BOSS: Bandwidth-Optimized Search Accelerator for Storage-Class Memory

Projective analysis is an important solution in three-dimensional (3D) shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different viewpoints. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, and thus cannot satisfy the basic requirement of scalability for search engines. In the past three years, shape retrieval contest (SHREC) pays much attention to the scalability of 3D shape retrieval algorithms, and organizes several large scale tracks accordingly [1] – [3] . However, the experimental results indicate that conventional algorithms cannot be directly applied to large datasets. In this paper, we present a real-time 3D shape search engine based on the projective images of 3D shapes. The real-time property of our search engine results from the following aspects: 1) efficient projection and view feature extraction using GPU acceleration; 2) the first inverted file, called F-IF, is utilized to speed up the procedure of multiview matching; and 3) the second inverted file, which captures a local distribution of 3D shapes in the feature manifold, is adopted for efficient context-based reranking. As a result, for each query the retrieval task can be finished within one second despite the necessary cost of IO overhead. We name the proposed 3D shape search engine, which combines G PU acceleration and i nverted f ile ( t wice), as GIFT . Besides its high efficiency, GIFT also outperforms state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks (ModelNet40 dataset, ModelNet10 dataset, PSB dataset, McGill dataset) and competitions (SHREC14LSGTB, ShapeNet Core55, WM-SHREC07).

There are two inherent obstacles to effectively using Graphics Processing Units (GPUs) for query processing in search engines: (a) the highly restricted GPU memory space, and (b) the CPU-GPU transfer latency. Previously, Ao et al. presented a GPU method for lists intersection, an essential component in AND-based query processing. However, this work assumes the whole inverted index can be stored in GPU memory and does not address document ranking. In this paper, we describe and analyze a GPU query processing method which incorporates both lists intersection and top-k ranking. We introduce a parameterized pruned posting list GPU caching method where the parameter determines how much GPU memory is used for caching. This method allows list caching for large inverted indexes using the limited GPU memory, thereby making a qualitative improvement over previous work. We also give a mathematical model which can identify an approximately optimal choice of the parameter. Experimental results indicate that this GPU approach under the pruned list caching policy achieves better query throughput than its CPU counterpart, even when the inverted index size is much larger than the GPU memory space.

After analyzing inverted index structure and access mode of an open source search engine-ASPSeek,this paper gave an Abstract definition of "inverted index".In order to solve the difficulties of inverted index updating and the efficiency issues caused by directly accessing inverted index through file caching of operating system in ASPSeek,considering the characteristics of 1.25 million Chinese agricultural Web pages,this article proposed a new blocking inverted index storage scheme with a buffer mechanism which was based on CLOCK replacement algorithm.The experimental results show that the new scheme is more efficient than ASPSeek whether the buffer system is disabled or enabled.When the buffer system got enabled and 160 thousand Chinese terms or 50 thousand high-frequency Chinese terms were used as a test set,the retrieval time of new scheme tended to be a constant after one million accesses.Even when using entire 827 309 terms as a test set,the retrieval time of new scheme began to converge after two million accesses.

This study proposes a heterogeneous integration of precise and approximate storage in data center storage. The storage control engine allocates precise and error-tolerant applications to precise and approximate storage, respectively. The appropriate use of both precise and approximate storage is examined by applying a non-volatile memory capacity algorithm. To respond to the changes in application over time, the non-volatile memory capacity algorithm changes capacity of storage class memories (SCMs), namely the memory-type SCM (M-SCM) and storage-type SCM (S-SCM), in non-volatile memory resource. A three-dimensional triple-level cell (TLC) NAND flash is used as a large capacity memory. The results indicate that precise storage exhibits a high performance when the maximum storage cost is high. By contrast, with a low maximum storage cost, approximate storage exhibits high performance using a low bit cost approximate multiple-level cell (MLC) S-SCM.

The data resources provided by the European Bioinformatics Institute (EMBL-EBI) cover major areas of biological and biomedical research, giving free and open access to users ranging from expert to casual level. The EBI Search engine provides a unified metadata search engine across these resources. It provides a full-text search engine across over 6.5 billion data items, accessed through a user-friendly website and an OpenAPI-compliant programmatic interface. Here, we discuss recent developments and improvements in the service.

The medical imaging technology widely used in the medical domain, the massive medical images produced in the clinic has an important reference value to the medical scientific research, but for its large quantity and difficult to manage, the efficiency and accuracy in the retrieval are still insufficient. Lucene is architecture with a full-text search engine, providing the complete query and index engine. Several kinds of station search engine can be developed based on it. The architecture of medical image retrieval system is designed with ExtJS, Struts2, Spring, Hibernate and Lucene, including preprocessing, index establishment and resource retrieval. The retrieval efficiency of this system is high and the function is perfect.

Engineering a Web search engine offering effective and efficient information retrieval is a challenging task. Mitos is a recently developed search engine that offers a wide spectrum of functionalities. A rather unusual design choice is that its index is based on an object-relational database system, instead of the classical inverted file. This paper discusses the benefits and the drawbacks of this choice (compared to inverted files), proposes three different database representations, and reports comparative experimental results. Two of these representations are one order of magnitude more space economical and two orders of magnitude faster in query evaluation, than the plain relational representation.

Recent litigation and intense regulatory focus on secure retention of electronic records have spurred a rush to introduce Write-Once-Read-Many (WORM) storage devices for retaining business records such as electronic mail. However, simply storing records in WORM storage is insuffcient to ensure that the records are trustworthy, i.e., able to provide irrefutable proof and accurate details of past events. Specifically, some form of index is needed for timely access to the records, but unless the index is maintained securely, the records can in effect be hidden or altered, even if stored in WORM storage. In this paper, we systematically analyze the requirements for establishing a trustworthy inverted index to enable keyword-based search queries. We propose a novel scheme for effcient creation of such an index and demonstrate, through extensive simulations and experiments with an enterprise keyword search engine, that the scheme can achieve online update speeds while maintaining good query performance. In addition, we present a secure index structure for multi-keyword queries that supports insert, lookup and range queries in time logarithmic in the number of documents.

Processing queries in Web search engines demands the efficient use of hardware resources to cope with the scale and dynamics of user traffic. This paper focuses on the multithreaded processing of queries that requires (1) accessing a large inverted index data structure to obtain a set of documents, (2) rank them by executing the WAND operator in order to obtain the top K most pertinent documents for the query, and (3) resolve the insertion of new documents on the inverted index concurrently with the execution of queries. We propose an efficient strategy to assign threads to queries and index update operations which is suitable to support updates on the index concurrently with query processing. The core of our proposal is a simple classification technique devised to quickly assign threads to query operations.

Small embedded systems, such as using a 16-bit CPU, are required to connect to the Internet. In addition, the systems are required to support encrypted communication. We develop a ubiquitous platform which supports Internet Protocol version 6 (IPv6) and IP Security (IPSec). This paper describes realization of Advanced Encryption Standard (AES) processing controlled by an original compact CPU core for small embedded systems. AES encryption and decryption processing requires CPU capability. AES accelerators for a Field Programmable Gate Array (FPGA) perform high speed execution, furthermore they require the resources of an FPGA. In order to conserve the resources of the FPGA, the basic operations of AES, SubBytes, ShiftRows and MixColumns transformation, are incorporated in each hardware module. The modules are controlled by the original compact CPU core. Moreover, the CPU core is designed for small embedded systems. The CPU core is able to support only instructions used in application software. Using the CPU core, AES processing is accomplished conserving FPGA resources, and is simply designed.

Near-data processing is a promising paradigm to address the bandwidth, latency, and energy limitations in today's computer systems. In this work, we introduce an architecture that enhances a contemporary multi-core CPU with new features for supporting a seamless integration of near-data processing capabilities. Crucial aspects such as coherency, data placement, communication, address translation, and the programming model are discussed. The essential components, as well as a system simulator, are realized in hardware and software. Results for the important Graph500 benchmark show a 1.5x speedup when using the proposed architecture.

Collection Eye is the name of the collection management software developed in-house by EYE. It is a web-based application, optimized for Mozilla Firefox, which currently can only be consulted on-site at all the different locations of EYE and online via the EYE website. Collection Eye runs on an PostGresSQL database that stores the data, and uses Apache SOLR, a powerful open source search engine. This is a flexible full-text indexing search engine, that searches across all collections (the Google approach). This environment is easy to adjust: you can add index paths, develop queries, and adjust the ranking of search results. The interface is developed with PHP and ExtJS (Javascript). Configuration Tools are developed in McAccess. The metadata model that EYE implemented in Collection Eye is centered around the concept of Filmwork (Filmwerk). All different item types are linked to the concept of Filmwork. Examples of item types are posters, photos, filmcopy information (analog and digital), and film equipment. This is EYE’s translation of the Freebase entity-relationship model. The database model in the underlying PostGreSQL is structured around item types (entities) with their fields, item-type views (views configured on the item types), and index configuration (configuration of the search engine).

research-article Share on Heracles: a tool for fast RTL-based design space exploration of multicore processors Authors: Michel A. Kinsy Massachusetts Institute of Technology, Cambridge, MA, USA Massachusetts Institute of Technology, Cambridge, MA, USAView Profile , Michael Pellauer Intel Corporation, Hudson, MA, USA Intel Corporation, Hudson, MA, USAView Profile , Srinivas Devadas Massachusetts Institute of Technology, Cambridge, MA, USA Massachusetts Institute of Technology, Cambridge, MA, USAView Profile Authors Info & Claims FPGA '13: Proceedings of the ACM/SIGDA international symposium on Field programmable gate arraysFebruary 2013 Pages 125–134https://doi.org/10.1145/2435264.2435287Published:11 February 2013Publication History 26citation288DownloadsMetricsTotal Citations26Total Downloads288Last 12 Months19Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In today’s world, we have tremendous amount of data in the world of Web. This data is present in the various places or Web sites in the forms of hyperlink and URL. Web crawler is used as full text search engine which helps to navigate the Web text. They can also be used for the image/media search. So, in this work, we have developed a Web crawler which extracts data and takes care of the IP address of host and produces crawling results; all the top ranked results will be displayed to the user.

Two methods are currently used to organize and retrieve information on the Internet. Search engines like Google and AltaVista use a robot-based keyword searching method by constructing inverted index files for Web pages and matching users’ query terms with the index terms. The other method organizes human-selected Internet resources into a searchable database, and gives users structured hierarchical access to the database in a similar way to browsing through library classification schemes. We call this structured hierarchical system a Web directory. Knowledge structures, like a library classification schema or a Web directory, visualize and reflect what people know about things, and help people understand things better, identify gaps, recognize patterns, predict future trends, and so forth (Kwasnik, 2005). Moreover, Web directories offer quality control and give access only to selected Internet resources. All these advantages make the browsing structure based on subject classification a desirable complement to the search engine type service (Koch, Day, Brümmer, Hiom, Peereboom, Poulter, & Worsfold, 1997).