scholarly journals Fast Playback Framework for Analysis of Ground-Based Doppler Radar Observations Using MapReduce Technology

2016 ◽  
Vol 33 (4) ◽  
pp. 621-634 ◽  
Author(s):  
Jingyin Tang ◽  
Corene J. Matyas

AbstractThe creation of a 3D mosaic is often the first step when using the high-spatial- and temporal-resolution data produced by ground-based radars. Efficient yet accurate methods are needed to mosaic data from dozens of radar to better understand the precipitation processes in synoptic-scale systems such as tropical cyclones. Research-grade radar mosaic methods of analyzing historical weather events should utilize data from both sides of a moving temporal window and process them in a flexible data architecture that is not available in most stand-alone software tools or real-time systems. Thus, these historical analyses require a different strategy for optimizing flexibility and scalability by removing time constraints from the design. This paper presents a MapReduce-based playback framework using Apache Spark’s computational engine to interpolate large volumes of radar reflectivity and velocity data onto 3D grids. Designed as being friendly to use on a high-performance computing cluster, these methods may also be executed on a low-end configured machine. A protocol is designed to enable interoperability with GIS and spatial analysis functions in this framework. Open-source software is utilized to enhance radar usability in the nonspecialist community. Case studies during a tropical cyclone landfall shows this framework’s capability of efficiently creating a large-scale high-resolution 3D radar mosaic with the integration of GIS functions for spatial analysis.

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bingzheng Li ◽  
Jinchen Xu ◽  
Zijing Liu

With the development of high-performance computing and big data applications, the scale of data transmitted, stored, and processed by high-performance computing cluster systems is increasing explosively. Efficient compression of large-scale data and reducing the space required for data storage and transmission is one of the keys to improving the performance of high-performance computing cluster systems. In this paper, we present SW-LZMA, a parallel design and optimization of LZMA based on the Sunway 26010 heterogeneous many-core processor. Combined with the characteristics of SW26010 processors, we analyse the storage space requirements, memory access characteristics, and hotspot functions of the LZMA algorithm and implement the thread-level parallelism of the LZMA algorithm based on Athread interface. Furthermore, we make a fine-grained layout of LDM address space to achieve DMA double buffer cyclic sliding window algorithm, which optimizes the performance of SW-LZMA. The experimental results show that compared with the serial baseline implementation of LZMA, the parallel LZMA algorithm obtains a maximum speedup ratio of 4.1 times using the Silesia corpus benchmark, while on the large-scale data set, speedup is 5.3 times.


2021 ◽  
Author(s):  
Mohsen Hadianpour ◽  
Ehsan Rezayat ◽  
Mohammad-Reza Dehaqani

Abstract Due to the significantly drastic progress and improvement in neurophysiological recording technologies, neuroscientists have faced various complexities dealing with unstructured large-scale neural data. In the neuroscience community, these complexities could create serious bottlenecks in storing, sharing, and processing neural datasets. In this article, we developed a distributed high-performance computing (HPC) framework called `Big neuronal data framework' (BNDF), to overcome these complexities. BNDF is based on open-source big data frameworks, Hadoop and Spark providing a flexible and scalable structure. We examined BNDF on three different large-scale electrophysiological recording datasets from nonhuman primate’s brains. Our results exhibited faster runtimes with scalability due to the distributed nature of BNDF. We compared BNDF results to a widely used platform like MATLAB in an equitable computational resource. Compared with other similar methods, using BNDF provides more than five times faster performance in spike sorting as a usual neuroscience application.


F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2060
Author(s):  
Aleksandr Agafonov ◽  
Kimmo Mattila ◽  
Cuong Duong Tuan ◽  
Lars Tiede ◽  
Inge Alexander Raknes ◽  
...  

META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the service to all ELIXIR users. We describe our approach for setting up and executing the functional analysis of META-pipe on additional academic and commercial clouds. Our goal is to provide a powerful analysis service that is easy to use and to maintain. Our design therefore uses a distributed architecture where we combine central servers with multiple distributed backends that execute the computationally intensive jobs. We believe our experiences developing and operating META-pipe provides a useful model for others that plan to provide a portal based data analysis service in ELIXIR and other organizations with geographically distributed compute and storage resources.


2017 ◽  
Vol 33 (2) ◽  
pp. 119-130
Author(s):  
Vinh Van Le ◽  
Hoai Van Tran ◽  
Hieu Ngoc Duong ◽  
Giang Xuan Bui ◽  
Lang Van Tran

Metagenomics is a powerful approach to study environment samples which do not require the isolation and cultivation of individual organisms. One of the essential tasks in a metagenomic project is to identify the origin of reads, referred to as taxonomic assignment. Due to the fact that each metagenomic project has to analyze large-scale datasets, the metatenomic assignment is very much computation intensive. This study proposes a parallel algorithm for the taxonomic assignment problem, called SeMetaPL, which aims to deal with the computational challenge. The proposed algorithm is evaluated with both simulated and real datasets on a high performance computing system. Experimental results demonstrate that the algorithm is able to achieve good performance and utilize resources of the system efficiently. The software implementing the algorithm and all test datasets can be downloaded at http://it.hcmute.edu.vn/bioinfo/metapro/SeMetaPL.html.


Author(s):  
Adrian Jackson ◽  
Michèle Weiland

This chapter describes experiences using Cloud infrastructures for scientific computing, both for serial and parallel computing. Amazon’s High Performance Computing (HPC) Cloud computing resources were compared to traditional HPC resources to quantify performance as well as assessing the complexity and cost of using the Cloud. Furthermore, a shared Cloud infrastructure is compared to standard desktop resources for scientific simulations. Whilst this is only a small scale evaluation these Cloud offerings, it does allow some conclusions to be drawn, particularly that the Cloud can currently not match the parallel performance of dedicated HPC machines for large scale parallel programs but can match the serial performance of standard computing resources for serial and small scale parallel programs. Also, the shared Cloud infrastructure cannot match dedicated computing resources for low level benchmarks, although for an actual scientific code, performance is comparable.


Green computing is a contemporary research topic to address climate and energy challenges. In this chapter, the authors envision the duality of green computing with technological trends in other fields of computing such as High Performance Computing (HPC) and cloud computing on one hand and economy and business on the other hand. For instance, in order to provide electricity for large-scale cloud infrastructures and to reach exascale computing, we need huge amounts of energy. Thus, green computing is a challenge for the future of cloud computing and HPC. Alternatively, clouds and HPC provide solutions for green computing and climate change. In this chapter, the authors discuss this proposition by looking at the technology in detail.


Big Data ◽  
2016 ◽  
pp. 1555-1581
Author(s):  
Gueyoung Jung ◽  
Tridib Mukherjee

In the modern information era, the amount of data has exploded. Current trends further indicate exponential growth of data in the future. This prevalent humungous amount of data—referred to as big data—has given rise to the problem of finding the “needle in the haystack” (i.e., extracting meaningful information from big data). Many researchers and practitioners are focusing on big data analytics to address the problem. One of the major issues in this regard is the computation requirement of big data analytics. In recent years, the proliferation of many loosely coupled distributed computing infrastructures (e.g., modern public, private, and hybrid clouds, high performance computing clusters, and grids) have enabled high computing capability to be offered for large-scale computation. This has allowed the execution of the big data analytics to gather pace in recent years across organizations and enterprises. However, even with the high computing capability, it is a big challenge to efficiently extract valuable information from vast astronomical data. Hence, we require unforeseen scalability of performance to deal with the execution of big data analytics. A big question in this regard is how to maximally leverage the high computing capabilities from the aforementioned loosely coupled distributed infrastructure to ensure fast and accurate execution of big data analytics. In this regard, this chapter focuses on synchronous parallelization of big data analytics over a distributed system environment to optimize performance.


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