Pozna Supercomputing and Networking Center

Poland

Pozna Supercomputing and Networking Center

Poland
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Kats P.,Europeana Foundation | Mielnicki M.,Pozna Supercomputing and Networking Center | Knoth P.,Open University Milton Keynes | Muhr M.,European Library | And 2 more authors.
Proceedings of the ACM/IEEE Joint Conference on Digital Libraries | Year: 2014

In this paper, we present the overview of Europeana Cloud system, which is a new undertaking of Europeana Foundation and partnering institutions aimed to provide shared, cloud-based infrastructure for aggregation and exchange of cultural heritage metadata and content for European institutions. © 2014 IEEE.


Witkowski M.,Pozna Supercomputing and Networking Center | Oleksiak A.,Pozna Supercomputing and Networking Center | Piontek T.,Pozna Supercomputing and Networking Center | Weoglarz J.,Pozna Supercomputing and Networking Center | Weoglarz J.,AGH University of Science and Technology
Future Generation Computer Systems | Year: 2013

Due to high energy costs, fine-grained power consumption accounting and capability of making users of High Performance Computing (HPC) clusters aware of the cost of their computation is becoming more and more important. Hardware power measurement solutions can be very expensive, hence the appeal of software-based estimation methods. In this paper we present a practical approach to power consumption estimation of both individual application executions and whole computing nodes. We compare it to existing state-of-the-art solutions, provide accuracy figures, and discuss possible deployment scenarios. © 2012 Elsevier B.V. All rights reserved.


Blazewicz J.,AGH University of Science and Technology | Blazewicz J.,Polish Academy of Sciences | Frohmberg W.,AGH University of Science and Technology | Kierzynka M.,AGH University of Science and Technology | And 2 more authors.
Journal of Parallel and Distributed Computing | Year: 2013

Multiple sequence alignment (MSA) methods are essential in biological analysis. Several MSA algorithms have been proposed in recent years. The quality of the results produced by those methods is reasonable, but there is no single method that consistently outperforms others. Additionally, the increasing number of sequences in the biological databases is perceived as one of the upcoming challenges for alignment methods in the nearest future. The lack of performance concerns not only the alignment problems, but may be observed in many areas of biologically related research. To overcome this problem in the field of pairwise alignment, several GPU (Graphics Processing Unit) computing approaches have been proposed lately. These solutions show a great potential of GPU platform. Therefore, our main idea was to design and implement an MSA method which can take advantage of modern graphics cards. Our solution is based on T-Coffee-well known for its high accuracy MSA algorithm. Its computational time, however, is often unacceptable. Performed tests show that our method, named G-MSA, is highly efficient achieving up to 193-fold speedup on a single GPU while the quality of its results remains very good. Due to effective memory usage the method can perform alignment for huge sets of sequences that previously could only be aligned on computer clusters. Moreover, multiple GPUs support with load balancing makes the application very scalable. © 2012 Elsevier Inc. All rights reserved.

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