CCL: a portable and tunable collective communication library for scalable parallel computers

A collective communication library for parallel computers includes frequently used operations such as broadcast, reduce, scatter, gather, concatenate, synchronize, and shift. Such a library provides users with a convenient programming interface, efficient communication operations, and the advantage of portability. A library of this nature, the Collective Communication Library (CCL), intended for the line of scalable parallel computer products by IBM, has been designed. CCL is part of the parallel application programming interface of the recently announced IBM 9076 Scalable POWERparallel System 1 (SP1). In this paper, we examine several issues related to the functionality, correctness, and performance of a portable collective communication library while focusing on three novel aspects in the design and implementation of CCL: 1) the introduction of process groups, 2) the definition of semantics that ensures correctness, and 3) the design of new and tunable algorithms based on a realistic point-to-point communication model

Finding approximate palindromes in strings

We introduce a novel definition of approximate palindromes in strings, and provide an algorithm to find all maximal approximate palindromes in a string with up to $k$ errors. Our definition is based on the usual edit operations of approximate pattern matching, and the algorithm we give, for a string of size $n$ on a fixed alphabet, runs in $O(k^2 n)$ time. We also discuss two implementation-related improvements to the algorithm, and demonstrate their efficacy in practice by means of both experiments and an average-case analysis

EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE

Scaling Simulations of Reconfigurable Meshes.

This dissertation deals with reconfigurable bus-based models, a new type of parallel machine that uses dynamically alterable connections between processors to allow efficient communication and to perform fast computations. We focus this work on the Reconfigurable Mesh (R-Mesh), one of the most widely studied reconfigurable models. We study the ability of the R-Mesh to adapt an algorithm instance of an arbitrary size to run on a given smaller model size without significant loss of efficiency. A scaling simulation achieves this adaptation, and the simulation overhead expresses the efficiency of the simulation. We construct a scaling simulation for the Fusing-Restricted Reconfigurable Mesh (FR-Mesh), an important restriction of the R-Mesh. The overhead of this simulation depends only on the simulating machine size and not on the simulated machine size. The results of this scaling simulation extend to a variety of concurrent write rules and also translate to an improved scaling simulation of the R-Mesh itself. We present a bus linearization procedure that transforms an arbitrary non-linear bus configuration of an R-Mesh into an equivalent acyclic linear bus configuration implementable on an Linear Reconfigurable Mesh (LR-Mesh), a weaker version of the R-Mesh. This procedure gives the algorithm designer the liberty of using buses of arbitrary shape, while automatically translating the algorithm to run on a simpler platform. We illustrate our bus linearization method through two important applications. The first leads to a faster scaling simulation of the R-Mesh. The second application adapts algorithms designed for R-Meshes to run on models with pipelined optical buses. We also present a simulation of a Directional Reconfigurable Mesh (DR-Mesh) on an LR-Mesh. This simulation has a much better efficiency compared to previous work. In addition to the LR-Mesh, this simulation also runs on models that use pipelined optical buses

High-performance computing for vision

Vision is a challenging application for high-performance computing (HPC). Many vision tasks have stringent latency and throughput requirements. Further, the vision process has a heterogeneous computational profile. Low-level vision consists of structured computations, with regular data dependencies. The subsequent, higher level operations consist of symbolic computations with irregular data dependencies. Over the years, many approaches to high-speed vision have been pursued. VLSI hardware solutions such as ASIC's and digital signal processors (DSP's) have provided good processing speeds on structured low-level vision tasks. Special purpose systems for vision have also been designed. Currently, there is growing interest in using general purpose parallel systems for vision problems. These systems offer advantages of higher performance, sofavare programmability, generality, and architectural flexibility over the earlier approaches. The choice of low-cost commercial-off-theshelf (COTS) components as building blocks for these systems leads to easy upgradability and increased system life. The main focus of the paper is on effectively using the COTSbased general purpose parallel computing platforms to realize high-speed implementations of vision tasks. Due to the successful use of the COTS-based systems in a variety of high performance applications, it is attractive to consider their use for vision applications as well. However, the irregular data dependencies in vision tasks lead to large communication overheads in the HPC systems. At the University of Southern California, our research efforts have been directed toward designing scalable parallel algorithms for vision tasks on the HPC systems. In our approach, we use the message passing programming model to develop portable code. Our algorithms are specified using C and MPI. In this paper, we summarize our efforts, and illustrate our approach using several example vision tasks. To facilitate the analysis and development of scalable algorithms, a realistic computational model of the parallel system must be used. Several such models have been proposed in the literature. We use the General-purpose Distributed Memory (GDM) model which is a simple but realistic model of state-of-theart parallel machines. Using the GDM model, generic algorithmic techniques such as data remapping, overlapping of communication with computation, message packing, asynchronous execution, and communication scheduling are developed. Using these techniques, we have developed scalable algorithms for many vision tasks. For instance, a scalable algorithm for linear approximation has been developed using the asynchronous execution technique. Using this algorithm, linear feature extraction can be performed in 0.065 s on a 64 node SP-2 for a 512 × 512 image. A serial implementation takes 3.45 s for the same task. Similarly, the communication scheduling and decomposition techniques lead to a scalable algorithm for the line grouping task. We believe that such an algorithmic approach can result in the development of scalable and portable solutions for vision tasks. © 1996 IEEE Publisher Item Identifier S 0018-9219(96)04992-4.published_or_final_versio