Third-generation sequencing techniques have achieved major breakthroughs in sequencing long reads and speed. Continuous improvements in sequencing techniques have reduced sequencing costs, and the number of sequencing data files has shown explosive growth. In terms of sequence alignment, to deal with these high numbers and large-scale data, the conventional serial alignment method can no longer effectively meet the research requirements, therefore, it is of great importance to develop a faster, low-load, and compatible parallel alignment program. In this paper, we propose a parallel task pool algorithm based on the minimap2, a sequence alignment tool, and develop the task pool parallel alignment program based on this algorithm. We compare the program’s work with the average segmentation parallel alignment program. The results show that the task pool parallel alignment program has significant improvement in speedup, memory load, segmentation flexibility, and computational efficiency, it also has good scalability and computational stability. MinimapPool is available at https://github.com/krkrcc/MinimapPool.

Abstract Sparse general matrix–matrix multiplication (SpGEMM) is a crucial and complex computational task in many practical applications. Improving the performance of SpGEMM on SIMT processors like modern GPUs is challenging due to the unpredictable sparsity of sparse matrices. Although existing GPU solutions have made progress in improving performance through advanced algorithm design, they ignore some optimizations related to specific processor architectures. This can result in a partially inefficient implementation of their algorithms. This paper focuses on optimizing four inefficient parts of the NSparse algorithm on DCU (a GPU-like accelerator). The optimizations include: 1) setting parameters to improve the load balance of the second matrix by extracting maximum row information at runtime; 2) reducing overhead of binning operations by making full use of registers and shared memory effectively; 3) improving numerical SpGEMM performance by adjusting its calculation mode; and 4) enhancing global load balance through finer-grained grouping and kernel configurations. Experiment results demonstrate that when compared to five state-of-the-art SpGEMM algorithms (bhSparse, KokkosKernels, NSparse, rocSparse, and spECK), our optimized method achieves an average of 7.99x (up to 18.2x), 8.01x (up to 20.83x), 2.37x (up to 6.16x), 1.82x (up to 4.20x), and 1.63x (up to 5.01x) speedups on 29 sparse matrices with different sparse structures, respectively.

Abstract Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework’s performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers.

In the era of big data, data is playing an increasingly important role in scientific study, and reliable storage and secure sharing of data have become a research hotspot. At present, centralized solutions based on data centers and cloud storage have problems with data-right confirmation and center trust. A large number of decentralized storage solutions are public systems, in which blockchain technology, as a tool for value exchange, does not solve the problems of data verification and system supervision. We propose a peer-to-peer storage system with identity access, which achieves data validation, cross-organizational data retrieval, trusted authorization, and sharing based on the consortium blockchain. Our solution proposes a peer-to-peer data storage scheme based on the consortium blockchain and a set of identity authentication mechanisms compatible with the consortium blockchain. Based on this, we propose a blockchain-based permission control scheme and a set of retrieval, authorization, and sharing processes. Finally, we implemented and tested the system to prove the feasibility of the scheme.

Recently, deep learning techniques based on electronic health record (EHR) data have achieved success in medical prediction. However, due to the complexity, heterogeneity nature of EHR data, most previous studies build models based on single-modal data (e.g. the structured data or the unstructured free-text data). Although some studies have trained the models based on multimodal EHR data and achieved more advanced performance, they still suffer from the clinical practicability problems, as they require separate modeling for each medical prediction task. Moreover, they ignore the potential correlation between clinical prediction tasks. In this work, we propose UniMed, a Unified model handles multiple Medical prediction tasks simultaneously by learning from multimodal EHR data. Our UniMed model encodes each input modality separately and uses a transformer decoder followed by task-specific prediction heads to predict each medical task. Experimental results conducted on publicly available EHR dataset demonstrate that there is a time-progressive correlation between medical prediction tasks and show the effectiveness of our method.

Symptom diagnosis in dialogue remains a challenging task because the symptom entities and their status need to be extracted correctly at the same time. Most previous studies treat symptom diagnosis as a classification or sequence labeling task and focus on using single-sentence dialogue as input. Unique from past studies, in this paper, we propose a new framework for dialogue symptom diagnosis, which formulate it as a machine reading comprehension (MRC) task. We first use window-level multi-turn of dialogue as input and extract the symptom entities. Then, we generate a question for each entity to infer the symptom status in the form of question answering (QA). Benefit from the MRC formalization, our proposed framework can encode more informative prior knowledge, which can effectively improve the performance of symptom status inference. Experiments on the Chinese medical dialogue dataset show that the proposed framework outperforms the previous best model and several competitive baselines, which indicates that our framework provides a useful direction for dialogue symptom diagnosis. The code and data are publicly available at https://github.com/zhaoxiongjun/DSD.

The application of deep neural networks (DNNs) for road extraction from remote sensing images has gained broad interest because of the competence concerning complex nonlinear relations; however, the presence of noisy labels in the training data sets adversely affects the performance of DNNs. The existing methods of improving the robustness of DNNs focus on modeling the noise distribution. However, these approaches are not satisfactory because of the inaccurate high-level image features obtained by the DNNs. To address this issue, we develop a noise probabilistic model for learning the label noise based on the relationship between the input images, noisy labels, and true labels. The key idea of the probabilistic model is to directly explore the information from the input images and apply it to model the label noise. Then, a robust deep neural network (RDNN) is proposed to instantiate the noise probabilistic model, which consists of two important modules: the true label predictor (TLP) and the noise label estimator (NLE). Especially, the TLP is made of a DNN with softmax, which is used to learn the true label distribution. The NLE is applied to model the label noise distribution, which aims to absorb the label noise in the training process. Moreover, to tackle the challenges in the optimization, we deduce a loss function with the novel regularization, which allows the RDNN to conduct effective training on the noise data set. The effectiveness of the proposed method is validated by experiments on three road data sets that contain various resolutions and imaging conditions. The results demonstrate its superiority over state-of-the-art methods in visual performance and classification accuracy.

Summary Cloud computing emerges as one of the most important technologies for interconnecting people and building the so‐called Internet of People (IoP). In such a cloud‐based IoP, the virtualization technique provides the key supporting environments for running the IoP jobs such as performing data analysis and mining personal information. Nowadays, energy consumption in such a system is a critical metric to measure the sustainability and eco‐friendliness of the system. This paper develops three power‐aware scheduling strategies in virtualized systems managed by Xen, which is a popular virtualization technique. These three strategies are the Least performance Loss Scheduling strategy, the No performance Loss Scheduling strategy, and the Best Frequency Match scheduling strategy. These power‐aware strategies are developed by identifying the limitation of Xen in scaling the CPU frequency and aim to reduce the energy waste without sacrificing the jobs running performance in the computing systems virtualized by Xen. Least performance Loss Scheduling works by re‐arranging the execution order of the virtual machines (VMs). No performance Loss Scheduling works by setting a proper initial CPU frequency for running the VMs. Best Frequency Match reduces energy waste and performance loss by allowing the VMs to jump the queue so that the VM that is put into execution best matches the current CPU frequency. Scheduling for both single core and multicore processors is considered in this paper. The evaluation experiments have been conducted, and the results show that compared with the original scheduling strategy in Xen, the developed power‐aware scheduling algorithm is able to reduce energy consumption without reducing the performance for the jobs running in Xen. Copyright © 2016 John Wiley & Sons, Ltd.