66,409 research outputs found
Variations on Multi-Core Nested Depth-First Search
Recently, two new parallel algorithms for on-the-fly model checking of LTL
properties were presented at the same conference: Automated Technology for
Verification and Analysis, 2011. Both approaches extend Swarmed NDFS, which
runs several sequential NDFS instances in parallel. While parallel random
search already speeds up detection of bugs, the workers must share some global
information in order to speed up full verification of correct models. The two
algorithms differ considerably in the global information shared between
workers, and in the way they synchronize.
Here, we provide a thorough experimental comparison between the two
algorithms, by measuring the runtime of their implementations on a multi-core
machine. Both algorithms were implemented in the same framework of the model
checker LTSmin, using similar optimizations, and have been subjected to the
full BEEM model database.
Because both algorithms have complementary advantages, we constructed an
algorithm that combines both ideas. This combination clearly has an improved
speedup. We also compare the results with the alternative parallel algorithm
for accepting cycle detection OWCTY-MAP. Finally, we study a simple statistical
model for input models that do contain accepting cycles. The goal is to
distinguish the speedup due to parallel random search from the speedup that can
be attributed to clever work sharing schemes.Comment: In Proceedings PDMC 2011, arXiv:1111.006
Platform Dependent Verification: On Engineering Verification Tools for 21st Century
The paper overviews recent developments in platform-dependent explicit-state
LTL model checking.Comment: In Proceedings PDMC 2011, arXiv:1111.006
An efficient multi-core implementation of a novel HSS-structured multifrontal solver using randomized sampling
We present a sparse linear system solver that is based on a multifrontal
variant of Gaussian elimination, and exploits low-rank approximation of the
resulting dense frontal matrices. We use hierarchically semiseparable (HSS)
matrices, which have low-rank off-diagonal blocks, to approximate the frontal
matrices. For HSS matrix construction, a randomized sampling algorithm is used
together with interpolative decompositions. The combination of the randomized
compression with a fast ULV HSS factorization leads to a solver with lower
computational complexity than the standard multifrontal method for many
applications, resulting in speedups up to 7 fold for problems in our test
suite. The implementation targets many-core systems by using task parallelism
with dynamic runtime scheduling. Numerical experiments show performance
improvements over state-of-the-art sparse direct solvers. The implementation
achieves high performance and good scalability on a range of modern shared
memory parallel systems, including the Intel Xeon Phi (MIC). The code is part
of a software package called STRUMPACK -- STRUctured Matrices PACKage, which
also has a distributed memory component for dense rank-structured matrices
Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems
To comprehend the hierarchical organization of large integrated systems, we
introduce the hierarchical map equation, which reveals multilevel structures in
networks. In this information-theoretic approach, we exploit the duality
between compression and pattern detection; by compressing a description of a
random walker as a proxy for real flow on a network, we find regularities in
the network that induce this system-wide flow. Finding the shortest multilevel
description of the random walker therefore gives us the best hierarchical
clustering of the network, the optimal number of levels and modular partition
at each level, with respect to the dynamics on the network. With a novel search
algorithm, we extract and illustrate the rich multilevel organization of
several large social and biological networks. For example, from the global air
traffic network we uncover countries and continents, and from the pattern of
scientific communication we reveal more than 100 scientific fields organized in
four major disciplines: life sciences, physical sciences, ecology and earth
sciences, and social sciences. In general, we find shallow hierarchical
structures in globally interconnected systems, such as neural networks, and
rich multilevel organizations in systems with highly separated regions, such as
road networks.Comment: 11 pages, 5 figures. For associated code, see
http://www.tp.umu.se/~rosvall/code.htm
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