BlueDBM

Abstract

Complex data queries, because of their need for random accesses, have proven to be slow unless all the data can be accommodated in DRAM. There are many domains, such as genomics, geological data and daily twitter feeds where the datasets of interest are 5TB to 20 TB. For such a dataset, one would need a cluster with 100 servers, each with 128GB to 256GBs of DRAM, to accommodate all the data in DRAM. On the other hand, such datasets could be stored easily in the flash memory of a rack-sized cluster. Flash storage has much better random access performance than hard disks, which makes it desirable for analytics workloads. In this paper we present BlueDBM, a new system architecture which has flashbased storage with in-store processing capability and a lowlatency high-throughput inter-controller network. We show that BlueDBM outperforms a flash-based system without these features by a factor of 10 for some important applications. While the performance of a ram-cloud system falls sharply even if only 5%~10% of the references are to the secondary storage, this sharp performance degradation is not an issue in BlueDBM. BlueDBM presents an attractive point in the cost-performance trade-off for Big Data analytics.

Accelerating In-Memory Database Selections Using Latency Masking Hardware Threads

“…Embedding reconfigurable hardware in storage devices is a growing area of interest [6,33,43,65]. For instance, Ibex [79] is a MySQL accelerator platform where a SATA SSD is coupled with an FPGA.…”

Dynamic Multi-Resolution Data Storage

“…Even though VS shares the benefits from recent advances in ISP [6,9,14,23,31,33,35,40,67,75,76,82,85,89,96] and neardata processing [2,17,20,39,48,50,63,80,84], these frameworks need the mechanisms that VS offers in order to execute approximate computing applications efficiently. And while using approximate computing in channel encoding [38,62] and memory controller [30] can achieve an effect similar to that of VS in terms of reducing data-movement overhead, VS is independent of these projects and requires no changes in hardware.…”

Summarizer

“…Therefore, the system design can implement most architectural supports that Summarizer needs through updating the rmware. On the other hand, Active Disks Meets Flash [7], Ibex [41] or BlueDBM [15] leverages re-congurable hardware or specialized processor architectures to achieve the same goal, limiting the exibility of applications but increasing the cost of devices.…”

“…As modern solid state drives (SSDs) for data centers integrate large DRAM buers as well as multiple general-purpose embedded cores that are often under-utilized, moving computation closer to data storage becomes feasible. Previous work demonstrated the potential of using near-data processing models to ooad data analytics, SQL queries, operating system functions, graph traversal, image processing, and MapReduce operations [4,5,8,15,16,30,32,34,36,41] to data storage devices. Many of these prior works assume the presence of a commodity general purpose processor near storage.…”