A key-value store (KVS), such as memcached and Redis, is widely used as a caching layer to augment the slower persistent backend storage in data centers. DRAM-based KVS provides fast key-value access, but its scalability is limited by the cost, power and space needed by the machine cluster to support a large amount of DRAM. This paper offers a 10X to 100X cheaper solution based on flash storage and hardware accelerators. In BlueCache key-value pairs are stored in flash storage and all KVS operations, including the flash controller are directly implemented in hardware. Furthermore, BlueCache includes a fast interconnect between flash controllers to provide a scalable solution. We show that BlueCache has 4.18X higher throughput and consumes 25X less power than a flash-backed KVS software implementation on x86 servers. We further show that BlueCache can outperform DRAM-based KVS when the latter has more than 7.4% misses for a read-intensive aplication. BlueCache is an attractive solution for both rack-level appliances and data-center-scale key-value cache.

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 20TB. For such a dataset, one would need a cluster with 100 servers, each with 128GB to 256GB 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. However, currently available off-the-shelf flash storage packaged as SSDs does not make effective use of flash storage because it incurs a great amount of additional overhead during flash device management and network access. In this article, we present BlueDBM, a new system architecture that has flash-based storage with in-store processing capability and a low-latency high-throughput intercontroller network between storage devices. 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 DRAM-centric system falls sharply even if only 5% to 10% of the references are to secondary storage, this sharp performance degradation is not an issue in BlueDBM. BlueDBM presents an attractive point in the cost/performance tradeoff for Big Data analytics.

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.

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 flash-based storage with in-store processing capability and a low-latency 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.

Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.

H.264, a state-of-the-art video compression standard, is used across a range of products from cellphones to HDTV. These products have vastly different performance, power and cost requirements, necessitating different hardware-software solutions for H.264 decoding. We show that a design methodology and associated tools which support synthesis from high-level descriptions and which allow modular refinement throughout the design cycle, can share the majority of design effort across multiple design points. Using Bluespec SystemVerilog, we have created a variety of designs for the H.264 decoder tuned to support decoding at resolutions ranging from QCIF video (176 times 144 @ 15 frames/second) to 1080p video ((1280 times 1080)p @60 frames/second) in a 180 nm process. Some of these design points require major transformations of pipelining to increase performance or to reduce area. We also explore several common design issues surrounding memory structures, such as caches and on-chip vs. off-chip memories. We believe the design methodology used in this paper is directly applicable to many IP blocks involving algorithmic specifications. The same design capabilities also permit rapid microarchitecture exploration and changes in RTL late in the design process even in non-algorithmic IP blocks.

Orthogonal frequency-division multiplexing (OFDM) has become the preferred modulation scheme for both broadband and high bitrate digital wireless protocols because of its spectral efficiency and robustness against multipath interference. Although the components and overall structure of different OFDM protocols are functionally similar, the characteristics of the environment for which a wireless protocol is designed often result in different instantiations of various components. In this paper, we describe how we can instantiate baseband processoring of two different wireless protocols, namely 802.11a and 802.16 in Bluespec from a highly parameterized code for a generic OFDM protocol. Our approach results in highly reusable IP blocks that can dramatically reduce the time-to-market of new OFDM protocols. One advantage of Bluespec over SystemC is that our code is synthesizable into high quality hardware, which we demonstrate via synthesis results. Using a Viterbi decoder we also demonstrate how parameterization can be used to study area-performance tradeoff in the implementation of a module. Furthermore, parameterized modules and modular composition can facilitate implementation-grounded algorithmic exploration in the design of new protocols.