisfying in both pre-training and end-to-end training approaches. We used various evaluation metrics including precision, recall, F1-score, ROC curve, and training time for both strategies. The precision, recall, and F1-score were 0.93, 0.95, and 0.94 for the Densenet architecture trained from scratch and 0.97, 0.94, and 0.95 for the transfer learning approach (see Table 1). The ROC curve was plotted for both approaches and the area under the curves (AUCs) were calculated. The transfer learning approach yielded an AUC of 0.98, whereas this parameter was 0.94 for the fully-trained approach (see Figure 1). Finally, the training time of transfer learning approach was one-fifth the time of training from scratch, as it was anticipated. Conclusion: The results showed the superiority of the transfer learning approach in tumor classification. Higher statistical metrics with lower training time makes this approach more compatible with SWE images.

Experimental analysis and evaluation of wide residual networks based agricultural disease identification in smart agriculture system

Specialised pest and disease control in the agricultural crops industry have been a high-priority issue. Due to great cost-effectiveness and efficient automation, computer vision (CV)–based automatic pest or disease identification techniques are widely utilised in the smart agricultural systems. As rapid development of artificial intelligence, in the field of computer vision–based agricultural pest identification, an increasing number of scholars have begun to move their attentions from traditional machine learning models to deep learning techniques. However, so far, deep learning techniques still have been suffering from many problems such as limited data samples, cost-effectiveness of network structure, and high image quality requirements. These issues greatly limit the potential utilisation of deep-learning techniques into smart agricultural systems. This paper aims at investigating utilization of one new deep-learning model WRN (wide residual networks) into CV-based automatic disease identification problem. We first built up a large-scale agricultural disease images dataset containing over 36,000 pieces of diseases, which includes typical types of disease in tomato, potato, grape, corn and apple. Then, we analysed and evaluated wide residual networks algorithm using the Tesla K80 graphics processor (GPU) in the TensorFlow deep-learning framework. A set of comprehensive experimental protocols have been designed in comparing with GoogLeNet Inception V4 regarding several benchmarks. The experimental results indicate that (1) under WRN architecture, Softmax loss function gives a faster convergence and improved accuracy than GoogLeNet inception V4 network. (2) While WRN shows a good effect for identification of agricultural diseases, its effectiveness has a strong need on the number of training samples of dataset like at least 36 k images in our experiment. (3) The overall performance is better than 800 sheets. The disease identification results show that the WRN model can be applied to the identification of agricultural diseases.

Accelerating DNN-based 3D point cloud processing for mobile computing

3D point cloud data, which are produced by various 3D sensors such as LIDAR and stereo cameras, have been widely deployed by industry leaders such as Google, Uber, Tesla, and Mobileye, for mobile robotic applications such as autonomous driving and humanoid robots. Point cloud data, which are composed of reliable depth information, can provide accurate location and shape characteristics for scene understanding, such as object recognition and semantic segmentation. However, deep neural networks (DNNs), which directly consume point cloud data, are particularly computation-intensive because they have to not only perform multiplication-and-accumulation (MAC) operations but also search neighbors from the irregular 3D point cloud data. Such a task goes beyond the capabilities of general-purpose processors in real-time to figure out the solution as the scales of both point cloud data and DNNs increase from application to application. We present the first accelerator architecture that dynamically configures the hardware on-the-fly to match the computation of both neighbor point search and MAC computation for point-based DNNs. To facilitate the process of neighbor point search and reduce the computation costs, a grid-based algorithm is introduced to search neighbor points from a local region of grids. Evaluation results based on the scene recognition and segmentation tasks show that the proposed design harvests 16.4$\times$ higher performance and saves 99.95% of energy than an NVIDIA Tesla K40 GPU baseline in point cloud scene understanding applications.

Redesigning the rCUDA communication layer for a better adaptation to the underlying hardware

SummaryThe use of Graphics Processing Units (GPUs) has become a very popular way to accelerate the execution of many applications. However, GPUs are not exempt from side effects. For instance, GPUs are expensive devices which additionally consume a non‐negligible amount of energy even when they are not performing any computation. Furthermore, most applications present low GPU utilization. To address these concerns, the use of GPU virtualization has been proposed. In particular, remote GPU virtualization is a promising technology that allows applications to transparently leverage GPUs installed in any node of the cluster. In this paper, the remote GPU virtualization mechanism is comparatively analyzed across three different generations of GPUs. The first contribution of this study is an analysis about how the performance of the remote GPU virtualization technique is impacted by the underlying hardware. To that end, the Tesla K20, Tesla K40, and Tesla P100 GPUs along with FDR and EDR InfiniBand fabrics are used in the study. The analysis is performed in the context of the rCUDA middleware. It is clearly shown that the GPU virtualization middleware requires a comprehensive design of its communication layer, which should be perfectly adapted to every hardware generation in order to avoid a reduction in performance. This is precisely the second contribution of this work, ie, redesigning the rCUDA communication layer in order to improve the management of the underlying hardware. Results show that it is possible to improve bandwidth up to 29.43%, which translates into up to 4.81% average less execution time in the performance of the analyzed applications.

Parallel Fourier ptychographic microscopy for high-throughput screening with 96 cameras (96 Eyes)

We report the implementation of a parallel microscopy system (96 Eyes) that is capable of simultaneous imaging of all wells on a 96-well plate. The optical system consists of 96 microscopy units, where each unit is made out of a four element objective, made through a molded injection process, and a low cost CMOS camera chip. By illuminating the sample with angle varying light and applying Fourier Ptychography, we can improve the effective brightfield imaging numerical aperture of the objectives from 0.23 to 0.3, and extend the depth of field from ±5 μm to ±15 μm. The use of Fourier Ptychography additionally allows us to computationally correct the objectives’ aberrations out of the rendered images, and provides us with the ability to render phase images. The 96 Eyes acquires raw data at a rate of 0.7 frame per second (all wells) and the data are processed with 4 cores of graphical processing units (GPUs; GK210, Nvidia Tesla K80, USA). The system is also capable of fluorescence imaging (excitation = 465 nm, emission = 510 nm) at the native resolution of the objectives. We demonstrate the capability of this system by imaging S1P1-eGFP-Human bone osteosarcoma epithelial (U2OS) cells.

Compressible lattice Boltzmann simulations on high‐performance and low‐cost GeForce GPU

SummaryThe feasibility of compressible lattice Boltzmann simulations on high‐performance and low‐cost NVIDIA GeForce GPU is studied to enhance computational efficiency of compressible lattice Boltzmann by using much cheaper computing hardware. The NVIDIA GeForce GTX 1080 GPU, which has similar single precision operation computing performance with NVIDIA scientific computational Tesla K80 GPU and much lower price (seven to ten times) than the latter, is used in this work. The problem of GeForce GPUs is with low double precision operation performance and lack of error‐correcting code memory, which will influence the accuracy of compressible lattice Boltzmann simulations. To evaluate its feasibility, the double‐distribution‐function lattice Boltzmann method is adopted for compressible fluid flows. Three typical compressible problems are tested, ie, the Riemann problem, the regular shock reflection problem, and the supersonic boundary layer problem. The results by GeForce GPU with single precision operation have numerical fluctuations in the simulations with shock waves. Moreover, GeForce GPUs have similar numerical accuracy with CPUs in the regular shock reflection problem and supersonic boundary layer problem.

GPU-Accelerated Computation of Time-Evolving Electromagnetic Backscattering Field From Large Dynamic Sea Surfaces

An efficient facet-based composite scattering model (FBCSM) is developed for calculating the time-evolving electromagnetic (EM) scattering field (TESF) to study the normalized radar cross section and Doppler spectrum characteristics from dynamic sea surfaces. The dynamic sea surface comprises two-scale profiles: small-scale capillary ripples modulated by large-scale gravity waves, which are modeled by millions of small facets. In microwave bands, two scattering mechanisms, quasi-specular scattering with respect to gravity waves and Bragg scattering with respect to ripples, are taken into account in the FBCSM for computation of the time-evolving EM scattering field under diverse polarizations. However, it may be very time-consuming and difficult to calculate the TESF due to the high resolution and dynamic complexity of the large dynamic sea surface. In this paper, the NVIDIA Tesla K80 graphics processing unit (GPU) with the compute unified device architecture is utilized to improve the computational performance of the TESF. The whole GPU-based TESF computation includes the optimal use of temporary variables, shared memory, constant memory and register, fast-math compiler options, asynchronous data transfer, and the most suitable block size and number of registers. By utilizing the proposed five improvement strategies, a significant speedup of $1200 \times $ can be achieved for computation of TESF from large dynamic sea surfaces for microwave bands compared with the single-threaded C program executed on the Intel(R) Core(TM) i5-3450 CPU.

Optimized finite difference method with artificial dissipation for under-resolved unsteady incompressible flow computations using kinetically reduced local Navier-Stokes equations

Under-resolved unsteady incompressible flow computations employed on coarser grids are presented. Kinetically Reduced Local Navier-Stokes (KRLNS) equations is a newly method to deal with unsteady incompressible flows, which is applicable to the unsteady incompressible flows without the need for sub-iterations and is capable of capturing the correct transient behavior. To stabilize the computations and achieve high accuracy, the KRLNS equations is discretized with higher order standard and optimized types of central finite difference method (FDM) together with artificial dissipation or spatial filter and is integrated by using 4-stage Runge-Kutta method. Numerical simulations of 2D doubly periodic shear layers are carried out on coarser regular grids and the computed solutions are compared with those obtained on finer grids. The parallel computations are implemented on multiple GPU (Tesla K40) system with 4 GPUs, based on the domain decomposition method and the acceleration is investigated. It is found that the solution obtained by optimized type of FDM is more accurate than that of standard one especially for much coarser grids, and that the proposed approach is easy to perform the parallel computations and obtain a large acceleration according to the number of GPUs used.

Молекулярная динамика в силовом поле FF14SB в воде TIP4P-Ew, и в силовом поле FF15IPQ в воде SPC/Eb: сравнительный анализ на GPU и CPU

A comparative analysis of computational efficiency and scalability of molecular dynamics (MD) implemented in the AMBER package was carried out on real biological systems using the classical force field FF14SB with the 4-site water model TIP4P-Ew, as well as the new promising force field FF15IPQ with the 3-site water model SPC/Eb. The Intel Xeon E5-2697 v3 processors, as well as GPU accelerators Tesla K40 (Kepler architecture) and P100 (Pascal) were used. Reduction of the number of atoms in a cell by 25–31 % as a result of implementing a 3-site solvent model speeds up the MD calculations by up to 63 % and decreases scalability by about 11 %. The obtained results can be qualitatively different, what indicates the need for joint use of different force fields at studying biological systems. The use of GPU-accelerators as an alternative to classical CPUs provides an opportunity to significantly increase the length on MD trajectories in the daily laboratory practice.

Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees

Toxicity prediction of chemical compounds is a grand challenge. Lately, it achieved significant progress in accuracy but using a huge set of features, implementing a complex blackbox technique such as a deep neural network, and exploiting enormous computational resources. In this paper, we strongly argue for the models and methods that are simple in machine learning characteristics, efficient in computing resource usage, and powerful to achieve very high accuracy levels. To demonstrate this, we develop a single task-based chemical toxicity prediction framework using only 2D features that are less compute intensive. We effectively use a decision tree to obtain an optimum number of features from a collection of thousands of them. We use a shallow neural network and jointly optimize it with decision tree taking both network parameters and input features into account. Our model needs only a minute on a single CPU for its training while existing methods using deep neural networks need about 10 min on NVidia Tesla K40 GPU. However, we obtain similar or better performance on several toxicity benchmark tasks. We also develop a cumulative feature ranking method which enables us to identify features that can help chemists perform prescreening of toxic compounds effectively.