Simple test for quantum channel capacity

Basing on states and channels isomorphism we point out that semidefinite programming can be used as a quick test for nonzero one-way quantum channel capacity. This can be achieved by search of symmetric extensions of states isomorphic to a given quantum channel. With this method we provide examples of quantum channels that can lead to high entanglement transmission but still have zero one-way capacity, in particular, regions of symmetric extendibility for isotropic states in arbitrary dimensions are presented. Further we derive {\it a new entanglement parameter} based on (normalised) relative entropy distance to the set of states that have symmetric extensions and show explicitly the symmetric extension of isotropic states being the nearest to singlets in the set of symmetrically extendible states. The suitable regularisation of the parameter provides a new upper bound on one-way distillable entanglement.Comment: 6 pages, no figures, RevTeX4. Signifficantly corrected version. Claim on continuity of channel capacities removed due to flaw in the corresponding proof. Changes and corrections performed in the part proposing a new upper bound on one-way distillable etanglement which happens to be not one-way entanglement monoton

Performance comparison of point and spatial access methods

In the past few years a large number of multidimensional point access methods, also called multiattribute index structures, has been suggested, all of them claiming good performance. Since no performance comparison of these structures under arbitrary (strongly correlated nonuniform, short "ugly") data distributions and under various types of queries has been performed, database researchers and designers were hesitant to use any of these new point access methods. As shown in a recent paper, such point access methods are not only important in traditional database applications. In new applications such as CAD/CIM and geographic or environmental information systems, access methods for spatial objects are needed. As recently shown such access methods are based on point access methods in terms of functionality and performance. Our performance comparison naturally consists of two parts. In part I we w i l l compare multidimensional point access methods, whereas in part I I spatial access methods for rectangles will be compared. In part I we present a survey and classification of existing point access methods. Then we carefully select the following four methods for implementation and performance comparison under seven different data files (distributions) and various types of queries: the 2-level grid file, the BANG file, the hB-tree and a new scheme, called the BUDDY hash tree. We were surprised to see one method to be the clear winner which was the BUDDY hash tree. It exhibits an at least 20 % better average performance than its competitors and is robust under ugly data and queries. In part I I we compare spatial access methods for rectangles. After presenting a survey and classification of existing spatial access methods we carefully selected the following four methods for implementation and performance comparison under six different data files (distributions) and various types of queries: the R-tree, the BANG file, PLOP hashing and the BUDDY hash tree. The result presented two winners: the BANG file and the BUDDY hash tree. This comparison is a first step towards a standardized testbed or benchmark. We offer our data and query files to each designer of a new point or spatial access method such that he can run his implementation in our testbed

A Robust Scheme for Multilevel Extendible Hashing

Dynamic hashing, while surpassing other access methods for uniformly distributed data, usually performs badly for non-uniformly distributed data. We propose a robust scheme for multi-level extendible hashing allowing efficient processing of skewed data as well as uniformly distributed data. In order to test our access method we implemented it and compared it to several existing hashing schemes. The results of the experimental evaluation demonstrate the superiority of our approach in both index size and performance

Parallelizing Windowed Stream Joins in a Shared-Nothing Cluster

The availability of large number of processing nodes in a parallel and distributed computing environment enables sophisticated real time processing over high speed data streams, as required by many emerging applications. Sliding window stream joins are among the most important operators in a stream processing system. In this paper, we consider the issue of parallelizing a sliding window stream join operator over a shared nothing cluster. We propose a framework, based on fixed or predefined communication pattern, to distribute the join processing loads over the shared-nothing cluster. We consider various overheads while scaling over a large number of nodes, and propose solution methodologies to cope with the issues. We implement the algorithm over a cluster using a message passing system, and present the experimental results showing the effectiveness of the join processing algorithm.Comment: 11 page

A Survey of Hashing Techniques for High Performance Computing

Hashing is a well-known and widely used technique for providing O(1) access to large files on secondary storage and tables in memory. Hashing techniques were introduced in the early 60s. The term hash function historically is used to denote a function that compresses a string of arbitrary input to a string of fixed length. Hashing finds applications in other fields such as fuzzy matching, error checking, authentication, cryptography, and networking. Hashing techniques have found application to provide faster access in routing tables, with the increase in the size of the routing tables. More recently, hashing has found applications in transactional memory in hardware. Motivated by these newly emerged applications of hashing, in this paper we present a survey of hashing techniques starting from traditional hashing methods with greater emphasis on the recent developments. We provide a brief explanation on hardware hashing and a brief introduction to transactional memory

Taking the Shortcut: Actively Incorporating the Virtual Memory Index of the OS to Hardware-Accelerate Database Indexing

Index structures often materialize one or multiple levels of explicit indirections (aka pointers) to allow for a quick traversal to the data of interest. Unfortunately, dereferencing a pointer to go from one level to the other is costly since additionally to following the address, it involves two address translations from virtual memory to physical memory under the hood. In the worst case, such an address translation is resolved by an index access itself, namely by a lookup into the page table, a central hardware-accelerated index structure of the OS. However, if the page table is anyways constantly queried, it raises the question whether we can actively incorporate it into our database indexes and make it work for us. Precisely, instead of materializing indirections in form of pointers, we propose to express these indirections directly in the page table wherever possible. By introducing such shortcuts, we (a) effectively reduce the height of traversal during lookups and (b) exploit the hardware-acceleration of lookups in the page table. In this work, we analyze the strengths and considerations of this approach and showcase its effectiveness at the case of the real-world indexing scheme extendible hashing

Node.js based Document Store for Web Crawling

WARC files are central to internet preservation projects. They contain the raw resources of web crawled data and can be used to create windows into the past of web pages at the time they were accessed. Yet there are few tools that manipulate WARC files outside of basic parsing. The creation of our tool WARC-KIT gives users in the Node.js JavaScript environment, a tool kit to interact with and manipulate WARC files. Included with WARC-KIT is a WARC parsing tool known as WARCFilter that can be used standalone tool to parse, filter, and create new WARC files. WARCFilter can also, create CDX index files on the WARC files, parse existing CDX files, or even generate webgraph datasets for graph analysis algorithms. Aside from WARCFilter, WARC-KIT includes a custom on disk database system implemented with an underlying Linear Hash Table data structure. The database system is the first of its kind as a JavaScript only on disk document store. The overall main application of WARC-KIT is that it allows users to create custom indices upon collections of WARC files. After creating an index on a WARC collections, users are then query their collection using the GraphQL query language to retrieve desired WARC records. Experiments with WARCFilter on a WARC dataset composed of 238,000 WARC records demonstrates that utilizing CDX index files speeds WARC record filtering around ten to twenty times faster than raw WARC parsing. Database timing tests with the JavaScript Linear Hash Table database system displayed twice as fast insertion and retrieval operations than a similar Rust implemented Linear Hash Table database. Experiments with the overall WARC-KIT application on the same 238,000 WARC record dataset exhibited consistent query times for different complex queries