As we are approaching the exascale era in supercomputing, designing a balanced computer system with powerful computing ability and low energy consumption becomes increasingly important. GPU is a widely used accelerato...
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ISBN:
(纸本)9781509032068
As we are approaching the exascale era in supercomputing, designing a balanced computer system with powerful computing ability and low energy consumption becomes increasingly important. GPU is a widely used accelerator in most recently applied supercomputers. It adopts massive multithreads to hide long latency and has high energy efficiency. In contrast to its strong computing power, GPUs have few on-chip resources with several MB of fast on-chip memory storage per SM (Streaming Multiprocessors). GPU caches exhibit poor efficiency due to the mismatch of the throughput-oriented execution model and its cache hierarchy design. Since the severe deficiency in on-chip memory, the benefit of high computing capacity of GPUs is pulled down by the poor cache performance dramatically, which limits system performance and energy-efficiency. In this paper, we put forward a locality protected scheme to make full use of the data locality based on the fixed capacity. We present a Locality Protected method based on instruction PC (LPP) to promote GPU performance. Firstly, we use a PC-based collector to collect the reuse information of each cache line. After getting the dynamic reuse information of the cache line, we take an intelligent cache allocation unit (ICAU) which coordinates the reuse information with LRU (Least Recently Used) replacement policy to find out the cache line with the least locality for eviction. The results show that LPP provides an up to 17.8% speedup and an average of 5.5% improvement over the baseline method.
Regenerating codes have been proposed to achieve an optimal trade-off curve between the amount of storage space and the network traffic for repair. However, existing repair schemes based on regenerating codes are inad...
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Moving objects detection is important in traffic video analysis, and many algorithms are being increasingly applied to moving objects detection. Most of these algorithms are time-consuming and cannot satisfy real-time...
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Bloom filters are frequently used to perform set queries that test the existence of some items. However, Bloom filters face a dilemma: the transmission bandwidth and the accuracy cannot be optimized simultaneously. Th...
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Currently, the performance problems of software systems gets more and more attentions. Among various diagnosis methods based on system traces, principal component analysis (PCA) based methods are widely used due to th...
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Functional programming languages have a long history and receive more and more attention today. The paper focuses on the development of functional languages and aims to introduce the concepts, such as higher-order fun...
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This paper investigates the problem of maximizing uniform multicast throughput (MUMT) for multi-channel dense wireless sensor networks, where all nodes locate within one-hop transmission range and can communicate with...
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ISBN:
(纸本)9781509056972
This paper investigates the problem of maximizing uniform multicast throughput (MUMT) for multi-channel dense wireless sensor networks, where all nodes locate within one-hop transmission range and can communicate with each other on multiple orthogonal channels. This kind of networks show wide application in the real world, and maximizing uniform multicast throughput for these networks is worth deep studying. Previous researches have proved MUMT problem is NP-hard. However, previous researches are either hard to implement, or use too many relay nodes to complete the multicast task, and thus incur high overhead or poor performance. To efficiently solve MUMT problem, we adopt the concept of the maximum independent set with the size constraint, and present one novel Single-Broadcast based Multicast algorithm called SBM based on the concept. We prove that SBM algorithm achieves a constant ratio to the theoretical throughput upper bound. Extensive experimental results demonstrate that, SBM performs better than existing work in terms of both the uniform multicast throughput and the total number of transmissions.
The coupling of microwaves into apertures plays an important part in many electromagnetic physics and engineering fields. When the width of apertures is very small, Finite Difference Time Domain (FDTD) simulation of t...
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Stragglers can temporize jobs and reduce cluster efficiency *** researches have been contributed to the solution,such as Blacklist[8],speculative execution[1,6],Dolly[8].In this paper,we put forward a new approach for...
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Stragglers can temporize jobs and reduce cluster efficiency *** researches have been contributed to the solution,such as Blacklist[8],speculative execution[1,6],Dolly[8].In this paper,we put forward a new approach for mitigating stragglers in Map Reduce,name *** starts task clones only for high-risk delaying *** experiments have been carried and results show that it can decrease the job delaying risk with fewer resources *** small jobs,Hummer also improves job completion time by 48% and 10% compared to LATE and Dolly.
Nowadays GPS embedded in mobile device such as smartphones can easily identify people's physical locations. However, in daily life people are more concerned about semantic locations (such as dormitories, laborator...
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ISBN:
(纸本)9781467372121
Nowadays GPS embedded in mobile device such as smartphones can easily identify people's physical locations. However, in daily life people are more concerned about semantic locations (such as dormitories, laboratories, shopping malls, etc.). Usually GPS positioning uses continuous sampling method, which results in a lot of semantically independent sample points. We call these points outliers. How to remove outliers from GPS data and thereby cluster meaningful semantic places is a research challenge in current field of pervasive computing. Aiming at the characteristics of this problem, we first propose a novel approach to add semantic annotations to newly discovered places every day. We use an unsupervised method to discover semantic places, which ensures accuracy of the results and reduces the amount of calculation. Secondly, we discuss the concept of outliers in GPS data collected in daily life, and then eliminate outliers using a density-based method. Moreover, we perform experiments to validate its effectiveness. Thirdly by taking advantage of rule-based inference and reverse geocoding we proposed an approach to calculate the probable semantic labels, which can help user annotate places and reduce the burden on users. Finally, we develop a local System Annotating Semantic Label of Location(SASLL) and by carrying out experiments we demonstrate the validity of our research.
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