Abstract A full‐duplex (FD) multiuser multiple‐input multiple‐output (MU‐MIMO) system, where a FD base station (BS) with multiple antennas serves multiple half‐duplex (HD) user equipments (UEs) in both uplink (UL) and downlink (DL) via the same time‐frequency resources, is considered. UE scheduling is in demand to manage the UL‐to‐DL interference (UDI) incurred by the FD operation. Existing scheduling algorithms require UDI channel state information between each pair of the candidate UEs, which incurs a significant amount of overhead as the number of UEs grows. To reduce the overhead, a new UE scheduling algorithm that uses channel reciprocity and UL‐DL duality to design a scheduling algorithm based on downlink performance was designed. By selecting UEs only based on their DL channels and received UDI strength, the proposed scheme no longer requires the massive UDI channel state information (CSI). Numerical results demonstrate the proposed algorithm can achieve a near optimal performance without knowing any UDI CSI.

In a wideband full‐duplex system, the residual self‐interference (SI) channel exhibits significant frequency selective fading characteristics after radio frequency SI cancellation. Resource allocation using the difference between the signal channel and the residual SI channel is a new method of suppressing residual SI and increasing system capacity. In this work, the residual SI channel and the user uplink channel are modelled as multi‐path frequency selective fading channels that satisfy the Rayleigh distribution. To improve system capacity, the authors propose to allocate the spectrum resource avoiding occupation of subcarriers with high residual SI, while guaranteeing a fair subcarrier assignment (SA) and power allocation (PA). A sum rate of uplink maximisation is formulated, and an algorithmic solution is developed to effectively conduct SA and PA. Numerical results demonstrate the effectiveness of the scheme in increasing system capacity and suppressing residual SI.

In this work we present the first algorithm for reconstructing multi-labeled material interfaces the allows for explicit topology control. Our algorithm takes in a set of 2D cross-sectional slices (not necessarily parallel), each partitioned by a curve network into labeled regions representing different material types. For each label, the user has the option to constrain the number of connected components and genus. Our algorithm is able to not only produce a material interface that interpolates the curve networks but also simultaneously satisfy the topological requirements. Our key innovation is defining a space of topology-varying material interfaces, which extends the family of level sets in a scalar function, and developing discrete methods for sampling distinct topologies in this space. Besides specifying topological constraints, the user can steer the algorithm interactively, such as by scribbling. We demonstrate, on synthetic and biological shapes, how our algorithm opens up new opportunities for topology-aware modeling in the multi-labeled context.

In this work we detail the first algorithm that provides topological control during surface reconstruction from an input set of planar cross-sections. Our work has broad application in a number of fields including surface modeling and biomedical image analysis, where surfaces of known topology must be recovered. Given curves on arbitrarily oriented cross-sections, our method produces a manifold interpolating surface that exactly matches a user-specified genus. The key insight behind our approach is to formulate the topological search as a divide-and-conquer optimization process which scores local sets of topologies and combines them to satisfy the global topology constraint. We further extend our method to allow image data to guide the topological search, achieving even better results than relying on the curves alone. By simultaneously satisfying both geometric and topological constraints, we are able to produce accurate reconstructions with fewer input cross-sections, hence reducing the manual time needed to extract the desired shape.

Abstract We present an interactive method for mesh segmentation that is inspired by the classical live‐wire interaction for image segmentation. The core contribution of the work is the definition and computation of wires on surfaces that are likely to lie at segment boundaries. We define wires as geodesics in a new tensor‐based anisotropic metric, which improves upon previous metrics in stability and feature‐awareness. We further introduce a simple but effective mesh embedding approach that allows geodesic paths in an anisotropic path to be computed efficiently using existing algorithms designed for Euclidean geodesics. Our tool is particularly suited for delineating segmentation boundaries that are aligned with features or curvature directions, and we demonstrate its use in creating artist‐guided segmentations.

Abstract We present an algorithm for obtaining a triangulation of multiple, non‐planar 3D polygons. The output minimizes additive weights, such as the total triangle areas or the total dihedral angles between adjacent triangles. Our algorithm generalizes a classical method for optimally triangulating a single polygon. The key novelty is a mechanism for avoiding non‐manifold outputs for two and more input polygons without compromising optimality. For better performance on real‐world data, we also propose an approximate solution by feeding the algorithm with a reduced set of triangles. In particular, we demonstrate experimentally that the triangles in the Delaunay tetrahedralization of the polygon vertices offer a reasonable trade off between performance and optimality.

Generating surfaces from 3D curve networks has been a longstanding problem in computer graphics. Recent attention to this area has resurfaced as a result of new sketch based modeling systems. In this work we present a new algorithm for finding cycles that bound surface patches. Unlike prior art in this area, the output of our technique is unrestricted, generating both manifold and non-manifold geometry with arbitrary genus. The novel insight behind our method is to formulate our problem as finding local mappings at the vertices and curves of our network, where each mapping describes how incident curves are grouped into cycles. This approach lends us the efficiency necessary to present our system in an interactive design modeler, whereby the user can adjust patch constraints and change the manifold properties of curves while the system automatically re-optimizes the solution.

MPI collective operations on multi-core clusters should be multi-core aware. In this paper, collective algorithms with hierarchical virtual topology focus on the performance difference among different communication levels on multi-core clusters, simply for intra-node and inter-node communication; Furthermore, to select befitting segment sizes for intra-node collective communication can cater to cache hierarchy in multi-core processors. Based on existing collective algorithms in MPICH2, above two techniques construct portable optimization methodology over MPICH2 for collective operations on multi-core clusters. Conforming to above optimization methodology, multi-core aware broadcast algorithm has been implemented and evaluated as a case study. The results of performance evaluation show that the multi-core aware optimization methodology over MPICH2 is efficient.

Cluster management software has faced more increased scalability challenge with ever enlarged cluster scale. Its good scalability rests with feasible design techniques focusing on hybrid software topologies with partitioning policy, non-blocking I/O multiplexing and message on demand. Design patterns are generic solutions to recurring software design problems, and above three important techniques are abstracted the design pattern of scalable cluster management software in this paper. According to this design pattern, some cluster management tools, such as job scheduling, MPI job launcher and so on, have been designed and applied on Dawning supercomputers. Some results of performance evaluation have shown that good scalability of cluster management software on Dawning supercomputers has benefited from this design pattern.

Grid system software is inherently complex, hard to build and maintain. In this paper, we propose a self- managing building block: Grid Unit, which facilitates constructing Grid system with higher availability and lower management overhead. We present an agent organization as autonomic management framework, and propose a self-recovering protocol to eliminate most of tough jobs from system administrator's routines. The system has been deployed on Dawning 4000A since 2004, the biggest node for China Grid system. We have done extensive experiments to evaluate Grid Unit, and the collected log data shows the availability of a Grid parallel process management service, built on the basis of Grid Unit, reaches 99.997%.

The design pattern of cluster system software has an important influence on scalability of massive cluster system. The paper presents design patterns of scalable cluster system software, including scalable software topologies and optimized communication modes. These design patterns have been widely applied in Dawning series of supercomputers and some results of performance evaluation show their good scalability