Xiangke Liao

The TianHe-1A Supercomputer: Its Hardware and Software

This paper presents an overview of TianHe-1A (TH-1A) supercomputer, which is built by National University of Defense Technology of China (NUDT). TH-1A adopts a hybrid architecture by integrating CPUs and GPUs, and its interconnect network is a proprietary high-speed communication network. The theoretical peak performance of TH-1A is 4700 TFlops, and its LINPACK test result is 2566 TFlops. It was ranked the No. 1 on the TOP500 List released in November, 2010. TH-1A is now deployed in National Supercomputer Center in Tianjin and provides high performance computing services. TH-1A has played an important role in many applications, such as oil exploration, weather forecast, bio-medical research.

Sweep Coverage with Mobile Sensors

Many efforts have been made for addressing coverage problems in sensor networks. They fall into two categories, full coverage and barrier coverage, featured as static coverage. In this work, we study a new coverage scenario, sweep coverage, which differs with the previous static coverage. In sweep coverage, we only need to monitor certain points of interest (POIs) periodically so the coverage at each POI is time-variant, and thus we are able to utilize a small number of mobile sensors to achieve sweep coverage among a much larger number of POIs. We investigate the definitions and model for sweep coverage. Given a set of POIs and their sweep period requirements, we prove that determining the minimum number of required sensors (min-sensor sweep-coverage problem) is NP-hard, and it cannot be approximated within a factor of 2. We propose a centralized algorithm with constant approximation ratio 3 for the min-sensor sweep-coverage problem. We further characterize the nonlocality of the problem and design a distributed sweep algorithm, DSWEEP, cooperating sensors to provide efficiency with the best effort. We conduct extensive simulations to study the performance of the proposed algorithms. Our simulations show that DSWEEP outperforms the randomized scheme in both effectiveness and efficiency.

Barrier Coverage with Mobile Sensors

Barrier coverage, which guarantees that every movement crossing a barrier of sensors will be detected, is known to be an appropriate model of coverage for moving detection and boundary guard. The related problems about barrier coverage with stationary sensors are extensively studied. When sensors are randomly deployed, we require much more sensors to achieve barrier coverage than deterministic deployment. In this paper we study barrier coverage with mobile sensors, in which the sensors can be relocated after deployment, and we are able to utilize much fewer mobile sensors than stationary sensors to achieve barrier coverage with random deployment. We study the energy-efficient relocation problem for barrier coverage, and propose a centralized barrier algorithm, which computes the relocated positions based on knowing the initial positions of all sensors. For practicability and scalability, we further design a distributed barrier algorithm based on our proposed virtual force model. We conduct extensive simulations to study the effectiveness of the proposed algorithms.

Sweep coverage with mobile sensors

Many efforts have been made for addressing coverage problems in sensor networks. They fall into two categories, full coverage and barrier coverage, featured as static coverage. In this work, we study a new coverage scenario, sweep coverage, which differs with the previous static coverage. In sweep coverage, we only need to monitor certain points of interest (POIs) periodically so the coverage at each POI is time-variant, and thus we are able to utilize a small number of mobile sensors to achieve sweep coverage among a much larger number of POIs. We investigate the definitions and model for sweep coverage. Given a set of POIs and their sweep period requirements, we prove that determining the minimum number of required sensors (min-sensor sweep-coverage problem) is NP-hard, and it cannot be approximated within a factor of 2. We propose a centralized algorithm with constant approximation ratio 2 + epsi for the simplified problem where all sweep periods are identical. We further characterize the non-locality of the problem and design a distributed sweep algorithm, DSWEEP, cooperating sensors to provide required sweep requirements with the best effort. We conduct extensive simulations to study the performance of the proposed algorithms. Our simulations show that DSWEEP outperforms the randomized scheme in both effectiveness and efficiency.

MilkyWay-2 supercomputer: system and application

On June 17, 2013, MilkyWay-2 (Tianhe-2) supercomputer was crowned as the fastest supercomputer in the world on the 41th TOP500 list. This paper provides an overview of the MilkyWay-2 project and describes the design of hardware and software systems. The key architecture features of MilkyWay-2 are highlighted, including neo-heterogeneous compute nodes integrating commodity-off-the-shelf processors and accelerators that share similar instruction set architecture, powerful networks that employ proprietary interconnection chips to support the massively parallel message-passing communications, proprietary 16-core processor designed for scientific computing, efficient software stacks that provide high performance file system, emerging programming model for heterogeneous systems, and intelligent system administration. We perform extensive evaluation with wide-ranging applications from LINPACK and Graph500 benchmarks to massively parallel software deployed in the system.

Fine-grained boundary recognition in wireless ad hoc and sensor networks by topological methods

Location-free boundary recognition is crucial and critical for many fundamental network functionalities in wireless ad hoc and sensor networks. Previous designs, often coarse-grained, fail to accurately locate boundaries, especially when small holes exist. To address this issue, we propose a fine-grained boundary recognition approach using connectivity information only. This algorithm accurately discovers inner and outer boundary cycles without using location information. To the best of our knowledge, this is the first design being able to determinately locate all hole boundaries no matter how small the holes are. Also, this distributed algorithm does not rely on high node density. We formally prove the correctness of our design, and evaluate its effectiveness through extensive simulations.

IMGPU: GPU-Accelerated Influence Maximization in Large-Scale Social Networks

Influence Maximization aims to find the top-

Edge Self-Monitoring for Wireless Sensor Networks

(K)

Self-monitoring for sensor networks

influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Proven to be NP-hard, the influence maximization problem attracts tremendous studies. Though there exist basic greedy algorithms which may provide good approximation to optimal result, they mainly suffer from low computational efficiency and excessively long execution time, limiting the application to large-scale social networks. In this paper, we present IMGPU, a novel framework to accelerate the influence maximization by leveraging the parallel processing capability of graphics processing unit (GPU). We first improve the existing greedy algorithms and design a bottom-up traversal algorithm with GPU implementation, which contains inherent parallelism. To best fit the proposed influence maximization algorithm with the GPU architecture, we further develop an adaptive K-level combination method to maximize the parallelism and reorganize the influence graph to minimize the potential divergence. We carry out comprehensive experiments with both real-world and sythetic social network traces and demonstrate that with IMGPU framework, we are able to outperform the state-of-the-art influence maximization algorithm up to a factor of 60, and show potential to scale up to extraordinarily large-scale networks.

On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

Local monitoring is an effective mechanism for the security of wireless sensor networks (WSNs). Existing schemes assume the existence of sufficient number of active nodes to carry out monitoring operations. Such an assumption, however, is often difficult for a large-scale sensor network. In this work, we focus on designing an efficient scheme integrated with good self-monitoring capability as well as providing an infrastructure for various security protocols using local monitoring. To the best of our knowledge, we are the first to present the formal study on optimizing network topology for edge self-monitoring in WSNs. We show that the problem is NP-complete even under the unit disk graph (UDG) model and give the upper bound on the approximation ratio in various graph models. We provide polynomial-time approximation scheme (PTAS) algorithms for the problem in some specific graphs, for example, the monitoring-set-bounded graph. We further design two distributed polynomial algorithms with provable approximation ratio. Through comprehensive simulations, we evaluate the effectiveness of our design.