4,224 research outputs found
LUNES: Agent-based Simulation of P2P Systems (Extended Version)
We present LUNES, an agent-based Large Unstructured NEtwork Simulator, which
allows to simulate complex networks composed of a high number of nodes. LUNES
is modular, since it splits the three phases of network topology creation,
protocol simulation and performance evaluation. This permits to easily
integrate external software tools into the main software architecture. The
simulation of the interaction protocols among network nodes is performed via a
simulation middleware that supports both the sequential and the
parallel/distributed simulation approaches. In the latter case, a specific
mechanism for the communication overhead-reduction is used; this guarantees
high levels of performance and scalability. To demonstrate the efficiency of
LUNES, we test the simulator with gossip protocols executed on top of networks
(representing peer-to-peer overlays), generated with different topologies.
Results demonstrate the effectiveness of the proposed approach.Comment: Proceedings of the International Workshop on Modeling and Simulation
of Peer-to-Peer Architectures and Systems (MOSPAS 2011). As part of the 2011
International Conference on High Performance Computing and Simulation (HPCS
2011
A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters
In this work, we consider the solution of boundary integral equations by
means of a scalable hierarchical matrix approach on clusters equipped with
graphics hardware, i.e. graphics processing units (GPUs). To this end, we
extend our existing single-GPU hierarchical matrix library hmglib such that it
is able to scale on many GPUs and such that it can be coupled to arbitrary
application codes. Using a model GPU implementation of a boundary element
method (BEM) solver, we are able to achieve more than 67 percent relative
parallel speed-up going from 128 to 1024 GPUs for a model geometry test case
with 1.5 million unknowns and a real-world geometry test case with almost 1.2
million unknowns. On 1024 GPUs of the cluster Titan, it takes less than 6
minutes to solve the 1.5 million unknowns problem, with 5.7 minutes for the
setup phase and 20 seconds for the iterative solver. To the best of the
authors' knowledge, we here discuss the first fully GPU-based
distributed-memory parallel hierarchical matrix Open Source library using the
traditional H-matrix format and adaptive cross approximation with an
application to BEM problems
Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.
The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics
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