Efficient and Scalable Listing of Four-Vertex Subgraph

Identifying four-vertex subgraphs has long been recognized as a fundamental technique in bioinformatics and social networks. However, listing these structures is a challenging task, especially for graphs that do not fit in RAM. To address this problem, we build a set of algorithms, models, and implementations that can handle massive graphs on commodity hardware. Our technique achieves 4 – 5 orders of magnitude speedup compared to the best prior methods on graphs with billions of edges, with external-memory operation equally efficient

Efficient and Scalable Listing of Four-Vertex Subgraph

Identifying four-vertex subgraphs has long been recognized as a fundamental technique in bioinformatics and social networks. However, listing these structures is a challenging task, especially for graphs that do not fit in RAM. To address this problem, we build a set of algorithms, models, and implementations that can handle massive graphs on commodity hardware. Our technique achieves 4 – 5 orders of magnitude speedup compared to the best prior methods on graphs with billions of edges, with external-memory operation equally efficient

High Performance Large Graph Analytics by Enhancing Locality

Graphs are widely used in a variety of domains for representing entities and their relationship to each other. Graph analytics helps to understand, detect, extract and visualize insightful relationships between different entities. Graph analytics has a wide range of applications in various domains including computational biology, commerce, intelligence, health care and transportation. The breadth of problems that require large graph analytics is growing rapidly resulting in a need for fast and efficient graph processing. One of the major challenges in graph processing is poor locality of reference. Locality of reference refers to the phenomenon of frequently accessing the same memory location or adjacent memory locations. Applications with poor data locality reduce the effectiveness of the cache memory. They result in large number of cache misses, requiring access to high latency main memory. Therefore, it is essential to have good locality for good performance. Most graph processing applications have highly random memory access patterns. Coupled with the current large sizes of the graphs, they result in poor cache utilization. Additionally, the computation to data access ratio in many graph processing applications is very low, making it difficult to cover the memory latency using computation. It is also challenging to efficiently parallelize most graph applications. Many graphs in real world have unbalanced degree distribution. It is difficult to achieve a balanced workload for such graphs. The parallelism in graph applications is generally fine-grained in nature. This calls for efficient synchronization and communication between the processing units. Techniques for enhancing locality have been well studied in the context of regular applications like linear algebra. Those techniques are in most cases not applicable to the graph problems. In this dissertation, we propose two techniques for enhancing locality in graph algorithms: access transformation and task-set reduction. Access transformation can be applied to algorithms to improve the spatial locality by changing the random access pattern to sequential access. It is applicable to iterative algorithms that process random vertices/edges in each iteration. The task-set reduction technique can be applied to enhance the temporal locality. It is applicable to algorithms which repeatedly access the same data to perform certain task. Using the two techniques, we propose novel algorithms for three graph problems: k-core decomposition, maximal clique enumeration and triangle listing. We have implemented the algorithms. The results show that these algorithms provide significant improvement in performance and also scale well

Play Among Books

How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an “infinite flow” of real books

Supply Chain Disruption Costs Study in International Containerised Maritime Transportation

The global economy relies highly on international trade, and the international maritime transport system acts as the lifeblood carrying and transporting materials and goods globally, realizing the economy globalization in an effective and efficient way. However, globalization increases the interdependence and complexity of global supply chains and drives it to be more vulnerable to disruptions. Meanwhile, the international marine transport system is a complex and intertwined system exposed to high risks and decreased safety due to its very accessibility and operational flexibility. Thereby, global supply chains integrated with international maritime transportation systems are inherently vulnerable to various disruptions. Studies of supply chain disruptions particularly quantifying transport related disruption costs are becoming increasingly important. However, research on maritime transport related supply chain disruptions, in particular, quantifying its disruption costs is under-represented in the transport literature, due largely to the features of supply chain disruptions, but also because of the complexity of maritime related supply chains. Current research in transportation has tended to concentrate on shippers’ transport mode choice and port selection. In the context of a global market, however, the behaviour of maritime containerised shippers has to be viewed as a complex decision and an integral element of the supply chain management strategy. Those shippers’ transportation choice decisions should be emphasized and studied to reveal their behaviour changes between normal operations and disruption circumstance. This research adds to the paucity work on investigating the maritime transport related supply chain disruptions and quantifying its disruption costs based on shippers’ maritime transportation choice behaviour. It presents the results of a microanalysis of freight transport choice decisions in an international containerised maritime transport chain context. The Latent Class Model (LCM) is applied to identify the key service attributes and its preference heterogeneity in maritime transportation and to estimate the marginal values for the quality of maritime transport service with and without a disruption, simultaneously, quantifying the disruption costs through comparing each attribute’s marginal value difference between normal and disruption operations. The Seemingly Unrelated Regression model (SURE) is utilized to explore the sources influencing shippers’ preference heterogeneities. In doing so, we are able to gain an understanding as to where and how much should be invested in order to facilitate recovery in the case of a disruption based on the view of the maritime participants’ perspectives. The research results confirm freight rate, transit time, reliability, damage rate, and frequency as the key service attributes influencing shippers’ transport choice. They also reveal shippers’ VOT increase by more than four-times, VOR nearly double, and VOD increase about twenty percent if a disruption takes place, and identify shippers’ transport decisions vary with its product, shipment, company and supply chain characteristics no matter with or without a disruption. This research quantifies the costs of supply chain disruption in containerised maritime transport context for the first time, and its results provide useful industrial implications for maritime transport chain related parties

Supply Chain Disruption Costs Study in International Containerised Maritime Transportation

The global economy relies highly on international trade, and the international maritime transport system acts as the lifeblood carrying and transporting materials and goods globally, realizing the economy globalization in an effective and efficient way. However, globalization increases the interdependence and complexity of global supply chains and drives it to be more vulnerable to disruptions. Meanwhile, the international marine transport system is a complex and intertwined system exposed to high risks and decreased safety due to its very accessibility and operational flexibility. Thereby, global supply chains integrated with international maritime transportation systems are inherently vulnerable to various disruptions. Studies of supply chain disruptions particularly quantifying transport related disruption costs are becoming increasingly important. However, research on maritime transport related supply chain disruptions, in particular, quantifying its disruption costs is under-represented in the transport literature, due largely to the features of supply chain disruptions, but also because of the complexity of maritime related supply chains. Current research in transportation has tended to concentrate on shippers’ transport mode choice and port selection. In the context of a global market, however, the behaviour of maritime containerised shippers has to be viewed as a complex decision and an integral element of the supply chain management strategy. Those shippers’ transportation choice decisions should be emphasized and studied to reveal their behaviour changes between normal operations and disruption circumstance. This research adds to the paucity work on investigating the maritime transport related supply chain disruptions and quantifying its disruption costs based on shippers’ maritime transportation choice behaviour. It presents the results of a microanalysis of freight transport choice decisions in an international containerised maritime transport chain context. The Latent Class Model (LCM) is applied to identify the key service attributes and its preference heterogeneity in maritime transportation and to estimate the marginal values for the quality of maritime transport service with and without a disruption, simultaneously, quantifying the disruption costs through comparing each attribute’s marginal value difference between normal and disruption operations. The Seemingly Unrelated Regression model (SURE) is utilized to explore the sources influencing shippers’ preference heterogeneities. In doing so, we are able to gain an understanding as to where and how much should be invested in order to facilitate recovery in the case of a disruption based on the view of the maritime participants’ perspectives. The research results confirm freight rate, transit time, reliability, damage rate, and frequency as the key service attributes influencing shippers’ transport choice. They also reveal shippers’ VOT increase by more than four-times, VOR nearly double, and VOD increase about twenty percent if a disruption takes place, and identify shippers’ transport decisions vary with its product, shipment, company and supply chain characteristics no matter with or without a disruption. This research quantifies the costs of supply chain disruption in containerised maritime transport context for the first time, and its results provide useful industrial implications for maritime transport chain related parties

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

ICR ANNUAL REPORT 2022 (Volume 29)[All Pages]

This Annual Report covers from 1 January to 31 December 202

Travelling knowledges: urban poverty and slum/shack dwellers international

The relationship between knowledge and development is of growing importance in development theory and practice. Despite the growth in interest, there are significant issues that have not been explored in detail. I will focus on some of these issues, including: the ways in which knowledge and learning are conceived and created in development; the ways in which knowledge travels; the opportunities for learning between 'North' and 'South'; and the political spaces that are created through different kinds of knowledge. To explore these issues, I examine a network of non-governmental organisations (NGOs) and community-based organisations (CBOs) called Slum/Shack Dwellers International (SDI). This network seeks to reconfigure the governance of urban poverty reduction strategies and encourage poor' people to re-think their own capacities and potentials. In particular, I draw on interview-based fieldwork conducted on one key member of this group, the Indian Alliance based in Mumbai. I critically examine some of the possibilities and challenges of various forms of 'travelling knowledges'. These are strategies that have travelled through exchanges, wherein groups of poor people travel from one settlement to another to share stories and experiences with other poor people in what amounts to an informal 'training' process. By examining exchanges between SDI and groups in the UK, I critically discuss the broader potential in development to move beyond barriers of North and South that limit learning. I adopt a broadly post-rationalist approach to the concerns in the thesis. Through this, I argue the importance of considering knowledge and learning as produced through relations of near and far, social and material, and as driven by routines and practices. A post-rationalist approach helps us to understand and appreciate the importance of geography for knowledge and learning in the SDI network. This approach draws attention to power. It encourages a critical consciousness that is alert to the kinds of knowledge conceived for development, and that recognizes the various ways in which different knowledges help create different types of politics. A post-rationalist approach also cautions against conceptions of knowledge and learning that risk marginalizing geography and power in development more generally. The thesis demonstrates the need to give further consideration of how knowledge is conceived as a development strategy, and what the potential possibilities and pitfalls of travelling knowledges are