Large interconnected systems consist of a multitude of subsystems with their own dynamics, but coupled with each other via input-output connections. Each subsystem is typically modelled by ordinary differential equati...
详细信息
Today, many commercial and private cloud computing providers offer resources for leasing under the infrastructure as a service (IaaS) paradigm. Although an abundance of mechanisms already facilitate the lease and use ...
详细信息
Similarity search has been widely studied in the last years, as it can be applied to several fields such as searching by content in multimedia objects, text retrieval or computational biology. These applications usual...
详细信息
Similarity search has been widely studied in the last years, as it can be applied to several fields such as searching by content in multimedia objects, text retrieval or computational biology. These applications usually work on very large databases that are often indexed off-line to enable the acceleration of on-line searches. However, to maintain an acceptable throughput, it is essential to exploit the intrinsic parallelism of the algorithms used for the on-line query solving process, even with indexed databases. Therefore, many strategies have been proposed in the literature to parallelize these algorithms, both on shared and distributed memory multiprocessor systems. Lately, GPUs have also been used to implement brute-force approaches instead of using indexing structures, due to the difficulties introduced by the index in the efficient exploitation of the GPU resources. In this work we propose a Multi-GPU metric-space technique that efficiently exploits index data structures for similarity search in large databases, and show how it outperforms previous OpenMP and GPU brute-force strategies. Furthermore, our analysis covers the effects of the database size and its nature.
We develop methods for accelerating metric similarity search that are effective on modern hardware. Our algorithms factor into easily parallelizable components, making them simple to deploy and efficient on multicore ...
详细信息
We develop methods for accelerating metric similarity search that are effective on modern hardware. Our algorithms factor into easily parallelizable components, making them simple to deploy and efficient on multicore CPUs and GPUs. Despite the simple structure of our algorithms, their search performance is provably sub linear in the size of the database, with a factor dependent only on its intrinsic dimensionality. We demonstrate that our methods provide substantial speedups on a range of datasets and hardware platforms. In particular, we present results on a 48-core server machine, on graphics hardware, and on a multicore desktop.
We study a basic information ranking problem in networks where each node holds an individual preference over a set of items and the goal for each node is to identify a sorted list of items with the largest aggregate p...
详细信息
ISBN:
(纸本)9781467325790
We study a basic information ranking problem in networks where each node holds an individual preference over a set of items and the goal for each node is to identify a sorted list of items with the largest aggregate preference. We would like to achieve this with a fully decentralized algorithm that uses a limited per-node memory and limited pair-wise communications. We show how this problem can be reduced to a plurality selection problem where the goal for each node is to identify an item with the largest aggregate ranking score, and show that solving the reduced problem solves the original ranking problem with high probability. Then we introduce a simple and natural plurality selection algorithm for the selection over m >; 1 items that uses only log 2 (m) + 1 bits of per-node memory and per pair-wise communication. We prove correctness of the algorithm with high probability as the number of nodes grows large for the case when each node communicates with any other node, and establish tight convergence time bounds. The information ranking problem studied in this paper is a basic ranking problem that arises in various applications such as sorting elements in distributed computing systems, paralleldatabases, and may as well serve as a model of decentralized inference and opinion formation in distributed environments.
In present scenario parallel database systems are being applicable in a broad range of systems, right from database applications (OLTP) server to decision support systems (OLAP) server. These developments involve data...
详细信息
In present scenario parallel database systems are being applicable in a broad range of systems, right from database applications (OLTP) server to decision support systems (OLAP) server. These developments involve database processing and querying over parallelsystems. A means to the success of parallel database systems, particularly in decision-support applications (Data warehousing), is parallel query optimization. Given a SQL query, parallel query optimization has the goal of finding a parallel plan that delivers the query result in minimal time. Various useful and competent, optimizing solutions to be implemented For the paralleldatabases. parallel DBS attempt to develop recently in order to make highperformance and high-availability database servers at a much lower price for multiprocessor computer architectures than mainframe computers. The objective of this paper is define a novel approach on how to achieve parallelism for relational database multithreaded query execution use to maximum resource utilization of CPU and memory. This technique offer a solution to the problem of minimizing the response time of input queries against paralleldatabases.
In this paper, we propose a new architecture for parallel and distributed processing framework, gJobcasth, which enables data processing on a cloud style KVS database. Nowadays, lots of KVS (as known as Key Value Stor...
详细信息
In this paper, we propose a new architecture for parallel and distributed processing framework, gJobcasth, which enables data processing on a cloud style KVS database. Nowadays, lots of KVS (as known as Key Value Store) systems exist which achieve high scalability for data spaces among a huge number of computers. Some of KVS implementations use gconsistent hashh algorithm to identify the backend data node to store a pair of key and value. Jobcast also uses consistent hash algorithm for a distribution strategy and has a capability to store key and value pairs into huge number of computers as a KVS system. Furthermore, Jobcast also distributes "jobs" into data nodes for parallel and distributed processing. In this paper, we introduce a basic architecture of Jobcast and evaluate a data processing performance for a typical example.
As the foundation of cloud computing, Server consolidation allows multiple computer infrastructures running as virtual machines in a single physical node. It improves the utilization of most kinds of resource but memo...
详细信息
The future power grid is expected to further expand with highly distributed energy sources and smart loads. The increased size and complexity lead to increased burden on existing computational resources in energy cont...
详细信息
ISBN:
(纸本)9781467309745
The future power grid is expected to further expand with highly distributed energy sources and smart loads. The increased size and complexity lead to increased burden on existing computational resources in energy control centers. Thus the need to perform real-time assessment on such systems entails efficient means to distribute centralized functions such as state estimation in the power system. In this paper, we present our experience of prototyping a system architecture that connects distributed state estimators individually running parallel programs to solve non-linear estimation procedure. Through our experience, we highlight the needs of integrating the distributed state estimation algorithm with efficient partition and data communication tools so that distributed state estimation has low overhead compared to the centralized solution. We build a test case based on the IEEE 118 bus system and partition the state estimation of the whole system model to available HPC clusters. The measurement from the test bed demonstrates the low overhead of our solution.
暂无评论