With the development of desktop virtualization technology, more and more companies begins to deploy virtual desktop. Traditional NAS and SAN storages can only support small-scale users due to network bandwidth limit a...
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Replica management has become a hot research topic in storage systems. This paper presents a dynamic replica management strategy based on response time, named RTRM. RTRM strategy consists of replica creation, replica ...
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CS (Conflict Serializability) is a recently proposed relaxer correctness criterion that can increase transactional memory's parallelism. DDA (Distributed Dependency-Aware) model is currently proposed to implement ...
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With Graphics Processing Units (GPUs) becoming more and more popular in general purpose computing, more attentions have been paid on building a framework to provide convenient interfaces for GPU programming. MapReduce...
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ISBN:
(纸本)9781450323697
With Graphics Processing Units (GPUs) becoming more and more popular in general purpose computing, more attentions have been paid on building a framework to provide convenient interfaces for GPU programming. MapReduce can greatly simplify the programming for data-parallel applications in cloud computing environment, and it is also naturally suitable for GPUs. However, there are some problems in recent reduction-based MapReduce implementation on GPUs. Its performance is dramatically degraded when handling massive distinct keys because the massive data cannot be stored in tiny shared memory entirely. A new MapReduce framework on GPUs, called Jupiter, is proposed with continuous reduction structure. Two improvements are supported in Jupiter, a multi-level reduction scheme tailored for GPU memory hierarchy and a frequencybased cache policy on key-value pairs in shared memory. Shared memories are utilized efficiently for various dataparallel applications whether involving little or abundant distinct keys. Experiments show that Jupiter can achieve up to 3x speedup over the original reduction-based GPU MapReduce framework on the applications with lots of distinct keys. Categories and Subject Descriptors D.1.3 [Programming Techniques]: Concurrent Programming- parallel programming General Terms Algorithms, Performance, Design.
Online social networks are rapidly becoming popular for users to share, organize and locate interesting content. Users pay much attention to their close friends, those direct or two-hop friends. Users of Facebook comm...
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Delay Tolerant Networks (DTN) is one of the mobile wireless networks that topology logic may change frequently. Variable topology logic characteristics lead to several low efficiency routing problems, and in which how...
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As an import trend of cyberspace in the future, cloud computing has attracted much attention from the IT industry. Many research institutions and companies have launched their own cloud platforms, which have virtual m...
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Cloud computing contains many enterprise applications that require from each user to perform authenticate at first step. Then, he will gain a permit from the service provider to access resources at second step. The is...
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Web service technologies have been widely used in diverse applications. However, there are still many security challenges in reliability, confidentiality and data nonrepudiation, which are prominent especially in some...
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Recently the flexibility of RDF data model makes increasing number of organizations and communities keep their data available in the RDF format. There is a growing need for querying these data in scalable and efficien...
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