68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area)
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大規模二次割当問題への挑戦
(2022)
„Ich mache ihm ein Angebot, das er nicht ablehnen kann.” Diese Aussage aus einem gänzlich anderen Kontext lässt sich recht treffend übertragen als Wunsch von Dienstleistern und Zweck von Dienstleistungen für Datenproduzenten im Forschungsdatenmanagement. Zwar wirkt Druck zur Datenübergabe nicht förderlich, die Eröffnung einer Option aber sehr wohl. Im vorliegenden Artikel geht es um das Verständnis der Nachhaltigkeit von Forschung und ihren Daten anhand der Erkenntnisse und Erfahrungen aus der ersten Phase des DFG-Projekts EWIG. [Fn 01] Eine Auswahl von Fallstricken beim Forschungsdatenmanagement wird anhand der Erkenntnisse aus Expertengesprächen und eigenen Erfahrungen beim Aufbau von LZA-Workflows vorgestellt. Erste Konzepte in EWIG zur Datenübertragung aus unterschiedlich strukturierten Datenquellen in die „Langfristige Domäne” werden beschrieben.
UG is a generic framework to parallelize branch-and-bound based solvers (e.g., MIP, MINLP, ExactIP) in a distributed or shared memory computing environment. It exploits the powerful performance of state-of-the-art "base solvers", such as SCIP, CPLEX, etc. without the need for base solver parallelization.
UG framework, ParaSCIP(ug[SCIP,MPI]) and FiberSCIP (ug[SCIP,Pthreads]) are available as a beta version.
v1.0.0: new documentation and cmake, generalization of ug framework, implementation of selfsplitrampup for fiber- and parascip, better memory and time limit handling.
UG is a generic framework to parallelize branch-and-bound based solvers (e.g., MIP, MINLP, ExactIP) in a distributed or shared memory computing environment. It exploits the powerful performance of state-of-the-art "base solvers", such as SCIP, CPLEX, etc. without the need for base solver parallelization.
UG framework, ParaSCIP(ug[SCIP,MPI]) and FiberSCIP (ug[SCIP,Pthreads]) are available as a beta version. For MIP solving, ParaSCIP and FiberSCIP are well debugged and should be stable. For MINLP solving, they are relatively stable, but not as thoroughly debugged. This release version should handle branch-and-cut approaches where subproblems are defined by variable bounds and also by constrains for ug[SCIP,*] ParaSCIP and FiberSCIP). Therefore, problem classes other than MIP or MINLP can be handled, but they have not been tested yet.
v0.9.1: Update orbitope cip files.
Mixed integer linear programming (MIP) is a general form to model combinatorial optimization problems and has many industrial applications. The performance of MIP solvers has improved tremendously in the last two decades and these solvers have been used to solve many real-word problems. However, against the backdrop of modern computer technology, parallelization is of pivotal importance. In this way, ParaSCIP is the most successful parallel MIP solver in terms of solving previously unsolvable instances from the well-known benchmark instance set MIPLIB by using supercomputers. It solved two instances from MIPLIB2003 and 12 from MIPLIB2010 for the first time to optimality by using up to 80,000 cores on supercomputers. ParaSCIP has been developed by using the Ubiquity Generator (UG) framework, which is a general software package to parallelize any state-of-the-art branch-and-bound based solver. This paper discusses 7 years of progress in parallelizing branch-and-bound solvers with UG.
Neuroanatomical analysis, such as classification of cell types, depends on reliable reconstruction of large numbers of complete 3D dendrite and axon morphologies. At present, the majority of neuron reconstructions are obtained from preparations in a single tissue slice in vitro, thus suffering from cut off dendrites and, more dramatically, cut off axons. In general, axons can innervate volumes of several cubic millimeters and may reach path lengths of tens of centimeters. Thus, their complete reconstruction requires in vivo labeling, histological sectioning and imaging of large fields of view. Unfortunately, anisotropic background conditions across such large tissue volumes, as well as faintly labeled thin neurites, result in incomplete or erroneous automated tracings and even lead experts to make annotation errors during manual reconstructions. Consequently, tracing reliability renders the major bottleneck for reconstructing complete 3D neuron morphologies. Here, we present a novel set of tools, integrated into a software environment named ‘Filament Editor’, for creating reliable neuron tracings from sparsely labeled in vivo datasets. The Filament Editor allows for simultaneous visualization of complex neuronal tracings and image data in a 3D viewer, proof-editing of neuronal tracings, alignment and interconnection across sections, and morphometric analysis in relation to 3D anatomical reference structures. We illustrate the functionality of the Filament Editor on the example of in vivo labeled axons and demonstrate that for the exemplary dataset the final tracing results after proof-editing are independent of the expertise of the human operator.
Simulations of the critical Ising model by means of local update algorithms suffer from critical slowing down. One way to partially compensate for the influence of this phenomenon on the runtime of simulations is using increasingly faster and parallel computer hardware. Another approach is using algorithms that do not suffer
from critical slowing down, such as cluster algorithms. This paper reports on the Swendsen-Wang multi-cluster algorithm on Intel Xeon Phi coprocessor 5110P, Nvidia Tesla M2090 GPU, and x86
multi-core CPU. We present shared memory versions of the said algorithm for the simulation of the two- and three-dimensional Ising model. We use a combination of local cluster search and global label reduction by means of atomic hardware primitives. Further, we
describe an MPI version of the algorithm on Xeon Phi and CPU, respectively. Significant performance improvements over known im
plementations of the Swendsen-Wang algorithm are demonstrated.
This paper proposes a new method for probabilistic analysis of online algorithms. It is based on the notion of stochastic dominance. We develop the method for
the online bin coloring problem introduced by Krumke et al (2008). Using methods for the stochastic
comparison of Markov chains we establish the result that the performance of the online algorithm GreedyFit is stochastically better than the performance of the algorithm OneBin for any number of items processed. This result gives a more realistic
picture than competitive analysis and explains the behavior observed in simulations.
Mixed-integer programming (MIP) problem is arguably among the hardest classes of optimization problems. This paper describes how we solved 21 previously unsolved MIP instances from the MIPLIB benchmark sets. To achieve these results we used an enhanced version of ParaSCIP, setting a new record for the largest scale MIP computation: up to 80,000 cores in parallel on the Titan supercomputer. In this paper, we describe the basic parallelization mechanism of ParaSCIP, improvements of the dynamic load balancing and novel techniques to exploit the power of parallelization for MIP solving. We give a detailed overview of computing times and statistics for solving open MIPLIB instances.
This paper describes how we solved 12 previously unsolved mixed-integer program-
ming (MIP) instances from the MIPLIB benchmark sets. To achieve these results we
used an enhanced version of ParaSCIP, setting a new record for the largest scale MIP
computation: up to 80,000 cores in parallel on the Titan supercomputer. In this paper
we describe the basic parallelization mechanism of ParaSCIP, improvements of the
dynamic load balancing and novel techniques to exploit the power of parallelization
for MIP solving. We give a detailed overview of computing times and statistics for
solving open MIPLIB instances.