16,427 research outputs found
Parallel Graph Partitioning for Complex Networks
Processing large complex networks like social networks or web graphs has
recently attracted considerable interest. In order to do this in parallel, we
need to partition them into pieces of about equal size. Unfortunately, previous
parallel graph partitioners originally developed for more regular mesh-like
networks do not work well for these networks. This paper addresses this problem
by parallelizing and adapting the label propagation technique originally
developed for graph clustering. By introducing size constraints, label
propagation becomes applicable for both the coarsening and the refinement phase
of multilevel graph partitioning. We obtain very high quality by applying a
highly parallel evolutionary algorithm to the coarsened graph. The resulting
system is both more scalable and achieves higher quality than state-of-the-art
systems like ParMetis or PT-Scotch. For large complex networks the performance
differences are very big. For example, our algorithm can partition a web graph
with 3.3 billion edges in less than sixteen seconds using 512 cores of a high
performance cluster while producing a high quality partition -- none of the
competing systems can handle this graph on our system.Comment: Review article. Parallelization of our previous approach
arXiv:1402.328
Automated Protein Structure Classification: A Survey
Classification of proteins based on their structure provides a valuable
resource for studying protein structure, function and evolutionary
relationships. With the rapidly increasing number of known protein structures,
manual and semi-automatic classification is becoming ever more difficult and
prohibitively slow. Therefore, there is a growing need for automated, accurate
and efficient classification methods to generate classification databases or
increase the speed and accuracy of semi-automatic techniques. Recognizing this
need, several automated classification methods have been developed. In this
survey, we overview recent developments in this area. We classify different
methods based on their characteristics and compare their methodology, accuracy
and efficiency. We then present a few open problems and explain future
directions.Comment: 14 pages, Technical Report CSRG-589, University of Toront
LogBase: A Scalable Log-structured Database System in the Cloud
Numerous applications such as financial transactions (e.g., stock trading)
are write-heavy in nature. The shift from reads to writes in web applications
has also been accelerating in recent years. Write-ahead-logging is a common
approach for providing recovery capability while improving performance in most
storage systems. However, the separation of log and application data incurs
write overheads observed in write-heavy environments and hence adversely
affects the write throughput and recovery time in the system. In this paper, we
introduce LogBase - a scalable log-structured database system that adopts
log-only storage for removing the write bottleneck and supporting fast system
recovery. LogBase is designed to be dynamically deployed on commodity clusters
to take advantage of elastic scaling property of cloud environments. LogBase
provides in-memory multiversion indexes for supporting efficient access to data
maintained in the log. LogBase also supports transactions that bundle read and
write operations spanning across multiple records. We implemented the proposed
system and compared it with HBase and a disk-based log-structured
record-oriented system modeled after RAMCloud. The experimental results show
that LogBase is able to provide sustained write throughput, efficient data
access out of the cache, and effective system recovery.Comment: VLDB201
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
- …