In our approach we have applied the wave digital method to get a digital replica of the analog circuit . This offers several benefits , like the ability for preserving the passivity of the analog counterpart , the possibility for a parallel processing approach , efficiency , and robustness of the resulting algorithmic model . The memristive device of the original circuit has been replaced by a novel multilevel memristor model with suitable features for anticipation of digital patterns . In this paper , we focus on applying parallel processing techniques to HEVC encoder in order to significantly reduce the computational power requirements without disturbing its coding efficiency . So , we propose several , synchronous and asynchronous , parallelization approaches working at a coarse grain parallelization level , based on the Group Of Pictures ( GOP ) , which we call GOP-based level . Results showed that a network trained with a back-propagation technique was able to learn to associate 32 different individuals with the data they generated , then successfully transfer that knowledge to new data . It is rec- ommended that similar experiments studying anomalous human-machine interactions should attempt to identify person-specific patterns in data in addition to measuring the magnitude of effects ; parallel processing analysis techniques are also recommended . The analysis is not free of internal problems , nor is it a total explanation of the visual ( or any other ) pathway . In the final section , however , a more positive claim is argued : that , despite its weaknesses , the parallel processing analysis provides a starting point for a systematic analysis and understanding of sensory pathways that may help transcend some of the problems which limit the current ambit of sensory neurobiology . In order to overcome the shortcoming of traditional array analysis method in dealing with program parallel process , the concepts of petri net are introduced . A new parallel processing analysis method is proposed . It can make use of the data dependence in program fully and furthermore improve the parallel degree of program . Assuming full knowledge of the channel at the receiver , the proposed schemes select a subset of the candidate transmit antenna elements based on channel amplitude , antenna correlation , Euclidean distance between transmit vectors or a combination of these ; demonstrating a significant improvement in error performance compared with conventional SM , while imposing significantly lower computational complexity than EDAS-SM . Moreover , the proposed schemes employ a novel complexity reducing technique , which allows parallel processing of algorithms and can easily be applied to existing schemes . The main approach is to obtain solutions of such type equations in a more direct manner as in contrast to completely interactive methods . Parallel processing of algorithms is made use of in the computation of solutions of such nonlinear matrix equations associated with nonlinear matrix differential equations arising in the study of nonlinear dynamical control systems . This paper reports the experimental work on the mass Chinese text segmentation problem and its optimal solution using Hadoop cluster , Hadoop Distributed File System ( HDFS ) for storage and using parallel processing to process large data sets using Map Reduce programming framework . We have done prototype implementation of Hadoop cluster , HDFS storage and Map Reduce framework for processing large text data sets by considering prototype of big data application scenarios . The results obtained from various experiments indicate favorable results of above IC and IK algorithm to address mass Chinese text segmentation problem . Hadoop is one such structure that gives dependable bunch of storeroom , which stores big data in a disseminated way utilizing an exceptional document framework , Hadoop Distributed File System and gives proficient parallel processing through Map Reduce structure . We use 2 algorithms for Congestion Control utilizing Machine Framework ( CCMF ) and Traffic Congestion Analyzer utilizing Map Reduce ( TCAMP ) which effectively analyze the dataset and predict the road accident reasons and suggest appropriate actions to the authorities for increasing road safety . We considered information mining innovations to interface recorded road attributes to reduce road accidents in India , and build up an arrangement of standards that could be utilized by the National Highway Authority of India to improve safety . Most e-commerce clients may tend to leave if the service delay is longer than a few seconds . To overcome this problem , we use the map reduce framework to reduce the real time data service delays . With the existing chronos architecture map reduce framework is used to minimize the delays . To handle and classify voluminous big data efficiently , a hybrid algorithm of logical combination of machine learning technique decision tree along with big data analytical platform Hive is introduced in this paper . The objective of proposed solution is to uplift the efficiency of traditional decision tree learning classifier by using map reduce based framework . Experiments designed on big dataset using proposed hybrid approach have shown promising results in terms of improved accuracy . Existing distributed memory parallel algorithms for counting exact triangles are either Map-Reduce or message passing interface ( MPI ) based . Map-Reduce based algorithms generate prohibitively large intermediate data and do not demonstrate reasonably good runtime efficiency . The MPI based algorithms offer fast computation of the number of triangles . Load balancing is done to handle the big data . Then the stored files will be subjected to map reduce process . In mapping process the files are mapped and a key value will be assigned to the files and then the files are reduced . The map reduce process is to be done by assigning mappers and reducers to the cloud servers . After the mapreduce process the files will be optimized using genetic algorithm . The data is distributed in the form of blocks and for every block an instance of mapper is mapped to process the block and then output of all the mappers is combined by reducer . This Map/Reduce process has inter-node parallelism . To further speedup the process and to efficiently utilize the resources like Central processing unit and Graphical processing unit , a parallel processing framework called OpenCL is used . Given the large number of web pages on the World Wide Web , efficient computation of PageRank becomes a challenging problem . We accelerated the power method for computing PageRank on AMD GPUs . The core component of the power method is the Sparse Matrix-Vector Multiplication ( SpMV ) . Using one NVIDIA Tesla K40 GPU , the GPU-based YSU PBL scheme achieves a speedup of 193 × with respect to its CPU counterpart running on one CPU core , whereas the speedup for one CPU socket ( 4 cores ) with respect to 1 CPU core is only 3.5 × . We can even boost the speedup to 360 × with respect to 1 CPU core as two K40 GPUs are applied . Furthermore , it is easy to extend the DBFP mechanism on the heterogeneous system equipped with multiple GPUs and achieve high resource utilization . The results show that it can achieve 99 and 90 % of theoretical performance for dense general matrix multiplication on one GPU and two GPUs respectively with Nvidia GTX480 or K40 GPUs . It also enables K-means and T-nearest-neighbor algorithms to process larger datasets , which used to be limited by the GPU device memory . The performance of each of the individual code versions is assessed through calculations on a test system of five alanine amino acids in an α-helix arrangement using one-electron basis sets of increasing size ( ranging from double- to pentuple-ζ quality ) . For all but the smallest problem size of the present study , the optimized accelerated code ( using six K40 GPUs ) is capable of reducing the total time-to-solution by at least an order of magnitude over an optimized CPU-only reference implementation . We demonstrate the effectiveness of our proposed techniques by running experiments on the 2000-hour Switchboard speech recognition task and the ImageNet computer vision task . On an IBM P9 supercomputer , our system is able to train an LSTM acoustic model in 2.28 hours with 7.5 % WER on the Hub5 - 2000 Switchboard ( SWB ) test set and 13.3 % WER on the CallHome ( CH ) test set using 64 V100 GPUs and in 1.98 hours with 7.7 % WER on SWB and 13.3 % WER on CH using 128 V100 GPUs , the fastest training time reported to date . Moreover , a single-GPU benchmark on ORNL 's SummitDev and IBM 's AC922 suggests that the host-to-device NVLink can boost performance over PCIe by a remarkable 40\% . Lastly , we demonstrate , through a flow simulation in realistic shale pores , that the CPU counterpart requires 840 Power9 cores to rival the performance delivered by our package with four V100 GPUs on ORNL 's Summit architecture . This simulation package enables quick-turnaround and high-throughput mesoscopic numerical simulations for investigating complex flow phenomena in nano- to micro-porous rocks with realistic pore geometries . To be more specific , we aim to efficiently search for the feature pyramid network ( FPN ) as well as the prediction head of a simple anchor-free object detector , namely FCOS , using a tailored reinforcement learning paradigm . With carefully designed search space , search algorithms and strategies for evaluating network quality , we are able to efficiently search a top-performing detection architecture within 4 days using 8 V100 GPUs . The discovered architecture surpasses state-of-the-art object detection models ( such as Faster R-CNN , RetinaNet and FCOS ) by 1.5 to 3.5 points in AP on the COCO dataset , with comparable computation complexity and memory footprint , demonstrating the efficacy of the proposed NAS for object detection . Comparison of performance of various GPU cards . In this paper , we compared performances of different NVIDIA CUDA-enabled GPUs in accelerating MoM based software for EM modelling in frequency domain , WIPL-D. Three different GeForce GPUs were used : GTX 480 , GTX 580 and GTX 680 . The performance was measured on three EM problems : PEC sphere , Luneburg lens , and microstrip patch antenna array placed at the helicopter fuselage . To show the correctness and value of our framework and approach , we implement a selection of NN operations , covering the core operations needed for deploying three common image-processing neural networks . We target three different hardware platforms : NVIDIA , AMD , and Qualcomm GPUs . On NVIDIA GPUs , we show both portability between OpenCL and CUDA as well competitive performance compared to the vendor library . On NVIDIA GPUs , we show both portability between OpenCL and CUDA as well competitive performance compared to the vendor library . On Qualcomm GPUs , we show that our framework enables productive development of target-specific optimizations , and achieves reasonable absolute performance . Finally , On AMD GPUs , we show initial results that indicate our framework can yield reasonable performance on a new platform with minimal effort . Durch die Konvergenz der hohen massiv parallelen Rechenleistung der GPUs und der Interaktionsfahigkeiten in einer einzelnen Anwendung kann eine erhebliche Steigerung der Anwendungsqualitat erzielt werden . Dabei ist es durch Einsatz mehrerer GPUs moglich , dreidimensionale Stromungsprobleme mit praxisrelevanter Problemgrose zu berechnen und gleichzeitig eine interaktive Manipulation und Exploration des Stromungsgebiets zur Laufzeit zu ermoglichen . This article provides a broad perspective of the potential applicability of graphical processing units ( GPUs ) computing power in robotics , specifically in the well-known problem of two-dimensional ( 2-D ) robotic mapping . There are three possible ways of exploiting these massively parallel devices : 1 ) parallelizing existing algorithms , 2 ) integrating already existing parallelized general purpose software , and 3 ) use of its high-computational capabilities in the inception of new algorithms . The License Plate Recognition task is generally a computational-intensive task , where the entire input image frame is scanned , the found plates are segmented , and character recognition is then performed . Frequently , this processing is made using general purposes GPUs . This paper proposes an embedded solution to detect and recognize Brazilian license plates using convolutional neural networks ( CNN ) . The inter-process communication amongst the various processes during the computation is the major factor for the high speed distributed computing . In this paper , the performance of the most common several parallel processing tools are discussed and compared . PVM is a software infrastructure that emulates a generalized distributed memory in heterogeneous networked environments . But it is not widely used because of its long training time . Graphics Processing Units(GPUs ) have become powerful parallel processing tools for general purpose computing . In this paper , we use CUDA compatible GPU to accelerate RankBoost training procedure . The underlying algorithm consists of four stages : 1 ) Calculate the conductance values of the components in each block , 2 ) Build a matrix representing the connectivity of components , 3 ) Assign a unique number for each node , and finally 4 ) Calculate the admittance matrix using the data obtained from the previous steps . This is the initial work to support the development of a parallel computing tool where the power system will be solved using a tool developed by the University of Texas called CEMSolver . The second phase of this joint project will be finding the most optimal partitioned systems in such a way that the matrices of each partition are distributed to different CPUs and the total CPU usage for these subsystems will be minimal . The largest speedup value of 11.78 achieved here on the parallelization using SMHPC with 30 processors was encouraging . SMHPC was a very easy-to-use and efficient parallel computing tool for the CFD problems . Benefitting from the online environment , MOOC platforms can collect and store a huge amount of data related to learning processes . We use Storm as the parallel computing tool to accomplish the data analysis of MOOC . Our research focuses on three types of learning quality evaluation : relationship between students ’ forum participation and their academic performance , relationship between students ’ forum emotion and their academic performance , relationship between students ’ video seeking operation and their academic performance . The proposed parallel k-means algorithm was tested with our GPS data and the results are efficient in processing large datasets . It is a parallel data processing tool which is gathering significant importance from industry and academia especially with appearance of a new term to describe massive datasets having large-volume , high-complexity and growing data from different sources , “ big data ” . A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly . While MapReduce is used in many a ... The tool is highly configurable and provides a simple framework for classifier optimization and extensibility . MATLAB 's parallel processing toolbox is utilized during batch processing . Output formats include MySQL database entry , tab-delimited text and MATLAB archive ( .MAT ) . We build a prototype cluster which runs Wikipedia to demonstrate the use of our design in a real environment . For the MapReduce workload in the second study , we develop BEEMR ( Berkeley Energy Efficient MapReduce ) , an energy efficient MapReduce workload manager motivated by empirical analysis of real-life traces at Facebook . The key insight is that although MIA clusters host huge data volumes , the interactive jobs operate on a small fraction of the data , and thus can be served by a small pool of dedicated machines ; the less time-sensitive jobs can run on the rest of the cluster in a batch fashion . Large-scale data centers for cloud computing services consist of a number of commodity servers , resulting in a huge amount of power consumption . In order to save power consumption , BEEMR ( Berkeley Energy Efficient MapReduce ) , a MapReduce workload manager , is proposed . In a BEEMR-based data center , servers are allocated to either of the interactive and batch zones . In some recent works , the MapReduce model has been also used to exploit parallelism in other non-distributed environments , such as multi-cores , heterogeneous processors and GPUs . In this paper we introduce a novel approach for exploiting the heterogeneity of a Cell BE cluster linking an existing MapReduce runtime implementation for distributed clusters and one runtime to exploit the parallelism of the Cell BE nodes . The novel contribution of this work is the design and evaluation of a MapReduce execution environment that effectively exploits the parallelism existing at both the Cell BE cluster level and the heterogeneous processors level . In this paper , we investigate compilers for low power with parallel design patterns on embedded multicore systems . We evaluate four major parallel design patterns , Pipe and Filter , MapReduce with Iterator , Puppeteer , and Bulk Synchronous Parallel ( BSP ) Model . Our work attempts to devise power optimization schemes in compilers by exploiting the opportunities of the recurring patterns of embedded multicore programs . Hadoop is a software framework for dispersed preparing of vast information sets . Hadoop employments MapReduce paradigm to perstructure dispersed preparing over bunches of computers to reduce the time included in dissecting the item ’s highlight ( watchwords of a book ) . The proposed framework is reliable and fault tolerant when compared to the existing proposal frameworks as it gathers the appraisals from the client to foresee the interest and analyses the thing to find the features . In this paper , we propose to apply Meta-learning programmed with MapReduce to avoid parallelizing machine learning algorithms while also improving their scalability to big datasets . The experiments conducted on Hadoop ’s fully distributed mode on Amazon EC2 demonstrate that our algorithm Meta-MapReduce ( MMR ) reduces the training computational complexity significantly when the number of computing nodes increases while obtaining smaller error rates than those on a single node . The comparison of MMR with the contemporary parallelized Ad a B oost algorithm , AdaBoost . In this thesis , we study efficient distributed solutions for LOF . A baseline MapReduce solution for LOF implemented with Apache Spark , named MR-LOF , is introduced . We demonstrate its disadvantages in communication cost and execution time through complexity analysis and experimental evaluation . A hierarchical Multi-Cloud architecture is proposed which is able to transparently connect and manage several Clouds . The results obtained with our architecture and our MapReduce SW implementation in five Public Clouds show that , only by using the free quota , we were able to run the SW application over a huge genomic database in time that is comparable with the one obtained in multicore clusters , showing the appropriateness of our approach . The GTA transformation and optimization mechanism implemented inside is a black-box to the end users , while users can extend the library by modifying existing ( or implementing new ) generators , testers or aggregators through standard programming interfaces of the GTA library . This GTA programming library supports both sequential or parallel execution on single computer and on-cluster execution with MapReduce computing engines . We evaluate our library by giving the results of our experiments on large data to show the efficiency , scalability and usefulness of this GTA library . An integrated technique is proposed for handling social network interactions using Hadoop , which deals with huge computations as well as data analysis and Cassandra database ’s replication strategy for fault tolerance in distributed Environment , which in turn helps in data analytics . Hypergraph is used as an input to Mapreduce programming model of Hadoop , which reduce the write frequencies across the partitions . Honeypot technique is used as an early-warning and advanced security surveillance tool for data security against the attackers . On top of these methods , an auto-scaling mechanism was developed to dynamically allocate resources on-demand based on profile data and performance models for the application execution to meet the given time requirements . In addition , Virtual Hadoop can utilize Docker containers to facilitate the auto-scaling mechanism , where a container encapsulates a Hadoop node with the capability to leverage heterogeneous computing engines . Our experimental results reveal the efficiency of Virtual Hadoop , and hopefully the experiences and discussion presented in this paper will ease the adoption of heterogeneous computing for efficient big data processing . In the first part of this dissertation , I present a study of the performance of the Tessera D&R computational environment through designed experiments . The base of the D&R computational environment is RHIPE , the R and Hadoop Integrated Programming Environment . R is a widely used interactive language for data analysis . Computational methods and computational environments are critical . One development in computational environments is RHIPE , the R and Hadoop Integrated Programming Environment . It enables D&R to be carried out wholly from within R. Statistical division methods and statistical recombination methods are very broad . The performed tests should show that DSE is capable of successfully executing Hadoop applications without the need to adapt them for the underlying Cassandra distributed storage system [ 2 ] . Due to the Cassandra File System ( CFS ) [ 3 ] , which supports the Hadoop Distributed File System API , Hadoop stack applications should seamlessly run in DSE . The report is structured as follows : Section 2 provides a brief description of the technologies involved in our study . Combining with virtualization techniques , the paper proposes a novel architecture for fault diagnosis of weapon system based on test resource virtualization . Through collecting test data from the equipment , the paper applies the Condor and Hadoop techniques to process and achieve the virtualization of test resource . With the fault diagnosis methods applied to the architecture , the diagnosis results are obtained based on the test data and test resource , and artificial intelligence methods are inquired by the computational resource inquiring server to provide the fast computation for fault diagnosis . We implement our design as a user-space library ( KVFS ) providing an HDFS filesystem over an HBase key-value store . KVFS is designed to run Hadoop style analytics such as MapReduce , Hive , Pig and Mahout over NoSQL stores without the use of HDFS . We perform a preliminary evaluation of KVFS against a native HDFS setup using DFSIO with varying number of threads . In this paper , we describe a hardware prototype of our accelerated Hadoop deep learning system architecture and report initial performance and energy reduction results . By accelerating the convolutional layers of deep learning Convolutional Neural Network , we have observed a potential speed-up of 12.6 times and an energy reduction of 87.5 % on a 6-node FPGA accelerated Hadoop cluster . But what exactly is it , and more importantly , how do you even get a Hadoop cluster up and running ? From Apress , the name you ve come to trust for handson technical knowledge , Pro Hadoop brings you up to speed on Hadoop . You learn the ins and outs of MapReduce ; how to structure a cluster , design , and implement the Hadoop file system ; and how to build your first cloudcomputing tasks using Hadoop . MIPr is based on MapReduce and its open source implementation Apache Hadoop . MIPr provides various forms of image representations in Hadoop internal format and the input/output tools for integration of image processing into Hadoop data workflow . The image formats in the MIPr framework are based on the popular image processing libraries . In this paper , in the distributed file system that uses SSD as cache , we propose a mechanism that acquires a block ID that is scheduled to be used or already in use and loads it in SSD cache before the I/O request about the block is made in order to increase the performance of SSD cache during performance of Hadoop MapReduce task . In order to verify the reduction of MapReduce processing time and the improvement of Read I/O performance , which were run based on the proposed mechanism , experiments were carried out via Hadoop Benchmark tool . We could verify through the experiments that the performance of Hadoop MapReduce system was about 11 % improved in the system where the proposed mechanism was applied than in the distributed file system where the existing SSD cache was applied . The adapter supports using Hadoop-based applications as part of Galaxy workflows . We demonstrate a practical application where this Hadoop-Galaxy adapter was used at CRS4 to accelerate the bioinformatics analysis of viral vector integration sites through the introduction of Hadoop-based computation components , while keeping the workflow under control of biologists with little specific technical training .
In our approach we have applied the wave digital method to get a digital replica of the analog circuit . This offers several benefits , like the ability for preserving the passivity of the analog counterpart , the possibility for a parallel processing approach , efficiency , and robustness of the resulting algorithmic model . The memristive device of the original circuit has been replaced by a novel multilevel memristor model with suitable features for anticipation of digital patterns .
In this paper , we focus on applying parallel processing techniques to HEVC encoder in order to significantly reduce the computational power requirements without disturbing its coding efficiency . So , we propose several , synchronous and asynchronous , parallelization approaches working at a coarse grain parallelization level , based on the Group Of Pictures ( GOP ) , which we call GOP-based level .
Results showed that a network trained with a back-propagation technique was able to learn to associate 32 different individuals with the data they generated , then successfully transfer that knowledge to new data . It is rec- ommended that similar experiments studying anomalous human-machine interactions should attempt to identify person-specific patterns in data in addition to measuring the magnitude of effects ; parallel processing analysis techniques are also recommended .
The analysis is not free of internal problems , nor is it a total explanation of the visual ( or any other ) pathway . In the final section , however , a more positive claim is argued : that , despite its weaknesses , the parallel processing analysis provides a starting point for a systematic analysis and understanding of sensory pathways that may help transcend some of the problems which limit the current ambit of sensory neurobiology .
In order to overcome the shortcoming of traditional array analysis method in dealing with program parallel process , the concepts of petri net are introduced . A new parallel processing analysis method is proposed . It can make use of the data dependence in program fully and furthermore improve the parallel degree of program .
Assuming full knowledge of the channel at the receiver , the proposed schemes select a subset of the candidate transmit antenna elements based on channel amplitude , antenna correlation , Euclidean distance between transmit vectors or a combination of these ; demonstrating a significant improvement in error performance compared with conventional SM , while imposing significantly lower computational complexity than EDAS-SM . Moreover , the proposed schemes employ a novel complexity reducing technique , which allows parallel processing of algorithms and can easily be applied to existing schemes .
The main approach is to obtain solutions of such type equations in a more direct manner as in contrast to completely interactive methods . Parallel processing of algorithms is made use of in the computation of solutions of such nonlinear matrix equations associated with nonlinear matrix differential equations arising in the study of nonlinear dynamical control systems .
This paper reports the experimental work on the mass Chinese text segmentation problem and its optimal solution using Hadoop cluster , Hadoop Distributed File System ( HDFS ) for storage and using parallel processing to process large data sets using Map Reduce programming framework . We have done prototype implementation of Hadoop cluster , HDFS storage and Map Reduce framework for processing large text data sets by considering prototype of big data application scenarios . The results obtained from various experiments indicate favorable results of above IC and IK algorithm to address mass Chinese text segmentation problem .
Hadoop is one such structure that gives dependable bunch of storeroom , which stores big data in a disseminated way utilizing an exceptional document framework , Hadoop Distributed File System and gives proficient parallel processing through Map Reduce structure . We use 2 algorithms for Congestion Control utilizing Machine Framework ( CCMF ) and Traffic Congestion Analyzer utilizing Map Reduce ( TCAMP ) which effectively analyze the dataset and predict the road accident reasons and suggest appropriate actions to the authorities for increasing road safety . We considered information mining innovations to interface recorded road attributes to reduce road accidents in India , and build up an arrangement of standards that could be utilized by the National Highway Authority of India to improve safety .
Most e-commerce clients may tend to leave if the service delay is longer than a few seconds . To overcome this problem , we use the map reduce framework to reduce the real time data service delays . With the existing chronos architecture map reduce framework is used to minimize the delays .
To handle and classify voluminous big data efficiently , a hybrid algorithm of logical combination of machine learning technique decision tree along with big data analytical platform Hive is introduced in this paper . The objective of proposed solution is to uplift the efficiency of traditional decision tree learning classifier by using map reduce based framework . Experiments designed on big dataset using proposed hybrid approach have shown promising results in terms of improved accuracy .
Existing distributed memory parallel algorithms for counting exact triangles are either Map-Reduce or message passing interface ( MPI ) based . Map-Reduce based algorithms generate prohibitively large intermediate data and do not demonstrate reasonably good runtime efficiency . The MPI based algorithms offer fast computation of the number of triangles .
Load balancing is done to handle the big data . Then the stored files will be subjected to map reduce process . In mapping process the files are mapped and a key value will be assigned to the files and then the files are reduced . The map reduce process is to be done by assigning mappers and reducers to the cloud servers . After the mapreduce process the files will be optimized using genetic algorithm .
The data is distributed in the form of blocks and for every block an instance of mapper is mapped to process the block and then output of all the mappers is combined by reducer . This Map/Reduce process has inter-node parallelism . To further speedup the process and to efficiently utilize the resources like Central processing unit and Graphical processing unit , a parallel processing framework called OpenCL is used .
Given the large number of web pages on the World Wide Web , efficient computation of PageRank becomes a challenging problem . We accelerated the power method for computing PageRank on AMD GPUs . The core component of the power method is the Sparse Matrix-Vector Multiplication ( SpMV ) .
Using one NVIDIA Tesla K40 GPU , the GPU-based YSU PBL scheme achieves a speedup of 193 × with respect to its CPU counterpart running on one CPU core , whereas the speedup for one CPU socket ( 4 cores ) with respect to 1 CPU core is only 3.5 × . We can even boost the speedup to 360 × with respect to 1 CPU core as two K40 GPUs are applied .
Furthermore , it is easy to extend the DBFP mechanism on the heterogeneous system equipped with multiple GPUs and achieve high resource utilization . The results show that it can achieve 99 and 90 % of theoretical performance for dense general matrix multiplication on one GPU and two GPUs respectively with Nvidia GTX480 or K40 GPUs . It also enables K-means and T-nearest-neighbor algorithms to process larger datasets , which used to be limited by the GPU device memory .
The performance of each of the individual code versions is assessed through calculations on a test system of five alanine amino acids in an α-helix arrangement using one-electron basis sets of increasing size ( ranging from double- to pentuple-ζ quality ) . For all but the smallest problem size of the present study , the optimized accelerated code ( using six K40 GPUs ) is capable of reducing the total time-to-solution by at least an order of magnitude over an optimized CPU-only reference implementation .
We demonstrate the effectiveness of our proposed techniques by running experiments on the 2000-hour Switchboard speech recognition task and the ImageNet computer vision task . On an IBM P9 supercomputer , our system is able to train an LSTM acoustic model in 2.28 hours with 7.5 % WER on the Hub5 - 2000 Switchboard ( SWB ) test set and 13.3 % WER on the CallHome ( CH ) test set using 64 V100 GPUs and in 1.98 hours with 7.7 % WER on SWB and 13.3 % WER on CH using 128 V100 GPUs , the fastest training time reported to date .
Moreover , a single-GPU benchmark on ORNL 's SummitDev and IBM 's AC922 suggests that the host-to-device NVLink can boost performance over PCIe by a remarkable 40\% . Lastly , we demonstrate , through a flow simulation in realistic shale pores , that the CPU counterpart requires 840 Power9 cores to rival the performance delivered by our package with four V100 GPUs on ORNL 's Summit architecture . This simulation package enables quick-turnaround and high-throughput mesoscopic numerical simulations for investigating complex flow phenomena in nano- to micro-porous rocks with realistic pore geometries .
To be more specific , we aim to efficiently search for the feature pyramid network ( FPN ) as well as the prediction head of a simple anchor-free object detector , namely FCOS , using a tailored reinforcement learning paradigm . With carefully designed search space , search algorithms and strategies for evaluating network quality , we are able to efficiently search a top-performing detection architecture within 4 days using 8 V100 GPUs . The discovered architecture surpasses state-of-the-art object detection models ( such as Faster R-CNN , RetinaNet and FCOS ) by 1.5 to 3.5 points in AP on the COCO dataset , with comparable computation complexity and memory footprint , demonstrating the efficacy of the proposed NAS for object detection .
Comparison of performance of various GPU cards . In this paper , we compared performances of different NVIDIA CUDA-enabled GPUs in accelerating MoM based software for EM modelling in frequency domain , WIPL-D. Three different GeForce GPUs were used : GTX 480 , GTX 580 and GTX 680 . The performance was measured on three EM problems : PEC sphere , Luneburg lens , and microstrip patch antenna array placed at the helicopter fuselage .
To show the correctness and value of our framework and approach , we implement a selection of NN operations , covering the core operations needed for deploying three common image-processing neural networks . We target three different hardware platforms : NVIDIA , AMD , and Qualcomm GPUs . On NVIDIA GPUs , we show both portability between OpenCL and CUDA as well competitive performance compared to the vendor library .
On NVIDIA GPUs , we show both portability between OpenCL and CUDA as well competitive performance compared to the vendor library . On Qualcomm GPUs , we show that our framework enables productive development of target-specific optimizations , and achieves reasonable absolute performance . Finally , On AMD GPUs , we show initial results that indicate our framework can yield reasonable performance on a new platform with minimal effort .
Durch die Konvergenz der hohen massiv parallelen Rechenleistung der GPUs und der Interaktionsfahigkeiten in einer einzelnen Anwendung kann eine erhebliche Steigerung der Anwendungsqualitat erzielt werden . Dabei ist es durch Einsatz mehrerer GPUs moglich , dreidimensionale Stromungsprobleme mit praxisrelevanter Problemgrose zu berechnen und gleichzeitig eine interaktive Manipulation und Exploration des Stromungsgebiets zur Laufzeit zu ermoglichen .
This article provides a broad perspective of the potential applicability of graphical processing units ( GPUs ) computing power in robotics , specifically in the well-known problem of two-dimensional ( 2-D ) robotic mapping . There are three possible ways of exploiting these massively parallel devices : 1 ) parallelizing existing algorithms , 2 ) integrating already existing parallelized general purpose software , and 3 ) use of its high-computational capabilities in the inception of new algorithms .
The License Plate Recognition task is generally a computational-intensive task , where the entire input image frame is scanned , the found plates are segmented , and character recognition is then performed . Frequently , this processing is made using general purposes GPUs . This paper proposes an embedded solution to detect and recognize Brazilian license plates using convolutional neural networks ( CNN ) .
The inter-process communication amongst the various processes during the computation is the major factor for the high speed distributed computing . In this paper , the performance of the most common several parallel processing tools are discussed and compared . PVM is a software infrastructure that emulates a generalized distributed memory in heterogeneous networked environments .
But it is not widely used because of its long training time . Graphics Processing Units(GPUs ) have become powerful parallel processing tools for general purpose computing . In this paper , we use CUDA compatible GPU to accelerate RankBoost training procedure .
The underlying algorithm consists of four stages : 1 ) Calculate the conductance values of the components in each block , 2 ) Build a matrix representing the connectivity of components , 3 ) Assign a unique number for each node , and finally 4 ) Calculate the admittance matrix using the data obtained from the previous steps . This is the initial work to support the development of a parallel computing tool where the power system will be solved using a tool developed by the University of Texas called CEMSolver . The second phase of this joint project will be finding the most optimal partitioned systems in such a way that the matrices of each partition are distributed to different CPUs and the total CPU usage for these subsystems will be minimal .
The largest speedup value of 11.78 achieved here on the parallelization using SMHPC with 30 processors was encouraging . SMHPC was a very easy-to-use and efficient parallel computing tool for the CFD problems .
Benefitting from the online environment , MOOC platforms can collect and store a huge amount of data related to learning processes . We use Storm as the parallel computing tool to accomplish the data analysis of MOOC . Our research focuses on three types of learning quality evaluation : relationship between students ’ forum participation and their academic performance , relationship between students ’ forum emotion and their academic performance , relationship between students ’ video seeking operation and their academic performance .
The proposed parallel k-means algorithm was tested with our GPS data and the results are efficient in processing large datasets . It is a parallel data processing tool which is gathering significant importance from industry and academia especially with appearance of a new term to describe massive datasets having large-volume , high-complexity and growing data from different sources , “ big data ” .
A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly . While MapReduce is used in many a ...
The tool is highly configurable and provides a simple framework for classifier optimization and extensibility . MATLAB 's parallel processing toolbox is utilized during batch processing . Output formats include MySQL database entry , tab-delimited text and MATLAB archive ( .MAT ) .
We build a prototype cluster which runs Wikipedia to demonstrate the use of our design in a real environment . For the MapReduce workload in the second study , we develop BEEMR ( Berkeley Energy Efficient MapReduce ) , an energy efficient MapReduce workload manager motivated by empirical analysis of real-life traces at Facebook . The key insight is that although MIA clusters host huge data volumes , the interactive jobs operate on a small fraction of the data , and thus can be served by a small pool of dedicated machines ; the less time-sensitive jobs can run on the rest of the cluster in a batch fashion .
Large-scale data centers for cloud computing services consist of a number of commodity servers , resulting in a huge amount of power consumption . In order to save power consumption , BEEMR ( Berkeley Energy Efficient MapReduce ) , a MapReduce workload manager , is proposed . In a BEEMR-based data center , servers are allocated to either of the interactive and batch zones .
In some recent works , the MapReduce model has been also used to exploit parallelism in other non-distributed environments , such as multi-cores , heterogeneous processors and GPUs . In this paper we introduce a novel approach for exploiting the heterogeneity of a Cell BE cluster linking an existing MapReduce runtime implementation for distributed clusters and one runtime to exploit the parallelism of the Cell BE nodes . The novel contribution of this work is the design and evaluation of a MapReduce execution environment that effectively exploits the parallelism existing at both the Cell BE cluster level and the heterogeneous processors level .
In this paper , we investigate compilers for low power with parallel design patterns on embedded multicore systems . We evaluate four major parallel design patterns , Pipe and Filter , MapReduce with Iterator , Puppeteer , and Bulk Synchronous Parallel ( BSP ) Model . Our work attempts to devise power optimization schemes in compilers by exploiting the opportunities of the recurring patterns of embedded multicore programs .
Hadoop is a software framework for dispersed preparing of vast information sets . Hadoop employments MapReduce paradigm to perstructure dispersed preparing over bunches of computers to reduce the time included in dissecting the item ’s highlight ( watchwords of a book ) . The proposed framework is reliable and fault tolerant when compared to the existing proposal frameworks as it gathers the appraisals from the client to foresee the interest and analyses the thing to find the features .
In this paper , we propose to apply Meta-learning programmed with MapReduce to avoid parallelizing machine learning algorithms while also improving their scalability to big datasets . The experiments conducted on Hadoop ’s fully distributed mode on Amazon EC2 demonstrate that our algorithm Meta-MapReduce ( MMR ) reduces the training computational complexity significantly when the number of computing nodes increases while obtaining smaller error rates than those on a single node . The comparison of MMR with the contemporary parallelized Ad a B oost algorithm , AdaBoost .
In this thesis , we study efficient distributed solutions for LOF . A baseline MapReduce solution for LOF implemented with Apache Spark , named MR-LOF , is introduced . We demonstrate its disadvantages in communication cost and execution time through complexity analysis and experimental evaluation .
A hierarchical Multi-Cloud architecture is proposed which is able to transparently connect and manage several Clouds . The results obtained with our architecture and our MapReduce SW implementation in five Public Clouds show that , only by using the free quota , we were able to run the SW application over a huge genomic database in time that is comparable with the one obtained in multicore clusters , showing the appropriateness of our approach .
The GTA transformation and optimization mechanism implemented inside is a black-box to the end users , while users can extend the library by modifying existing ( or implementing new ) generators , testers or aggregators through standard programming interfaces of the GTA library . This GTA programming library supports both sequential or parallel execution on single computer and on-cluster execution with MapReduce computing engines . We evaluate our library by giving the results of our experiments on large data to show the efficiency , scalability and usefulness of this GTA library .
An integrated technique is proposed for handling social network interactions using Hadoop , which deals with huge computations as well as data analysis and Cassandra database ’s replication strategy for fault tolerance in distributed Environment , which in turn helps in data analytics . Hypergraph is used as an input to Mapreduce programming model of Hadoop , which reduce the write frequencies across the partitions . Honeypot technique is used as an early-warning and advanced security surveillance tool for data security against the attackers .
On top of these methods , an auto-scaling mechanism was developed to dynamically allocate resources on-demand based on profile data and performance models for the application execution to meet the given time requirements . In addition , Virtual Hadoop can utilize Docker containers to facilitate the auto-scaling mechanism , where a container encapsulates a Hadoop node with the capability to leverage heterogeneous computing engines . Our experimental results reveal the efficiency of Virtual Hadoop , and hopefully the experiences and discussion presented in this paper will ease the adoption of heterogeneous computing for efficient big data processing .
In the first part of this dissertation , I present a study of the performance of the Tessera D&R computational environment through designed experiments . The base of the D&R computational environment is RHIPE , the R and Hadoop Integrated Programming Environment . R is a widely used interactive language for data analysis .
Computational methods and computational environments are critical . One development in computational environments is RHIPE , the R and Hadoop Integrated Programming Environment . It enables D&R to be carried out wholly from within R. Statistical division methods and statistical recombination methods are very broad .
The performed tests should show that DSE is capable of successfully executing Hadoop applications without the need to adapt them for the underlying Cassandra distributed storage system [ 2 ] . Due to the Cassandra File System ( CFS ) [ 3 ] , which supports the Hadoop Distributed File System API , Hadoop stack applications should seamlessly run in DSE . The report is structured as follows : Section 2 provides a brief description of the technologies involved in our study .
Combining with virtualization techniques , the paper proposes a novel architecture for fault diagnosis of weapon system based on test resource virtualization . Through collecting test data from the equipment , the paper applies the Condor and Hadoop techniques to process and achieve the virtualization of test resource . With the fault diagnosis methods applied to the architecture , the diagnosis results are obtained based on the test data and test resource , and artificial intelligence methods are inquired by the computational resource inquiring server to provide the fast computation for fault diagnosis .
We implement our design as a user-space library ( KVFS ) providing an HDFS filesystem over an HBase key-value store . KVFS is designed to run Hadoop style analytics such as MapReduce , Hive , Pig and Mahout over NoSQL stores without the use of HDFS . We perform a preliminary evaluation of KVFS against a native HDFS setup using DFSIO with varying number of threads .
In this paper , we describe a hardware prototype of our accelerated Hadoop deep learning system architecture and report initial performance and energy reduction results . By accelerating the convolutional layers of deep learning Convolutional Neural Network , we have observed a potential speed-up of 12.6 times and an energy reduction of 87.5 % on a 6-node FPGA accelerated Hadoop cluster .
But what exactly is it , and more importantly , how do you even get a Hadoop cluster up and running ? From Apress , the name you ve come to trust for handson technical knowledge , Pro Hadoop brings you up to speed on Hadoop . You learn the ins and outs of MapReduce ; how to structure a cluster , design , and implement the Hadoop file system ; and how to build your first cloudcomputing tasks using Hadoop .
MIPr is based on MapReduce and its open source implementation Apache Hadoop . MIPr provides various forms of image representations in Hadoop internal format and the input/output tools for integration of image processing into Hadoop data workflow . The image formats in the MIPr framework are based on the popular image processing libraries .
In this paper , in the distributed file system that uses SSD as cache , we propose a mechanism that acquires a block ID that is scheduled to be used or already in use and loads it in SSD cache before the I/O request about the block is made in order to increase the performance of SSD cache during performance of Hadoop MapReduce task . In order to verify the reduction of MapReduce processing time and the improvement of Read I/O performance , which were run based on the proposed mechanism , experiments were carried out via Hadoop Benchmark tool . We could verify through the experiments that the performance of Hadoop MapReduce system was about 11 % improved in the system where the proposed mechanism was applied than in the distributed file system where the existing SSD cache was applied .
The adapter supports using Hadoop-based applications as part of Galaxy workflows . We demonstrate a practical application where this Hadoop-Galaxy adapter was used at CRS4 to accelerate the bioinformatics analysis of viral vector integration sites through the introduction of Hadoop-based computation components , while keeping the workflow under control of biologists with little specific technical training .
Wave digital information anticipator Synchronous and asynchronous HEVC parallel encoder versions based on a GOP approach Searching for "Signatures" in Anomalous Human-Machine Interaction Data: A Neural Network Approach* Extensions and Limits of the Parallel Processing Analysis New program parallel processing method based on Petri net Low-complexity transmit antenna selection schemes for spatial modulation Nonlinear operator equations and applications to modelling A Comparison of Approaches to Chinese Word Segmentation in Hadoop A Data Mining Framework to Analyze Road Accident Data Using Map Reduce Methods CCMF and TCAMP Algorithms An Efficient Way to Reduce Data Service Delay Using Map Reduce Framework A Novel Hybrid Technique for Big Data Classification Using Decision Tree Learning A fast parallel algorithm for counting triangles in graphs using dynamic load balancing An Effective Optimized Genetic Algorithm for Scalable Information Retrieval from Cloud using Big Data HCLBLAST for Genome Sequence Matching Efficient PageRank and SpMV Computation on AMD GPUs Development of efficient GPU parallelization of WRF Yonsei University planetary boundary layer scheme Dual buffer rotation four-stage pipeline for CPU–GPU cooperative computing Incrementally accelerating the RI-MP2 correlated method of electronic structure theory using OpenACC compiler directives Improving Efficiency in Large-Scale Decentralized Distributed Training A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics NAS-FCOS: Fast Neural Architecture Search for Object Detection GPU accelerated EM modelling in frequency domain: Comparison of performance of various GPU cards Boda: A Holistic Approach for Implementing Neural Network Computations A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications Ein integrierter Softwareansatz zur interaktiven Exploration und Steuerung von Strömungssimulationen auf Many-Core-Architekturen GPU-Mapping: Robotic Map Building with Graphical Multiprocessors An Embedded Automatic License Plate Recognition System Using Deep Learning A Practical Performance Analysis of Modern Software used in High Performance Computing GPU based accelerator for RankBoost in web search engines A general algorithm to automatically create admittance matrices for electric networks The application of shared-memory HPC in CFD Learning Quality Evaluation of MOOC Based on Big Data Analysis An Investigation of Parallel Road Map Inference from Big GPS Traces Data Parallel data processing with MapReduce SEV – a software toolbox for large scale analysis and visualization of polysomnography data Reducing Cluster Energy Consumption through Workload Management Effect of energy-saving server scheduling on power consumption for large-scale data centers Speeding Up Distributed MapReduce Applications Using Hardware Accelerators Compilers for Low Power with Design Patterns on Embedded Multicore Systems STRUCTURE MODIFIED APPLICATION BACKGROUND THROUGH BIG DATA AND HADOOP MAPREDUCE Meta-MapReduce for scalable data mining Distributed Local Outlier Factor with Locality-Sensitive Hashing Executing a biological sequence comparison application on a federated cloud environment A generate-test-aggregate parallel programming library: systematic parallel programming for MapReduce Optimized and Integrated Technique through Temporal Load Balancing Virtual Hadoop: MapReduce over Docker Containers with an Auto-Scaling Mechanism for Heterogeneous Environments The Tessera D&R computational environment: Designed experiments for R-Hadoop performance and Bitcoin analysis Divide and recombine (d&r) for the analysis of large and complex data sets with application to voip Benchmarking DataStax Enterprise/Cassandra with HiBench Research on Fault Diagnosis Based on Test Resource Virtualization KVFS: An HDFS Library over NoSQL Databases FPGA-Accelerated Hadoop Cluster for Deep Learning Computations Pro Hadoop MIPr - a framework for distributed image processing using Hadoop Design and implement of pre-loading SSD cache data using split file on Hadoop MapReduce A Hadoop-Galaxy adapter for user-friendly and scalable data-intensive bioinformatics in Galaxy
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