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.

Aladdin: A pre-RTL, power-performance accelerator simulator enabling large design space exploration of customized architectures

“…We perform rigorous validation of Aladdin against handwritten RTL implementations and a commercial HLS design flow. We show that Aladdin can model the behavior of recently published accelerators [38,55,19] and typical accelerator kernels [17] (Section 4). 3.…”

“…Hardware acceleration exists in many forms, such as analog accelerators [6,50], static [13,19,28,38,43,52,55] and dynamic datapath accelerators [14,25,27], and programmable accelerators, such as GPUs and DSPs. In this work, we focus on static datapath accelerators.…”

“…We also validate Aladdin against handwritten RTL for benchmarks ill-suited for HLS. Examples are taken from recently published accelerator research: NPU [19], Memcached [38], and HARP [55]. 4.2.1.…”

“…HARP is a partitioning accelerator for big data [55]. Essentially, given a stream of inputs, it uses a pipeline of comparators to check each input against a splitter value at each stage and categorize the inputs.…”

“…Starting with an unconstrained program DDDG, which corresponds to an initial representation of accelerator hardware, Aladdin applies optimizations as well as constraints to the graph to create a realistic model of accelerator activity. We rigorously validated Aladdin against RTL implementations of accelerators from both handwritten Verilog and a commercial HLS tool for a range of applications, including accelerators in Memcached [38], HARP [55], NPU [19], and a commonly used throughput-oriented benchmark suite, SHOC [17]. Our results show that Aladdin can model performance within 0.9%, power within 4.9%, and area within 6.6% compared to accelerator designs generated by traditional RTL flows.…”