Browsing by Subject "high performance computing"

Now showing 1 - 5 of 5
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    Building a scientific workflow framework to enable real‐time machine learning and visualization
    (Wiley, 2019-08) Li, Feng; Song, Fengguang; Computer and Information Science, School of Science
    Nowadays, we have entered the era of big data. In the area of high performance computing, large‐scale simulations can generate huge amounts of data with potentially critical information. However, these data are usually saved in intermediate files and are not instantly visible until advanced data analytics techniques are applied after reading all simulation data from persistent storages (eg, local disks or a parallel file system). This approach puts users in a situation where they spend long time on waiting for running simulations while not knowing the status of the running job. In this paper, we build a new computational framework to couple scientific simulations with multi‐step machine learning processes and in‐situ data visualizations. We also design a new scalable simulation‐time clustering algorithm to automatically detect fluid flow anomalies. This computational framework is built upon different software components and provides plug‐in data analysis and visualization functions over complex scientific workflows. With this advanced framework, users can monitor and get real‐time notifications of special patterns or anomalies from ongoing extreme‐scale turbulent flow simulations.
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    Designing a Synchronization-reducing Clustering Method on Manycores: Some Issues and Improvements
    (ACM, 2017-11) Zheng, Weijian; Song, Fengguang; Lin, Lan; Computer and Information Science, School of Science
    The k-means clustering method is one of the most widely used techniques in big data analytics. In this paper, we explore the ideas of software blocking, asynchronous local optimizations, and heuristics of simulated annealing to improve the performance of k-means clustering. Like most of the machine learning methods, the performance of k-means clustering relies on two main factors: the computing speed (per iteration), and the convergence rate. A straightforward realization of the software-blocking synchronization-reducing clustering algorithm, however, sees sporadic slower convergence rate than the standard k-means algorithm. To tackle the issues, we design an annealing-enhanced algorithm, which introduces the heuristics of stop conditions and annealing steps to provide as good or better performance than the standard k-means algorithm. This new enhanced k-means clustering algorithm is able to offer the same clustering quality as the standard k-means. Experiments with real-world datasets show that the new parallel implementation is faster than the open source HPC library of Parallel K-Means Data Clustering (e.g., 19% faster on relatively large datasets with 32 CPU cores, and 11% faster on a large dataset with 1,024 CPU cores). Moreover, the extent to which the program performance improves is largely determined by the actual convergence rate of applying the algorithm to different datasets.
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    Modeling and Implementation of an Asynchronous Approach to Integrating HPC and Big Data Analysis
    (Elsevier, 2016-06) Fu, Yuankun; Song, Fengguang; Zhu, Luoding; Department of Computer & Information Science, School of Science
    With the emergence of exascale computing and big data analytics, many important scientific applications require the integration of computationally intensive modeling and simulation with data-intensive analysis to accelerate scientific discovery. In this paper, we create an analytical model to steer the optimization of the end-to-end time-to-solution for the integrated computation and data analysis. We also design and develop an intelligent data broker to efficiently intertwine the computation stage and the analysis stage to practically achieve the optimal time-to-solution predicted by the analytical model. We perform experiments on both synthetic applications and real-world computational fluid dynamics (CFD) applications. The experiments show that the analytic model exhibits an average relative error of less than 10%, and the application performance can be improved by up to 131% for the synthetic programs and by up to 78% for the real-world CFD application.
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    Performance analysis and optimization of in-situ integration of simulation with data analysis: zipping applications up
    (ACM, 2018-06) Fu, Yuankun; Li, Feng; Song, Fengguang; Chen, Zizhong; Computer and Information Science, School of Science
    This paper targets an important class of applications that requires combining HPC simulations with data analysis for online or real-time scientific discovery. We use the state-of-the-art parallel-IO and data-staging libraries to build simulation-time data analysis workflows, and conduct performance analysis with real-world applications of computational fluid dynamics (CFD) simulations and molecular dynamics (MD) simulations. Driven by in-depth performance inefficiency analysis, we design an end-to-end application-level approach to eliminating the interlocks and synchronizations existent in the present methods. Our new approach employs both task parallelism and pipeline parallelism to reduce synchronizations effectively. In addition, we design a fully asynchronous, fine-grain, and pipelining runtime system, which is named Zipper. Zipper is a multi-threaded distributed runtime system and executes in a layer below the simulation and analysis applications. To further reduce the simulation application's stall time and enhance the data transfer performance, we design a concurrent data transfer optimization that uses both HPC network and parallel file system for improved bandwidth. The scalability of the Zipper system has been verified by a performance model and various empirical large scale experiments. The experimental results on an Intel multicore cluster as well as a Knight Landing HPC system demonstrate that the Zipper based approach can outperform the fastest state-of-the-art I/O transport library by up to 220% using 13,056 processor cores.
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    suCAQR: A Simplified Communication-Avoiding QR Factorization Solver Using the TBLAS Framework
    (IEEE, 2016-12) Zheng, Weijian; Song, Fengguang; Lin, Lan; Chen, Zizhong; Computer and Information Science, School of Science
    The scope of this paper is to design and implement a scalable QR factorization solver that can deliver the fastest performance for tall and skinny matrices and square matrices on modern supercomputers. The new solver, named scalable universal communication-avoiding QR factorization (suCAQR), introduces a simplified and tuning-less way to realize the communication-avoiding QR factorization algorithm to support matrices of any shapes. The software design includes a mixed usage of physical and logical data layouts, a simplified method of dynamic-root binary-tree reduction, and a dynamic dataflow implementation. Compared with the existing communication avoiding QR factorization implementations, suCAQR has the benefits of being simpler, more general, and more efficient. By balancing the degree of parallelism and the proportion of faster computational kernels, it is able to achieve scalable performance on clusters of multicore nodes. The software essentially combines the strengths of both synchronization-reducing approach and communication-avoiding approach to achieve high performance. Based on the experimental results using 1,024 CPU cores, suCAQR is faster than DPLASMA by up to 30%, and faster than ScaLAPACK by up to 30 times.