Brain science accelerates the study of intelligence and behavior,contributes fundamental insights into human cognition,and offers prospective treatments for brain disease.Faced with the challenges posed by imaging tec...Brain science accelerates the study of intelligence and behavior,contributes fundamental insights into human cognition,and offers prospective treatments for brain disease.Faced with the challenges posed by imaging technologies and deep learning computational models,big data and high-performance computing(HPC)play essential roles in studying brain function,brain diseases,and large-scale brain models or connectomes.We review the driving forces behind big data and HPC methods applied to brain science,including deep learning,powerful data analysis capabilities,and computational performance solutions,each of which can be used to improve diagnostic accuracy and research output.This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible,by improving data standardization and sharing,and by providing new neuromorphic insights.展开更多
Wide-area high-performance computing is widely used for large-scale parallel computing applications owing to its high computing and storage resources.However,the geographical distribution of computing and storage reso...Wide-area high-performance computing is widely used for large-scale parallel computing applications owing to its high computing and storage resources.However,the geographical distribution of computing and storage resources makes efficient task distribution and data placement more challenging.To achieve a higher system performance,this study proposes a two-level global collaborative scheduling strategy for wide-area high-performance computing environments.The collaborative scheduling strategy integrates lightweight solution selection,redundant data placement and task stealing mechanisms,optimizing task distribution and data placement to achieve efficient computing in wide-area environments.The experimental results indicate that compared with the state-of-the-art collaborative scheduling algorithm HPS+,the proposed scheduling strategy reduces the makespan by 23.24%,improves computing and storage resource utilization by 8.28%and 21.73%respectively,and achieves similar global data migration costs.展开更多
Within the last few decades, increases in computational resources have contributed enormously to the progress of science and engineering (S & E). To continue making rapid advancements, the S & E community must...Within the last few decades, increases in computational resources have contributed enormously to the progress of science and engineering (S & E). To continue making rapid advancements, the S & E community must be able to access computing resources. One way to provide such resources is through High-Performance Computing (HPC) centers. Many academic research institutions offer their own HPC Centers but struggle to make the computing resources easily accessible and user-friendly. Here we present SHABU, a RESTful Web API framework that enables S & E communities to access resources from Boston University’s Shared Computing Center (SCC). The SHABU requirements are derived from the use cases described in this work.展开更多
The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of par...The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.展开更多
Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes...Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.展开更多
Due to current technology enhancement,molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge data.There-fore,Multi-processing CPUs technology can be used...Due to current technology enhancement,molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge data.There-fore,Multi-processing CPUs technology can be used including physical and logical processors(Hyper Threading)to significantly increase the performance of computations.Accordingly,sequence comparison and pairwise alignment were both found contributing significantly in calculating the resemblance between sequences for constructing optimal alignments.This research used the Hash Table-NGram-Hirschberg(HT-NGH)algo-rithm to represent this pairwise alignment utilizing hashing capabilities.The authors propose using parallel shared memory architecture via Hyper Threading to improve the performance of molecular dataset protein pairwise alignment.The proposed parallel hyper threading method targeted the transformation of the HT-NGH on the datasets decomposition for sequence level efficient utilization within the processing units,that is,reducing idle processing unit situations.The authors combined hyper threading within the multicore architecture processing on shared memory utilization remarking perfor-mance of 24.8%average speed up to 34.4%as the highest boosting rate.The benefit of this work improvement is shown preserving acceptable accuracy,that is,reaching 2.08,2.88,and 3.87 boost-up as well as the efficiency of 1.04,0.96,and 0.97,using 2,3,and 4 cores,respectively,as attractive remarkable results.展开更多
As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent task...As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent tasks.By exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and devices.In this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC environment.The proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge environment.In addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)metaheuristic.Extensive experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.展开更多
The study of global climate change seeks to understand:(1)the components of the Earth’s varying environmental system,with a particular focus on climate;(2)how these components interact to determine present conditions...The study of global climate change seeks to understand:(1)the components of the Earth’s varying environmental system,with a particular focus on climate;(2)how these components interact to determine present conditions;(3)the factors driving these components;(4)the history of global change and the projection of future change;and(5)how knowledge about global environmental variability and change can be applied to present-day and future decision-making.This paper addresses the use of high-performance computing and high-throughput computing for a global change study on the Digital Earth(DE)platform.Two aspects of the use of high-performance computing(HPC)/high-throughput computing(HTC)on the DE platform are the processing of data from all sources,especially Earth observation data,and the simulation of global change models.The HPC/HTC is an essential and efficient tool for the processing of vast amounts of global data,especially Earth observation data.The current trend involves running complex global climate models using potentially millions of personal computers to achieve better climate change predictions than would ever be possible using the supercomputers currently available to scientists.展开更多
With the scaling up of high-performance computing systems in recent years,their reliability has been descending continuously.Therefore,system resilience has been regarded as one of the critical challenges for large-sc...With the scaling up of high-performance computing systems in recent years,their reliability has been descending continuously.Therefore,system resilience has been regarded as one of the critical challenges for large-scale HPC systems.Various techniques and systems have been proposed to ensure the correct execution and completion of parallel programs.This paper provides a comprehensive survey of existing software resilience approaches.Firstly,a classification of software resilience approaches is presented;then we introduce major approaches and techniques,including checkpointing,replication,soft error resilience,algorithmbased fault tolerance,fault detection and prediction.In addition,challenges exposed by system-scale and heterogeneous architecture are also discussed.展开更多
The geospatial sciences face grand information technology(IT)challenges in the twenty-first century:data intensity,computing intensity,concurrent access intensity and spatiotemporal intensity.These challenges require ...The geospatial sciences face grand information technology(IT)challenges in the twenty-first century:data intensity,computing intensity,concurrent access intensity and spatiotemporal intensity.These challenges require the readiness of a computing infrastructure that can:(1)better support discovery,access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries;(2)provide real-time IT resources to enable real-time applications,such as emergency response;(3)deal with access spikes;and(4)provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge.The emergence of cloud computing provides a potential solution with an elastic,on-demand computing platform to integrateobservation systems,parameter extracting algorithms,phenomena simulations,analytical visualization and decision support,and to provide social impact and user feedbackthe essential elements of the geospatial sciences.We discuss the utilization of cloud computing to support the intensities of geospatial sciences by reporting from our investigations on how cloud computing could enable the geospatial sciences and how spatiotemporal principles,the kernel of the geospatial sciences,could be utilized to ensure the benefits of cloud computing.Four research examples are presented to analyze how to:(1)search,access and utilize geospatial data;(2)configure computing infrastructure to enable the computability of intensive simulation models;(3)disseminate and utilize research results for massive numbers of concurrent users;and(4)adopt spatiotemporal principles to support spatiotemporal intensive applications.The paper concludes with a discussion of opportunities and challenges for spatial cloud computing(SCC).展开更多
This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar(Video-SAR)as a new mode of radar imaging for real-time remote sensing and surveillance.This research also conside...This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar(Video-SAR)as a new mode of radar imaging for real-time remote sensing and surveillance.This research also considers new suggestions in the systematic design,research taxonomy,and future trends of radar data processing.Despite the conventional modes of SAR imaging,Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night(regardless of light sources)in all weathers.First,an introduction to Video-SAR is presented.Then,some specific properties of this imaging mode are reviewed.Particularly,this research covers one of the most important aspects of the Video-SAR systems,namely,the systematic design requirements,and also some new types of visual distortions which are different from the distortions,artifacts and noises observed in the conventional imaging radar.In addition,some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle(UAV)platforms,Internet of Multimedia Things(IoMT),Video-SAR data processing issues,and real-world applications are investigated.展开更多
Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences.However,only a limited number of geoscientists have been ada...Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences.However,only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers:1)selecting an appropriate cloud platform for a specific application could be challenging,as various cloud services are available and 2)existing general cloud platforms are not designed to support geoscience applications,algorithms and models.To tackle such barriers,this research aims to design a hybrid cloud computing(HCC)platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform.This platform can manage different types of underlying cloud infrastructure(e.g.,private or public clouds),and enables geoscientists to test and leverage the cloud capabilities through a web interface.Additionally,the platform also provides different geospatial cloud services,such as workflow as a service,on the top of common cloud services(e.g.,infrastructure as a service)provided by general cloud platforms.Therefore,geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly.A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.展开更多
A high-performance predictor for critical unstable generators(CUGs) of power systems is presented in this paper. The predictor is driven by the Map Reduce based parallelized neural networks. Specifically, a group of b...A high-performance predictor for critical unstable generators(CUGs) of power systems is presented in this paper. The predictor is driven by the Map Reduce based parallelized neural networks. Specifically, a group of back propagation neural networks(BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing,enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert.Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.展开更多
Underwater acoustic models are effective tools for simulating underwater sound propagation.More than 50 years of research have been conducted on the theory and computational models of sound propagation in the ocean.Un...Underwater acoustic models are effective tools for simulating underwater sound propagation.More than 50 years of research have been conducted on the theory and computational models of sound propagation in the ocean.Unfortunately,underwater sound propagation models were unable to solve practical large-scale three-dimensional problems for many years due to limited computing power and hardware conditions.Since the mid-1980s,research on high performance computing for acoustic propagation models in the field of underwater acoustics has flourished with the emergence of high-performance computing platforms,enabling underwater acoustic propagation models to solve many practical application problems that could not be solved before.In this paper,the contributions of research on high-performance computing for underwater acoustic propagation models since the 1980s are thoroughly reviewed and the possible development directions for the future are outlined.展开更多
This article introduces“EarthLab”,a major new Earth system numerical simulation facility developed in China.EarthLab is a numerical simulation system for a physical climate system,an environmental system,an ecologic...This article introduces“EarthLab”,a major new Earth system numerical simulation facility developed in China.EarthLab is a numerical simulation system for a physical climate system,an environmental system,an ecological system,a solid earth system,and a space weather system as a whole with a high-performance scientific computing platform.EarthLab consists of five key elements-namely:a global earth numerical simulation system,a regional high-precision simulation system,a supercomputing support and management system,a database,data assimilation and visualization system,and a high-performance computing system for earth sciences.EarthLab helps to study the atmosphere,hydrosphere,cryosphere,lithosphere,and biosphere,as well as their interactions,to improve the accuracy of predictions by integrating simulations and observations,and to provide a scientific foundation for major issues such as national disaster prevention and mitigation.The construction and operation of EarthLab will involve close cooperation with joint contributions and shared benefits.展开更多
We lay out the ramifications of the 2020 pandemic for all people in geosciences,especially the young,and argue for significant changes on training and career development.We focus primarily on its devastating impact in...We lay out the ramifications of the 2020 pandemic for all people in geosciences,especially the young,and argue for significant changes on training and career development.We focus primarily on its devastating impact in USA and compare with that in other countries especially China.We review the potential effect for the next four years or so on the aspirations of an academic career versus more realistic career goals.We urge people in mid-career about the need to reassess previous goals.We stress the need for students or researchers to acquire technical skills in high-performance computing(HPC),data analytics,artificial intelligence,and/or visualization along with a broad set of technical skills in applied computer science and mathematics.We give advice about hot prospects in several areas that have great potential for advancement in the coming decade,such as visualization,deep learning,quantum computing and information,and cloud computing,all of which lie within the aegis of HPC.Our forecast is that the pandemic will significantly reshape the job landscape and career paths for both young and established researchers and we discuss bluntly the dire situation facing junior people in geosciences in the aftermath of the pandemic around the world until 2024.展开更多
Holographic displays have the promise to be the ultimate 3D display technology,able to account for all visual cues.Recent advances in photonics and electronics gave rise to high-resolution holographic display prototyp...Holographic displays have the promise to be the ultimate 3D display technology,able to account for all visual cues.Recent advances in photonics and electronics gave rise to high-resolution holographic display prototypes,indicating that they may become widely available in the near future.One major challenge in driving those display systems is computational:computer generated holography(CGH)consists of numerically simulating diffraction,which is very computationally intensive.Our goal in this paper is to give a broad overview of the state-of-the-art in CGH.We make a classification of modern CGH algorithms,we describe different algorithmic CGH acceleration techniques,discuss the latest dedicated hardware solutions and indicate how to evaluate the perceptual quality of CGH.We summarize our findings,discuss remaining challenges and make projections on the future of CGH.展开更多
With the rapid increase of the size of applications and the complexity of the supercomputer architecture,topology-aware process mapping becomes increasingly important.High communication cost has become a dominant cons...With the rapid increase of the size of applications and the complexity of the supercomputer architecture,topology-aware process mapping becomes increasingly important.High communication cost has become a dominant constraint of the performance of applications running on the supercomputer.To avoid a bad mapping strategy which can lead to terrible communication performance,we propose an optimized heuristic topology-aware mapping algorithm(OHTMA).The algorithm attempts to minimize the hop-byte metric that we use to measure the mapping results.OHTMA incorporates a new greedy heuristic method and pair-exchange-based optimization.It reduces the number of long-distance communications and effectively enhances the locality of the communication.Experimental results on the Tianhe-3 exascale supercomputer prototype indicate that OHTMA can significantly reduce the communication costs.展开更多
.The geometric multigrid method(GMG)is one of the most efficient solving techniques for discrete algebraic systems arising from elliptic partial differential equations.GMG utilizes a hierarchy of grids or discretizati....The geometric multigrid method(GMG)is one of the most efficient solving techniques for discrete algebraic systems arising from elliptic partial differential equations.GMG utilizes a hierarchy of grids or discretizations and reduces the error at a number of frequencies simultaneously.Graphics processing units(GPUs)have recently burst onto the scientific computing scene as a technology that has yielded substantial performance and energy-efficiency improvements.A central challenge in implementing GMG on GPUs,though,is that computational work on coarse levels cannot fully utilize the capacity of a GPU.In this work,we perform numerical studies of GMG on CPU–GPU heterogeneous computers.Furthermore,we compare our implementation with an efficient CPU implementation of GMG and with the most popular fast Poisson solver,Fast Fourier Transform,in the cuFFT library developed by NVIDIA.展开更多
As a case of space-time interaction,near-repeat calculation indicates that when an event takes place at a certain location,its immediate geographical surroundings would face an increased risk of experiencing subsequen...As a case of space-time interaction,near-repeat calculation indicates that when an event takes place at a certain location,its immediate geographical surroundings would face an increased risk of experiencing subsequent events within a fairly short period of time.This paper presents an exploratory study that extends the investigation of the near-repeat phenomena to a series of space-time interaction,namely event chain calculation.Existing near-repeat tools can only deal with a limited amount of data due to computation constraints,let alone the event chain analysis.By deploying the modern accelerator technology and hybrid computer systems,this study demonstrates that large-scale near-repeat calculation or event chain analysis can be partially resolved through high-performance computing solutions to advance such a challenging statistical problem in both spatial analysis and crime geography.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.31771466)the National Key R&D Program of China(Grant Nos.2018YFB0203903,2016YFC0503607,and 2016YFB0200300)+3 种基金the Transformation Project in Scientific and Technological Achievements of Qinghai,China(Grant No.2016-SF-127)the Special Project of Informatization of Chinese Academy of Sciences,China(Grant No.XXH13504-08)the Strategic Pilot Science and Technology Project of Chinese Academy of Sciences,China(Grant No.XDA12010000)the 100-Talents Program of Chinese Academy of Sciences,China(awarded to BN)
文摘Brain science accelerates the study of intelligence and behavior,contributes fundamental insights into human cognition,and offers prospective treatments for brain disease.Faced with the challenges posed by imaging technologies and deep learning computational models,big data and high-performance computing(HPC)play essential roles in studying brain function,brain diseases,and large-scale brain models or connectomes.We review the driving forces behind big data and HPC methods applied to brain science,including deep learning,powerful data analysis capabilities,and computational performance solutions,each of which can be used to improve diagnostic accuracy and research output.This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible,by improving data standardization and sharing,and by providing new neuromorphic insights.
基金This work was supported by the National key R&D Program of China(2018YFB0203901)the National Natural Science Foundation of China under(Grant No.61772053)the fund of the State Key Laboratory of Software Development Environment(SKLSDE-2020ZX15).
文摘Wide-area high-performance computing is widely used for large-scale parallel computing applications owing to its high computing and storage resources.However,the geographical distribution of computing and storage resources makes efficient task distribution and data placement more challenging.To achieve a higher system performance,this study proposes a two-level global collaborative scheduling strategy for wide-area high-performance computing environments.The collaborative scheduling strategy integrates lightweight solution selection,redundant data placement and task stealing mechanisms,optimizing task distribution and data placement to achieve efficient computing in wide-area environments.The experimental results indicate that compared with the state-of-the-art collaborative scheduling algorithm HPS+,the proposed scheduling strategy reduces the makespan by 23.24%,improves computing and storage resource utilization by 8.28%and 21.73%respectively,and achieves similar global data migration costs.
文摘Within the last few decades, increases in computational resources have contributed enormously to the progress of science and engineering (S & E). To continue making rapid advancements, the S & E community must be able to access computing resources. One way to provide such resources is through High-Performance Computing (HPC) centers. Many academic research institutions offer their own HPC Centers but struggle to make the computing resources easily accessible and user-friendly. Here we present SHABU, a RESTful Web API framework that enables S & E communities to access resources from Boston University’s Shared Computing Center (SCC). The SHABU requirements are derived from the use cases described in this work.
基金the Deanship of Scientific Research at King Abdulaziz University,Jeddah,Saudi Arabia under the Grant No.RG-12-611-43.
文摘The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.
文摘Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.
基金Deanship of Scientific Research(DSR),King Abdulaziz University,Grant/Award Number:D-139-137-1441。
文摘Due to current technology enhancement,molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge data.There-fore,Multi-processing CPUs technology can be used including physical and logical processors(Hyper Threading)to significantly increase the performance of computations.Accordingly,sequence comparison and pairwise alignment were both found contributing significantly in calculating the resemblance between sequences for constructing optimal alignments.This research used the Hash Table-NGram-Hirschberg(HT-NGH)algo-rithm to represent this pairwise alignment utilizing hashing capabilities.The authors propose using parallel shared memory architecture via Hyper Threading to improve the performance of molecular dataset protein pairwise alignment.The proposed parallel hyper threading method targeted the transformation of the HT-NGH on the datasets decomposition for sequence level efficient utilization within the processing units,that is,reducing idle processing unit situations.The authors combined hyper threading within the multicore architecture processing on shared memory utilization remarking perfor-mance of 24.8%average speed up to 34.4%as the highest boosting rate.The benefit of this work improvement is shown preserving acceptable accuracy,that is,reaching 2.08,2.88,and 3.87 boost-up as well as the efficiency of 1.04,0.96,and 0.97,using 2,3,and 4 cores,respectively,as attractive remarkable results.
文摘As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent tasks.By exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and devices.In this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC environment.The proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge environment.In addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)metaheuristic.Extensive experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.
基金This work was supported in part by the MOST,China under Grant Nos.2009CB723906 and 2008AA12Z109by CAS under Grant No.KZCX2-YW-313.
文摘The study of global climate change seeks to understand:(1)the components of the Earth’s varying environmental system,with a particular focus on climate;(2)how these components interact to determine present conditions;(3)the factors driving these components;(4)the history of global change and the projection of future change;and(5)how knowledge about global environmental variability and change can be applied to present-day and future decision-making.This paper addresses the use of high-performance computing and high-throughput computing for a global change study on the Digital Earth(DE)platform.Two aspects of the use of high-performance computing(HPC)/high-throughput computing(HTC)on the DE platform are the processing of data from all sources,especially Earth observation data,and the simulation of global change models.The HPC/HTC is an essential and efficient tool for the processing of vast amounts of global data,especially Earth observation data.The current trend involves running complex global climate models using potentially millions of personal computers to achieve better climate change predictions than would ever be possible using the supercomputers currently available to scientists.
基金supported by the GHFund A(No.ghfund202107010337).
文摘With the scaling up of high-performance computing systems in recent years,their reliability has been descending continuously.Therefore,system resilience has been regarded as one of the critical challenges for large-scale HPC systems.Various techniques and systems have been proposed to ensure the correct execution and completion of parallel programs.This paper provides a comprehensive survey of existing software resilience approaches.Firstly,a classification of software resilience approaches is presented;then we introduce major approaches and techniques,including checkpointing,replication,soft error resilience,algorithmbased fault tolerance,fault detection and prediction.In addition,challenges exposed by system-scale and heterogeneous architecture are also discussed.
基金We thank Drs.Huadong Guo and Changlin Wang for inviting us to write this definition and field review paper.Research reported is partially supported by NASA(NNX07AD99G and SMD-09-1448),FGDC(G09AC00103)Environmental Informatics Framework of the Earth,Energy,and Environment Program at Microsoft Research Connection.We thank insightful comments from reviewers including Dr.Aijun Chen(NASA/GMU),Dr.Thomas Huang(NASA JPL),Dr.Cao Kang(Clark Univ.),Krishna Kumar(Microsoft),Dr.Wenwen Li(UCSB),Dr.Michael Peterson(University of Nebraska-Omaha),Dr.Xuan Shi(Geogia Tech),Dr.Tong Zhang(Wuhan University),Jinesh Varia(Amazon)and an anonymous reviewer.This paper is a result from the collaborations/discussions with colleagues from NASA,FGDC,USGS,EPA,GSA,Microsoft,ESIP,AAG CISG,CPGIS,UCGIS,GEO,and ISDE.
文摘The geospatial sciences face grand information technology(IT)challenges in the twenty-first century:data intensity,computing intensity,concurrent access intensity and spatiotemporal intensity.These challenges require the readiness of a computing infrastructure that can:(1)better support discovery,access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries;(2)provide real-time IT resources to enable real-time applications,such as emergency response;(3)deal with access spikes;and(4)provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge.The emergence of cloud computing provides a potential solution with an elastic,on-demand computing platform to integrateobservation systems,parameter extracting algorithms,phenomena simulations,analytical visualization and decision support,and to provide social impact and user feedbackthe essential elements of the geospatial sciences.We discuss the utilization of cloud computing to support the intensities of geospatial sciences by reporting from our investigations on how cloud computing could enable the geospatial sciences and how spatiotemporal principles,the kernel of the geospatial sciences,could be utilized to ensure the benefits of cloud computing.Four research examples are presented to analyze how to:(1)search,access and utilize geospatial data;(2)configure computing infrastructure to enable the computability of intensive simulation models;(3)disseminate and utilize research results for massive numbers of concurrent users;and(4)adopt spatiotemporal principles to support spatiotemporal intensive applications.The paper concludes with a discussion of opportunities and challenges for spatial cloud computing(SCC).
文摘This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar(Video-SAR)as a new mode of radar imaging for real-time remote sensing and surveillance.This research also considers new suggestions in the systematic design,research taxonomy,and future trends of radar data processing.Despite the conventional modes of SAR imaging,Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night(regardless of light sources)in all weathers.First,an introduction to Video-SAR is presented.Then,some specific properties of this imaging mode are reviewed.Particularly,this research covers one of the most important aspects of the Video-SAR systems,namely,the systematic design requirements,and also some new types of visual distortions which are different from the distortions,artifacts and noises observed in the conventional imaging radar.In addition,some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle(UAV)platforms,Internet of Multimedia Things(IoMT),Video-SAR data processing issues,and real-world applications are investigated.
文摘Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences.However,only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers:1)selecting an appropriate cloud platform for a specific application could be challenging,as various cloud services are available and 2)existing general cloud platforms are not designed to support geoscience applications,algorithms and models.To tackle such barriers,this research aims to design a hybrid cloud computing(HCC)platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform.This platform can manage different types of underlying cloud infrastructure(e.g.,private or public clouds),and enables geoscientists to test and leverage the cloud capabilities through a web interface.Additionally,the platform also provides different geospatial cloud services,such as workflow as a service,on the top of common cloud services(e.g.,infrastructure as a service)provided by general cloud platforms.Therefore,geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly.A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.
文摘A high-performance predictor for critical unstable generators(CUGs) of power systems is presented in this paper. The predictor is driven by the Map Reduce based parallelized neural networks. Specifically, a group of back propagation neural networks(BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing,enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert.Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.
基金Project supported by the Fund for Key Laboratory of National Defense Science and Technology of Underwater Acoustic Countermeasure Technology(Grant No.6412214200403)the National Defense Fundamental Scientific Research Program(Grant No.JCKY2020550C011)the Special Independent Scientific Research Program of National University of Defense Technology(Grant No.ZZKY-ZX-04-01)。
文摘Underwater acoustic models are effective tools for simulating underwater sound propagation.More than 50 years of research have been conducted on the theory and computational models of sound propagation in the ocean.Unfortunately,underwater sound propagation models were unable to solve practical large-scale three-dimensional problems for many years due to limited computing power and hardware conditions.Since the mid-1980s,research on high performance computing for acoustic propagation models in the field of underwater acoustics has flourished with the emergence of high-performance computing platforms,enabling underwater acoustic propagation models to solve many practical application problems that could not be solved before.In this paper,the contributions of research on high-performance computing for underwater acoustic propagation models since the 1980s are thoroughly reviewed and the possible development directions for the future are outlined.
基金This work was supported by the National Key Scientific and Technological Infrastructure project“Earth System Numer-ical Simulation Facility”(EarthLab)and the National Major Research High-Performance Computing Program of China(Grant No.2016YFB0200800).
文摘This article introduces“EarthLab”,a major new Earth system numerical simulation facility developed in China.EarthLab is a numerical simulation system for a physical climate system,an environmental system,an ecological system,a solid earth system,and a space weather system as a whole with a high-performance scientific computing platform.EarthLab consists of five key elements-namely:a global earth numerical simulation system,a regional high-precision simulation system,a supercomputing support and management system,a database,data assimilation and visualization system,and a high-performance computing system for earth sciences.EarthLab helps to study the atmosphere,hydrosphere,cryosphere,lithosphere,and biosphere,as well as their interactions,to improve the accuracy of predictions by integrating simulations and observations,and to provide a scientific foundation for major issues such as national disaster prevention and mitigation.The construction and operation of EarthLab will involve close cooperation with joint contributions and shared benefits.
文摘We lay out the ramifications of the 2020 pandemic for all people in geosciences,especially the young,and argue for significant changes on training and career development.We focus primarily on its devastating impact in USA and compare with that in other countries especially China.We review the potential effect for the next four years or so on the aspirations of an academic career versus more realistic career goals.We urge people in mid-career about the need to reassess previous goals.We stress the need for students or researchers to acquire technical skills in high-performance computing(HPC),data analytics,artificial intelligence,and/or visualization along with a broad set of technical skills in applied computer science and mathematics.We give advice about hot prospects in several areas that have great potential for advancement in the coming decade,such as visualization,deep learning,quantum computing and information,and cloud computing,all of which lie within the aegis of HPC.Our forecast is that the pandemic will significantly reshape the job landscape and career paths for both young and established researchers and we discuss bluntly the dire situation facing junior people in geosciences in the aftermath of the pandemic around the world until 2024.
基金This research was funded by the Research Foundation-Flanders(FWO),Junior postdoctoral fellowship(12ZQ220N),the joint JSPS-FWO scientific cooperation program(VS07820N)the Japan Society for the Promotion of Science(19H04132 and JPJSBP120202302)。
文摘Holographic displays have the promise to be the ultimate 3D display technology,able to account for all visual cues.Recent advances in photonics and electronics gave rise to high-resolution holographic display prototypes,indicating that they may become widely available in the near future.One major challenge in driving those display systems is computational:computer generated holography(CGH)consists of numerically simulating diffraction,which is very computationally intensive.Our goal in this paper is to give a broad overview of the state-of-the-art in CGH.We make a classification of modern CGH algorithms,we describe different algorithmic CGH acceleration techniques,discuss the latest dedicated hardware solutions and indicate how to evaluate the perceptual quality of CGH.We summarize our findings,discuss remaining challenges and make projections on the future of CGH.
基金Project supported by the National Key Research and Development Program of China(No.2017YFB0202104)。
文摘With the rapid increase of the size of applications and the complexity of the supercomputer architecture,topology-aware process mapping becomes increasingly important.High communication cost has become a dominant constraint of the performance of applications running on the supercomputer.To avoid a bad mapping strategy which can lead to terrible communication performance,we propose an optimized heuristic topology-aware mapping algorithm(OHTMA).The algorithm attempts to minimize the hop-byte metric that we use to measure the mapping results.OHTMA incorporates a new greedy heuristic method and pair-exchange-based optimization.It reduces the number of long-distance communications and effectively enhances the locality of the communication.Experimental results on the Tianhe-3 exascale supercomputer prototype indicate that OHTMA can significantly reduce the communication costs.
基金the assistance provided by Mr.Xiaoqiang Yue and Mr.Zheng Li from Xiangtan University in regard in our numerical experiments.Feng is partially supported by the NSFC Grant 11201398Program for Changjiang Scholars and Innovative Research Team in University of China Grant IRT1179+4 种基金Specialized research Fund for the Doctoral Program of Higher Education of China Grant 20124301110003Shu is partially supported by NSFC Grant 91130002 and 11171281the Scientific Research Fund of the Hunan Provincial Education Department of China Grant 12A138Xu is partially supported by NSFC Grant 91130011 and NSF DMS-1217142.Zhang is partially supported by the Dean Startup Fund,Academy of Mathematics and System Sciences,and by NSFC Grant 91130011.
文摘.The geometric multigrid method(GMG)is one of the most efficient solving techniques for discrete algebraic systems arising from elliptic partial differential equations.GMG utilizes a hierarchy of grids or discretizations and reduces the error at a number of frequencies simultaneously.Graphics processing units(GPUs)have recently burst onto the scientific computing scene as a technology that has yielded substantial performance and energy-efficiency improvements.A central challenge in implementing GMG on GPUs,though,is that computational work on coarse levels cannot fully utilize the capacity of a GPU.In this work,we perform numerical studies of GMG on CPU–GPU heterogeneous computers.Furthermore,we compare our implementation with an efficient CPU implementation of GMG and with the most popular fast Poisson solver,Fast Fourier Transform,in the cuFFT library developed by NVIDIA.
文摘As a case of space-time interaction,near-repeat calculation indicates that when an event takes place at a certain location,its immediate geographical surroundings would face an increased risk of experiencing subsequent events within a fairly short period of time.This paper presents an exploratory study that extends the investigation of the near-repeat phenomena to a series of space-time interaction,namely event chain calculation.Existing near-repeat tools can only deal with a limited amount of data due to computation constraints,let alone the event chain analysis.By deploying the modern accelerator technology and hybrid computer systems,this study demonstrates that large-scale near-repeat calculation or event chain analysis can be partially resolved through high-performance computing solutions to advance such a challenging statistical problem in both spatial analysis and crime geography.
