Titlebook: Emerging Technology and Architecture for Big-data Analytics; Anupam Chattopadhyay,Chip Hong Chang,Hao Yu Book 2017 Springer International

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Scaling the Java Virtual Machine on a Many-Core Systemugh a NUMA aware garbage collector. In this chapter, we use the LArge Memory Business Data Analytics (LAMBDA) workload to illustrate the importance of various scaling bottlenecks and to demonstrate the performance gain from the discussed solutions.

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New Solutions for Cross-Layer System-Level and High-Level Synthesisoop: the definitive guide. O’Reilly Media, Sebastopol, 2009) have become primary datacenter applications, but the rise of massive data processing also has a major impact on the increasing demand for both datacenter computation and data processing in edge devices to improve scalability of massive sensing applications.

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OWL

Side Channel Attacks and Their Low Overhead Countermeasures on Residue Number System Multipliersance, and are compatible to other countermeasures on both the logic level and the algorithm level. We prototype the proposed design on FPGA, and presented the implementation results confirm the efficiency of the proposed countermeasures.

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Acceleration of MapReduce Framework on a Multicore Processoral results show that the MapReduce framework with hardware accelerators speeds up by 40 times at maximum compared to the pure software solution, and the proposed Topo-MapReduce speeds up further by 29% at maximum compared to the original MapReduce.

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ign abstraction and flow, from device, to circuits and syste.This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design a

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