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The digital world has given rise to massive quantities of data that include rich semantic and complex networks. A social graph, for example, containing hundreds of millions of actors and tens of billions of relationships is not uncommon. Analyzing these large data sets, even to answer simple analytic queries, often pushes the limits of algorithms and machine architectures. We present GraphCT, a scalable framework for graph analysis using parallel and multithreaded algorithms on shared memory platforms. Utilizing the unique characteristics of the Cray XMT, GraphCT enables fast network analysis at unprecedented scales on a variety of input data sets. On a synthetic power law graph with 2 billion vertices and 17 billion edges, we can find the connected components in 2 minutes. We can estimate the betweenness centrality of a similar graph with 537 million vertices and over 8 billion edges in under 1 hour. GraphCT is built for portability and performance. [ABSTRACT FROM AUTHOR]

The study of genomes has been revolutionized by sequencing machines that output many short overlapping substrings (called reads). The task of sequence assembly in practice is to reconstruct long contiguous genome subsequences from the reads. With Next Generation Sequencing (NGS) technologies, assembly software needs to be more accurate, faster, and more memory-efficient due to the problem complexity and the size of the data sets. In this paper, we develop parallel algorithms and compressed data structures to address several computational challenges of NGS assembly. We demonstrate how commonly available multicore architectures can be efficiently utilized for sequence assembly. In all stages (indexing input strings, string graph construction and simplification, extraction of contiguous subsequences) of our software Pasqual, we use shared-memory parallelism to speed up the assembly process. In our experiments with data of up to 6.8 billion base pairs, we demonstrate that Pasqual generally delivers the best tradeoff between speed, memory consumption, and solution quality. On synthetic and real data sets Pasqual scales well on our test machine with 40 CPU cores with increasing number of threads. Given enough cores, Pasqual is fastest in our comparison. [ABSTRACT FROM AUTHOR]

Reducing energy consumption has been an important design issue for large-scale streaming media storage systems. Existing energy conservation techniques are inadequate to achieve high energy efficiency for streaming media computing environments due to high data migration overhead. To address this problem, we propose in this paper a new energy-efficient method called Explicit Energy Saving Disk Cooling or EESDC. EESDC significantly reduces data migration overhead because of two reasons. First, a set of disks referred to Explicit Energy Saving Disks (EESD) is explicitly fixed according to temporal system load. Second, all the migrated data in EESDC directly contribute on extending the idle time of EESD to conserve more energy efficiently. Therefore, the EESDC method is conducive to saving more energy by quickly achieving energy-efficient data layouts without unnecessary data migrations. We implement EESDC in a simulated disk system, which is validated against a prototype system powered by our EESDC. Our experimental results using both real-world traces and synthetic traces show that EESDC can save up to 28.13-29.33 percent energy consumption for typical streaming media traces. Energy efficiency of streaming media storage systems can be improved by 3.3-6.0 times when EESDC is coupled. [ABSTRACT FROM AUTHOR]

A regeneration-theory approach is undertaken to analytically characterize the average overall completion time in a distributed system. The approach considers the heterogeneity in the processing rates of the nodes as well as the randomness in the delays imposed by the communication medium. The optimal one-shot load balancing policy is developed and subsequently extended to develop an autonomous and distributed load-balancing policy that can dynamically reallocate incoming external loads at each node. This adaptive and dynamic load balancing policy is implemented and evaluated in a two-node distributed system. The performance of the proposed dynamic load-balancing policy is compared to that of static policies as well as existing dynamic load-balancing policies by considering the average completion time per task and the system processing rate in the presence of random arrivals of the external loads. [ABSTRACT FROM AUTHOR]

The author reflects on the issues covered in the August 2006 issue of the journal. It describes high-performance computational biology as the development and application of high-performance computing techniques for extending the reach or scale of investigations in computational biology. It presents an outline of the topics discussed that includes the scalable implementation of the widely used BLAST search program for homology detection between a query sequence and a database of known sequences.

Presents the introductory editorial for this issue of the publication. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including the acceptance rate for research submissions, the visibility of "IEEE Transactions on Parallel and Distributed Systems" (TPDS) journal, and the value to the international community.

The article offers brief information on the first issue of IEEE Transactions on Parallel and Distributed Systems (TPDS) in 2016, which provides quality technical publications with increased visibility and relevance of TPDS.

The author discusses the January 2015 issue of the periodical, offers thanks to Manish Parashar for his role as the associate editor-in-chief of the journal and mentions the articles published by the journal that has highest quality research and is relevant to academia, industry and laboratories.

I am excited with my new role as incoming Editor-in-Chief (EiC) of IEEE Transactions on Parallel and Distributed Systems (TPDS) and look forward to serving the community over the next several years. As a brief introduction, I am a full professor at the Georgia Institute of Technology, and have a rich service record to the IEEE and the leading conferences and journals. One of my first official duties as incoming EIC is to recognize and thank Ivan Stojmen ovic for his dedication and service to TPDS. I'm inheriting a very healthy publication: As the former EIC, Ivan has been proactive to make TPDS one of the fastest growing among all of the transactions in the IEEE Computer and IEEE Communications Societies to accept and publish papers, within 36 weeks on average. TPDS was among the first of the IEEE transactions to adopt the OnlinePlus publication model, and the abstract booklet and disk are distributed on a quarterly basis to subscribers. My goals as EIC are to increase the visibility and relevance of TPDS. The IEEE is a hallmark of quality for technical publication. The value TPDS brings to the international community is in its collection of the highest quality research that is relevant to academia, industry, and laboratories. I will investigate new opportunities for TPDS to capture the best ideas and explore innovative ideas for partnerships with the leading parallel and distributed systems conferences in new mechanisms for publication. Last year, the scope of TPDS was updated to refl ect the latest and exciting developments in the area, such as manycore systems, network on chips, cloud computing, social networks, wireless networks, and cyber-physical systems. TPDS will continue to modify its scope to refl ect the state of the art in the research areas of parallel and distributed systems. I expect a very active discussion of the TPDS editorial board as we continue to modernize the scope by incorporating 'hot topic' areas. [ABSTRACT FROM AUTHOR]