NDLI: Navigating big data with high-throughput, energy-efficient data partitioning.

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Navigating big data with high-throughput, energy-efficient data partitioning.

Content Provider	CiteSeerX
Author	Wu, Lisa Barker, Raymond J. Kim, Martha A. Ross, Kenneth A.
Abstract	The global pool of data is growing at 2.5 quintillion bytes per day, with 90 % of it produced in the last two years alone [24]. There is no doubt the era of big data has arrived. This paper explores targeted deployment of hardware accelerators to improve the throughput and energy efficiency of largescale data processing. In particular, data partitioning is a critical operation for manipulating large data sets. It is often the limiting factor in database performance and represents a significant fraction of the overall runtime of large data queries. To accelerate partitioning, this paper describes a hardware accelerator for range partitioning, or HARP, and a hardware-software data streaming framework. The streaming framework offers a seamless execution environment for streaming accelerators such as HARP. Together, HARP and the streaming framework provide an order of magnitude improvement in partitioning performance and energy. A detailed analysis of a 32nm physical design shows 7.8 times the throughput of a highly optimized and optimistic software implementation, while consuming just 6.9 % of the area and 4.3 % of the power of a single Xeon core in the same technology generation. Categories and Subject Descriptors C.3 [Special-purpose and application-based systems]: Microprocessor/microcomputer applications
File Format	PDF
Access Restriction	Open
Subject Keyword	Big Data Energy-efficient Data Partitioning Hardware Accelerator Application-based System Critical Operation Targeted Deployment Detailed Analysis Range Partitioning Large Data Set Physical Design Seamless Execution Environment Quintillion Byte Subject Descriptor Hardware-software Data Streaming Framework Database Performance Overall Runtime Microprocessor Microcomputer Application Technology Generation Magnitude Improvement Energy Efficiency Optimistic Software Implementation Large Data Query Largescale Data Processing Data Partitioning Global Pool Single Xeon Core Streaming Framework Significant Fraction
Content Type	Text
Resource Type	Article

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