Project Suggestions

Contact

Please contact Eiko Yoneki (email: eiko.yoneki@cl.cam.ac.uk) if you are interested in any project below.

1.  Dynamic Task Scheduling on Heterogeneous CPU/GPU Environment using Machine Learning Technique for Parallel Processing

Originator/Supervisor: Eiko Yoneki  (with Anton Lokhmotov@dividiti.com)

Keywords: GPU Clusters, Heterogeneous many/multi-core, Parallel Computing, OpenCL, Task Scheduling

In this project, various aspects of parallel processing will be explored using a new generation of CPU/GPU integrated board, where more than one GPU clusters are placed on a chip. We use ARM based Mali-T628 MP6, in Exynos 5422 [1] [2], which has two clusters, of four (MP4) and two cores (MP2). Using OpenCL, tasks can be dispatched to GPU and CPU code in parallel. This new GPUs makes it possible to cluster the GPU nodes for different scale of parallel processing. GPUs offer a much higher hardware thread count and have access to higher memory bandwidth. We use a simulator on top of the hardware to experiment various task scheduling strategies explored by the machine learning methodologies for prediction of workload, vector instructions, and mixture of model parallelism and data parallelism.

Application running on top could be image analysis or irregular graph processing. Graph processing can take advantage of processor heterogeneity to adapt to structural data patterns. The overall aim of graph processing can be seen as scheduling irregular tasks to optimise data-parallel heterogeneous graph processing, by analysing the graph at runtime and dispatching graph elements to appropriate computation units. Efficient scheduling underlies the vision of a heterogeneous runtime platform for graph computation, where a data-centric scheduler is used to achieve optimal workload.

[1] http://www.anandtech.com/show/8234/arms-mali-midgard-architecture-explored

[2] www.samsung.com/global/business/semiconductor/product/application/detail?productId=7978&iaId=2341

2.  Clustering Entities across Multiple Documents in Massive Scale

Originator/Supervisor: Eiko Yoneki

Keywords: Clustering, Graph Partitioning, Random Walk, Distributed Algorithms

Many large-scale distributed problems including the optimal storage of large sets of graph structured data over several hosts - a key problem in today’s Cloud infrastructure. However, in very large-scale distributed scenarios, state-of-the-art algorithms are not directly applicable, because frequent global operations over the entire graph are difficult. In [1], balanced graph partitioning is achieved by a fully distributed an algorithm, called Ja-be-Ja, that uses local search and simulated annealing techniques for graph partitioning annealing techniques for graph partitioning. The algorithm is massively parallel: each node is processed independently, and only the direct neighbours of the nodes and a small subset of random nodes in the graph need to be known locally. Strict synchronisation is not required. These features allow Ja-be-Ja to be easily adapted to any distributed graph processing system. This project starts understanding Ja-be-Ja as a starting point, and investigates further performance improvement. A case study: a graph-based approach to coreference resolution, where a graph representation of the documents and their context is used and applying a community detection algorithm based in [1] can speed up the task of coreference resolution by a very large degree.

[1] Fatemeh Rahimian, Amir H. Payberah, Sarunas Girdzijauskas, Mark Jelasity and Seif Haridi: JabeJa: A Distributed Algorithm for Balanced Graph Partitioning, IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2013.

[2] Fatemeh Rahimian, Amir H. Payberah, Sarunas Girdzijauskas, and Seif Haridi: Distributed Vertex-Cut Partitioning, DAIS, 2014.

3. Approximate Algorithms Determining Local Clustering Coefficients Anonymously

Originator/Supervisor: Eiko Yoneki (with Amitabha Roy)

Keywords: Sampling, Approximation, Privacy, Cluster Coefficient

Anonymous social networks are a new phenomenon in an increasingly privacy conscious world. A natural question to ask in this setting is whether we can continue to apply known principles of network science in such settings where neighbourhood information is concealed both between nodes and to external observers. This project is to work on approximate algorithms that determines clustering coefficient in such settings. Clustering coefficients provide a way to relatively order nodes by importance in a network, determine densely connected neighbourhoods and distinguishing social graphs from web graphs. Algorithms to measure clustering coefficients have hitherto required global knowledge of graph links. This requires individual nodes in the graph to give up the identity of their neighbours. This project investigates an algorithm for estimating the clustering coefficient of a vertex by exchanging only anonymised set summaries of neighbours, from which it is difficult to reverse engineer individual links. The bulk of the work will consist of working on and improving sampling techniques to arrive at accurate local clustering coefficients without exchanging explicit neighbour lists in social networks.

[1] P. Flajolet, Eric Fusy, O. Gandouet, and et al. Hyperloglog: The analysis of a near-optimal cardinality estimation algorithm. In Proceedings of the International Conference on Analysis of Algorithms, 2007.

4. RasPi-Net: Building Stream Data Processing Platform over RasPiNET

Originator/Supervisor: Eiko Yoneki

Keywords: Raspberry Pi, Delay Tolerant Networks, Satellite Communication, Stream Processing

We have built a decentralised Raspberry Pi network (RasPiNET [1]), which can be deployed in wild and remote regions as a standalone network. The gateway Raspberry Pi nodes are integrated with satellite communication devices, where the light version of Delay Tolerant Network (DTN) bundle protocol is embedded. RasPiNET could consist of 10-100 nodes. As an example, a remote sensing application could be written either in RasPi or Smart phones that can connect to RasPi. Collected data could be processed within RasPiNET to reduce data size that streams over the satellite communication to the base location. The crowd sourcing application can run on top of RasPiNET, too. The goal of this project is building a stream processing platform in both directions: from data collection from RasPiNET nodes to the data processing nodes possibly via a satellite gateway and from bulk of data delivery to the satellite gateway node to disseminate necessary information to RasPiNET nodes. A good filtering function and RasPiNET in-network data aggregation could be developed.

References:

[1] E. Yoneki: RasPiNET: Decentralised Communication and Sensing Platform with Satellite Connectivity.  ACM CHANTS, 2014.

[2] Delay Tolerant Network Bundle Protocol: http://tools.ietf.org/html/rfc6255

[3] RockBlock Technology:http://rockblock.rock7mobile.com/

5. Graph Compression in the Semi-External Memory Environment

Originator/Supervisor: Eiko Yoneki (with Weixiong Rao)

Keywords: Graph Compression

This project explores graph compression mechanisms as part of a project looking into high performance semi-external memory graph processing (see [1] to get an idea of semi-external memory approaches). The graph compression work will build on an in-house graph representation format that we have developed that allows succinct representation of graphs that show hierarchical structures. The project will explore ways to improve the representation yielding smaller graphs on disk that are less costly to traverse. A key element of the representation scheme is a recursive graph partitioning step that minimises the number of edges between partitions. This is a rich space for exploration of suitable algorithms. We are concerned primarily with experimentally evaluating I/O costs on large graphs and measuring the compression performance. However a student with a theoretical background might consider designing algorithms with provable bounds on compression performance, which would be a big plus. If time allows you could also implement an efficient transformation tool based on the developed graph compression algorithm using parallel processing tools (e.g. map/reduce).

References:

[1] R. Pearce, M. Gokhale and N. Amato: Multithreaded Asynchronous Graph Traversal for In-Memory and Semi-External Memory, ACM/IEEE High Performance Computing, Networking, Storage and Analysis, 2010. http://dl.acm.org/citation.cfm?id=1884675

6. Develop Scale-Out SSD based Graph Traversal Platform

Originator/Supervisor: Eiko Yoneki

Keywords: Graph processing, I/O optimisation, Graph structure

This project contributes to our ongoing work on high performance semi-external memory graph processing (see [1]). We have developed an in-house prefetching algorithm for mining large graphs stored on Solid State Drives. A solid state drive provides the ability to service many random reads in parallel, provided that the requesting application is able to deliver requests at a rate that keeps the drive sufficiently busy. Thus pre-fetching multiple pages based on the current request in an intelligent manner can greatly improve performance. Our current pre-fetcher implementations are aligned with the graph traversal approach, (that is the graph iterator being employed).

Furthermore, the prefetcher implementation could be extended to utilise multiple storage devices in parallel. The aim in this setting would be to out-perform standard storage parallelisation techniques such as hardware or software RAID. The performance of this approach would also depend on the method of dividing graph data between available storage devices. Your task is developing scale-out version of our current work. You could also extend it in distributed graph traversals to study how a large graph (e.g. over 2 billion vertices) could get benefits from scale-out version, even though the current version can handle large graphs as a single node. You could also explore generating a large graph for testing by combining various entities of social network data (e.g. what happens when every "Like", "Comment" and "Photo" on Facebook are treated as vertices inside a graph -- not just "People" and "Pages" - such graphs would be huge).

References:

[1] E. Yoneki and A. Roy: Scale-up Graph Processing: A Storage-centric View.  ACM SIGMOD - GRADES, 2013.

[2] GraphLab: http://graphlab.org/

7. Building Graph Query Function using Functional Programming

Originator/Supervisor: Eiko Yoneki

Keywords: Graph, Functional programming

Demand to store and search of online data with graph structure is emerging. Such data range from online social networks to web links and it requires efficient query processing in a scalable manner. In this project, you will build a graph query function (layer) to achieve efficient graph data processing. The graph query function builds on a lazy graph loaded from multiple sources and performs queries at the same speed or faster than a relational database. The function should be written in Clojure or another functional language to support practical lazy loading from data source to an in-memory graph database. Efficient representations for graph queries should be also investigated. The evaluation includes comparisons with existing Graph Database and the query capability comparing with SQL. You can start the project by our existing work in this area.

References:

[1] Microsoft Research, Trinity project: Distributed graph database, http://research.microsoft.com/en-us/projects/trinity/

[2] Neo Technology, Java graph database, http://neo4j.org/

Contact Email

Please email to eiko.yoneki@cl.cam.ac.uk for any question.