The cost and complexity of hardware-centric systems can often be reduced by using software to perform tasks which don't appear on the critical path. Alternately, the performance of software can sometimes be improved by using special purpose hardware to implement tasks which do appear on the critical path. Whatever the motivation, most modern systems are composed of both hardware and software components.Given the importance of the connection between hardware and software in these systems, it is surprising how little automated and machine-checkable support there is for co-design space exploration. This paper presents the Connectal framework, which enables the development of hardware accelerators for software applications by generating hardware/software interface implementations from abstract Interface Design Language (IDL) specifications.Connectal generates stubs to support asynchronous remote method invocation from software to software, hardware to software, software to hardware, and hardware to hardware. For high-bandwidth communication, the Connectal framework provides comprehensive support for shared memory between hardware and software components, removing the repetitive work of processor bus interfacing from project tasks.This framework is released as open software under an MIT license, making it available for use in any projects.
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.
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