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

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.

Q100

“…The Q100 implements range partitioner, which splits the space of keys into contiguous ranges. We chose this because it is tolerant of irregular data distributions [39] and produces ordered partitions, making it a suitable precursor to the sorter.…”

“…Multiple instances of a streaming framework, such as the one described in recent work [39], could feed the Q100 assuming 5 GB/s per stream. At that rate, the Q100 would require 4-6 inbound stream buffers depending on the configuration and 2 outbound stream buffers, reflecting the read/write imbalance noted earlier.…”

An application-specific instruction set for accelerating set-oriented database primitives

“…Wu et al [37] proposed the HARP accelerator for speeding up partitioning of numerical values, which is heavily used in database systems. The key idea of the approach is to build specialized streaming cores that solely partition numeric sequences besides the regular cores used for processing.…”

A framework of service-oriented operation model of China׳s power system

“…Due to the rapid development of cloud computing [4][5][6], big data analytics [7][8][9], and other emerging information technologies (ICTs) [10][11][12], as well as their increasing penetration in energy systems, traditional power system is developing toward integration, digitization, automation, and personalization.…”