Heterogeneous Interconnection Network Research Articles

Human Microbe-Disease Association Prediction by a Novel Double-Ended Random Walk with Restart.

Microorganisms in the human body play a vital role in metabolism, immune defense, nutrient absorption, cancer control, and prevention of pathogen colonization. More and more biological and clinical studies have shown that the imbalance of microbial communities is closely related to the occurrence and development of various complex human diseases. Finding potential microbial-disease associations is critical for understanding the pathology of a few diseases and thus further improving disease diagnosis and prognosis. In this study, we proposed a novel computational model to predict disease-associated microbes. Specifically, we first constructed a heterogeneous interconnection network based on known microbe-disease associations deposited in a few databases, the similarity between diseases, and the similarity between microorganisms. We then predicted novel microbe-disease associations by a new method called the double-ended restart random walk model (DRWHMDA) implemented on the interconnection network. In addition, we performed case studies of colon cancer and asthma for further evaluation. The results indicate that 10 and 9 of the top 10 microorganisms predicted to be associated with colorectal cancer and asthma were validated by relevant literatures, respectively. Our method is expected to be effective in identifying disease-related microorganisms and will help to reveal the relationship between microorganisms and complex human diseases.

Influence maximization across heterogeneous interconnected networks based on deep learning

With the fast development of online social networks, a large number of their members are involved in more than one social network. Finding most influential users is one of the interesting social network analysis tasks. The influence maximization (IM) problem aims to select a minimum set of users who maximize the influence spread on the underlying network. Most of the previous researches only focus on a single social networks, whereas in real world, users join to multiple social networks. Thus, influence can spread through common users on multiple networks. Besides, the existing works including simulation based, proxy based and sketch based approaches suffer from different issues including scalability, efficiency and feasibility due to the nature of these approaches for exploring networks and computation of their influence diffusion. Moreover, in the previous algorithms, several heuristics are employed to capture network topology for IM. But, these methods have information loss during network exploration because of their pruning strategies.In this paper, a new research direction is presented for studying IM problem on interconnected networks. The proposed approach employs deep learning techniques to learn the feature vectors of network nodes while preserving both local and global structural information. To the best of our knowledge, network embedding has not yet been used to solve IM problem. Indeed, our algorithm leverages deep learning techniques for feature engineering to extract all the appropriate information related to IM problem for single and interconnected networks. Moreover, we prove that the proposed algorithm is monotone and submodular, thus, an optimal solution is guaranteed by the proposed approach. The experimental results on two interconnected networks including DBLP and Twitter-Foursquare illustrate the efficiency of the proposed algorithm in comparison to state of the art IM algorithms. We also conduct some experiments on NetHept dataset to evaluate the performance of the proposed approach on single networks.

Robustness in clustering-based weighted inter-connected networks

We study the robustness of symmetrically coupled and clustering-based weighted heterogeneous inter-connected networks with respect to load-failure-induced cascades. This is done under the assumption that the flow dynamics are governed by global redistribution of loads based on weighted betweenness centrality. Our results indicate that no weighting bias should be assigned to inter-links when calculating shortest path between node pairs under the clustering-based weighting scheme; i.e., inter-links shall be treated no differently than intra-links. In contrast with local load redistribution cases, we show that increasing connectivity is preferred for the robustness against global load redistribution-based cascading failures in clustering-based weighted inter-connected networks. Furthermore, comparisons among weighting schemes reveal that, both the clustering-based and degree-based schemes outperform the random one in the sense of requiring lower initial and total investments required to ensure robustness. We also find that clustering-based scheme outperforms degree-based one in terms of requiring lower initial investments. Except in a limited range where weighting is heavily suppressed, clustering-based scheme is shown to outperform degree-based one in terms of total investments. Finally, when there exists a hard investment budget constraint, clustering-based weighting scheme would be a better choice against a two-nodes-induced failure than the degree-based weighting, and the clustering-based scheme is more stable than degree-based scheme against one-or-two-nodes-induced failure. We expect our findings to be significantly useful in designing real-world weighted inter-connected networks that are robust against load-failure-induced cascades.

Exploiting address compression and heterogeneous interconnects for efficient message management in tiled CMPs

High performance processor designs have evolved toward architectures that integrate multiple processing cores on the same chip. As the number of cores inside a Chip MultiProcessor (CMP) increases, the interconnection network will have significant impact on both overall performance and energy consumption as previous studies have shown. Moreover, wires used in such interconnect can be designed with varying latency, bandwidth and power characteristics. In this work, we show how messages can be efficiently managed in tiled CMP, from the point of view of both performance and energy, by combining both address compression with a heterogeneous interconnect. In particular, our proposal is based on applying an address compression scheme that dynamically compresses the addresses within coherence messages allowing for a significant area slack. The arising area is exploited for wire latency improvement by using a heterogeneous interconnection network comprised of a small set of very-low-latency wires for critical short-messages in addition to baseline wires. Detailed simulations of a 16-core CMP show that our proposal obtains average improvements of 10% in execution time and 38% in the energy-delay^2 product of the interconnect. Additionally, the sensitivity analysis shows that our proposal performs well when either OoO cores or caches with higher latencies are considered.

Heterogeneous Interconnects for Energy-Efficient Message Management in CMPs

Continuous improvements in integration scale have made major microprocessor vendors to move to designs that integrate several processing cores on the same chip. Chip multiprocessors (CMPs) constitute a good alternative to traditional monolithic designs for several reasons, among others, better levels of performance, scalability, and performance/energy ratio. On the other hand, higher clock frequencies and the increasing transistor density have revealed power dissipation and temperature as critical design issues in current and future architectures. Previous studies have shown that the interconnection network of a Chip Multiprocessor (CMP) has significant impact on both overall performance and energy consumption. Moreover, wires used in such interconnect can be designed with varying latency, bandwidth, and power characteristics. In this work, we show how messages can be efficiently managed, from the point of view of both performance and energy, in tiled CMPs using a heterogeneous interconnect. Our proposal consists of two approaches. The first is Reply Partitioning, a technique that splits replies with data into a short Partial Reply message that carries a subblock of the cache line that includes the word requested by the processor plus an Ordinary Reply with the full cache line. This technique allows all messages used to ensure coherence between the L1 caches of a CMP to be classified into two groups: critical and short, and noncritical and long. The second approach is the use of a heterogeneous interconnection network composed of low-latency wires for critical messages and low-energy wires for noncritical ones. Detailed simulations of 8 and 16-core CMPs show that our proposal obtains average savings of 7 percent in execution time and 70 percent in the Energy-Delay squared Product (ED2P) metric of the interconnect over previous works (from 24 to 30 percent average ED2P improvement for the full CMP). Additionally, the sensitivity analysis shows that although the execution time is minimized for subblocks of 16 bytes, the best choice from the point of view of the ED2P metric is the 4-byte subblock configuration with an additional improvement of 2 percent over the 16-byte one for the ED2P metric of the full CMP.

Heuristics for scheduling file-sharing tasks on heterogeneous systems with distributed repositories

We consider the problem of scheduling an application on a computing system consisting of heterogeneous processors and data repositories. The application consists of a large number of file-sharing otherwise independent tasks. The files initially reside on the repositories. The processors and the repositories are connected through a heterogeneous interconnection network. Our aim is to assign the tasks to the processors, to schedule the file transfers from the repositories, and to schedule the executions of tasks on each processor in such a way that the turnaround time is minimized. We propose a heuristic composed of three phases: initial task assignment, task assignment refinement, and execution ordering. We experimentally compare the proposed heuristics with three well-known heuristics on a large number of problem instances. The proposed heuristic runs considerably faster than the existing heuristics and obtains 10–14% better turnaround times than the best of the three existing heuristics.

A platform for heterogeneous interconnection network management

A comprehensive network management function built into the interconnection network provides a means of meeting user needs of fault tolerance, performance, accounting, and security. However, meeting these needs on a heterogeneous interconnection network is several levels more complex than on a homogeneous network because of the existence of multiple protocol stacks for communication. An architecture that will solve this problem by analyzing the heterogeneous interconnection network as a single entity is proposed. The authors present the design issues evaluated in choosing this approach and establish the need for the various components in this architecture. An overview is presented of each of the components, and their place in the architecture is pointed out. >