Botulinum toxin injected into the internal anal sphincter is used in the treatment of chronic anal fissure but there is no standardised technique for its administration. This randomised single centre trial compares bilateral (either side of fissure) to unilateral injection.

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 application. BlueCache is an attractive solution for both rack-level appliances and data-center-scale key-value cache.

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.

NAND flash is seeing increasing adoption in the data center because of its orders of magnitude lower latency and higher bandwidth compared to hard disks. However, flash performance is often degraded by (i) inefficient storage I/O stack that hides flash characteristics under Flash Translation Layer (FTL), and (ii) long latency network protocols for distributed storage.In this paper, we propose a minimalistic clustered flash array (minFlash). First, minFlash exposes a simple, stable, error-free, shared-memory flash interface that enables the host to perform cross-layer flash management optimizations in file systems, databases and other user applications. Second, minFlash uses a controller-to-controller network to connect multiple flash drives with very little overhead. We envision minFlash to be used within a rack cluster of servers to provide fast scalable distributed flash storage. We show through benchmarks that minFlash can access both local and remote flash devices with negligible latency overhead, and it can expose near theoretical max performance of the NAND chips in a distributed setting.

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.

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.

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.

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.

A method and apparatus are provided to permit location discovery, including location discovery in indoor settings. The method may identify a wireless fingerprint present at a geographical location and determine whether the wireless fingerprint corresponds to a previously observed wireless fingerprint associated with the predefined geographical location. A new wireless fingerprint and a geographical location, or bind, may be received by the system and the system may evaluate whether or not the new bind is erroneous or not, or if the new bind is an improvement over existing binds in determining whether or not to accept the new bind.

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.

We describe an indoor, room-level location discovery method based on spatial variations in “wifi signatures,” i.e., MAC addresses and signal strengths of existing wireless access points. The principal novelty of our system is its organic nature; it builds signal strength maps from the natural mobility and lightweight contributions of ordinary users, rather than dedicated effort by a team of site surveyors. Whenever a user’s personal device observes an unrecognized signature, a GUI solicits the user’s location. The resulting location-tagged signature or “bind” is then shared with other clients through a common database, enabling devices subsequently arriving there to discover location with no further user contribution. Realizing a working system deployment required three novel elements: (1) a human-computer interface for indicating location over intervals of varying duration; (2) a client-server protocol for pre-fetching signature data for use in localization; and (3) a location-estimation algorithm incorporating highly variable signature data. We describe an experimental deployment of our method in a nine-story building with more than 1,400 distinct spaces served by more than 200 wireless access points. At the conclusion of the deployment, users could correctly localize to within 10 meters 92% of the time.

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.

The mobile phone is rapidly becoming the primary personal gateway to access information. In the near future, it will likely become the primary means for accessing services such as personal banking, online purchasing, mobile entertainment, and multiplayer online games. It might also someday replace wallets full of credit cards, cash, and identification cards and become a personal monitoring device, personal trainer, or storage facility for electronic medical records. Enormous challenges exist in realizing this vision, which is why Nokia and the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory have started a joint research venture to refine the vision and develop the required devices, applications, and services. As part of this venture, Nokia formed the Nokia Research Center, Cambridge, which is within walking distance of CSAIL. Researchers are working on six joint research projects, and we believe that constant intermingling of researchers is necessary for deep research collaboration

StarT-ng is a joint MIT-Motorola project to build a high-performance message passing machine from commercial systems. Each site of the machine consists of a PowerPC 620-based Motorola symmetric multiprocessor (SMP) running the AIX 4.1 operating system. Every processor is connected to a low-latency, high-bandwidth network that is directly accessible from user-level code. In addition to fast message passing capabilities, the machine has experimental support for cachecoherent shared memory across sites. When the machine requires memory to be kept globally coherent, one processor on each site is devoted to supporting shared memory. When globally coherent shared memory is not required, that processor can be used for normal computation tasks. StarT-ng will be delivered at about the time the base SMP is introduced into the marketplace. The ability to be both a collection of standard SMP and an aggressive message passing machine with coherent shared memory makes StarT-ng a good building block for incrementally expandable parallel machines.

In semi-nomadic professions such as medicine, practitioners move through office buildings carrying personal computing and communication devices. These practitioners make use of fixed computing and communication resources in the rooms they visit. Organizations have strong financial incentives to help their mobile professionals find each other, communicate effectively, and make efficient use of the infrastructure. The challenge is to provide the desired functionality in a cost-effective manner without requiring special hardware on the mobile devices. Our solution consists of a standards-based voice, video, and instant messaging communication system integrated with a location tracking system. Our location tracking system is a low-cost scanning network that monitors the wireless network transmissions from the mobile devices and uses that information to identify and track the mobile clients. This paper describes (1) the architecture, implementation, and performance tradeoffs for a low-cost sensor network for live tracking of 802.11 wireless clients in an office building, and (2) the integration of the location tracking data with a standards-based SIP voice/video/presence communication infrastructure. This architecture requires no special client hardware or software. This system has been deployed and tested both in the laboratory and at two conferences.

Power and energy management is crucial for mobile devices that rely on battery power. In addition to voice recognition, image understanding is an important class of applications for mobile environments. We propose a new compilation strategy for remote task mapping, and report experimental results for a face detection and recognition system. Our compilation strategy generates two versions of the input program, one to be executed on the mobile device (client), and the other on a machine connected to the mobile device via a wireless network (server). Compiler supported checkpointing is used to allow the client to monitor program progress on the server, and to request checkpoint data in case of anticipated server and/or network failure. The reported results have been obtained by actual power measurements, not simulation. Experiments show energy savings of up to one order of magnitude on the mobile machine. A prototype implementation of the discussed compilation framework is underway, and preliminary results are reported.