Optimization of a Parallel CFD Code and Its Performance Evaluation on Tianhe-1A

This paper describes performance tuning experiences with a parallel CFD code to enhance its performance and flexibility on large scale parallel computers. The code solves the incompressible Navier-Stokes equations based on the novel Slightly Compressible Model on three-dimensional structure grids. High level loop transformations and argument based code specialization are utilized to optimize its uniprocessor performance. Static arrays are converted into dynamically allocated arrays to improve the flexibility. The grid generator is coupled with the flow solver so that they can exchange grid data in the memory. A detailed performance evaluation is performed. The results show that our uniprocessor optimizations improve the performance of the flow solver for 1.38 times to 3.93 times on Tianhe-1A supercomputer. In memory grid data exchange optimization speeds up the application startup time by nearly two magnitudes. The optimized code exhibits an excellent parallel scalability running realistic test cases. On 4 096 CPU cores, it achieves a strong scaling parallel efficiency of 77.39 % and a maximum performance of 4.01 Tflops

I2DS: Interpretable Intrusion Detection System Using Autoencoder and Additive Tree

Intrusion detection system (IDS), the second security gate behind the firewall, can monitor the network without affecting the network performance and ensure the system security from the internal maximum. Many researches have applied traditional machine learning models, deep learning models, or hybrid models to IDS to improve detection effect. However, according to Predicted accuracy, Descriptive accuracy, and Relevancy (PDR) framework, most of detection models based on model-based interpretability lack good detection performance. To solve the problem, in this paper, we have proposed a novel intrusion detection system model based on model-based interpretability, called Interpretable Intrusion Detection System (I2DS). We firstly combine normal and attack samples reconstructed by AutoEncoder (AE) with training samples to highlight the normal and attack features, so that the classifier has a gorgeous effect. Then, Additive Tree (AddTree) is used as a binary classifier, which can provide excellent predictive performance in the combined dataset while maintaining good model-based interpretability. In the experiment, UNSW-NB15 dataset is used to evaluate our proposed model. For detection performance, I2DS achieves a detection accuracy of 99.95%, which is better than most of state-of-the-art intrusion detection methods. Moreover, I2DS maintains higher simulatability and captures the decision rules easily

Large-Scale Whale-Call Classification by Transfer Learning on Multi-Scale Waveforms and Time-Frequency Features

Whale vocal calls contain valuable information and abundant characteristics that are important for classification of whale sub-populations and related biological research. In this study, an effective data-driven approach based on pre-trained Convolutional Neural Networks (CNN) using multi-scale waveforms and time-frequency feature representations is developed in order to perform the classification of whale calls from a large open-source dataset recorded by sensors carried by whales. Specifically, the classification is carried out through a transfer learning approach by using pre-trained state-of-the-art CNN models in the field of computer vision. 1D raw waveforms and 2D log-mel features of the whale-call data are respectively used as the input of CNN models. For raw waveform input, windows are applied to capture multiple sketches of a whale-call clip at different time scales and stack the features from different sketches for classification. When using the log-mel features, the delta and delta-delta features are also calculated to produce a 3-channel feature representation for analysis. In the training, a 4-fold cross-validation technique is employed to reduce the overfitting effect, while the Mix-up technique is also applied to implement data augmentation in order to further improve the system performance. The results show that the proposed method can improve the accuracies by more than 20% in percentage for the classification into 16 whale pods compared with the baseline method using groups of 2D shape descriptors of spectrograms and the Fisher discriminant scores on the same dataset. Moreover, it is shown that classifications based on log-mel features have higher accuracies than those based directly on raw waveforms. The phylogeny graph is also produced to significantly illustrate the relationships among the whale sub-populations

Additional file 2: Figure S2. of An optimized protocol for generation and analysis of Ion Proton sequencing reads for RNA-Seq

Mean read quality distribution. The shade of color (from grey to blue to black) represents the density (percentage) of read with certain mean quality. The reads of highest mean quality were around 120 ~ 160 bp long in ProC libraries, whist for ProR libraries the reads mean quality remained high until 150 bp, where the quality began to deteriorate. (PDF 6020 kb

Additional file 5: Figure S5. of An optimized protocol for generation and analysis of Ion Proton sequencing reads for RNA-Seq

Comparison of gene expression estimate method by HTSeq and CuffDiff in terms of (A) detected gene number, and (B) consistency with TaqMan result. (PDF 2185 kb

Additional file 1: Figure S1: of An optimized protocol for generation and analysis of Ion Proton sequencing reads for RNA-Seq

The number of reads by read length in (A) the eleven Proton libraries. The peaks of read length of ProC libraries were around 150 ~ 200 bp, whilst around 90 bp for ProR libraries. (B) Insert sizes of the two HiSeq libraries, calculated by the distance between pair-end reads. (PDF 2520 kb

Additional file 6: of An optimized protocol for generation and analysis of Ion Proton sequencing reads for RNA-Seq

Supplementary Method. The files and commands used in the study. (DOCX 19 kb