METIS[3]是一种层次化的分割算法(multi-level partitioning),核心思想对于给定原图结构持续的稀疏化融合结点和边来降低原图的大小,然后达到一定程度对于缩减后的图结构进行分割,最后将分割后的小图还原成原始的图结构保证每份子图的均衡性。 如图2所示,将一个图分割为3份,首先进行3层的稀疏化然后对于缩小后包含3个顶...
点分割算法有：METIS采用层次化策略，随机初始化结点进行宽度优先遍历切分；Random Hash使用哈希函数随机分配结点；LDG考虑邻居结点放置减少边丢失；Fennel对LDG算法进行改进。边分割算法有：NE考虑邻居局部性进行切分，动态调整核心与候选集；DBH通过结点度信息切分，保持低度结点局部性；HDRF结合结点度与子图负载...
在许多情况下,这种方法既可缩短执行时间,又可提供非常高质量的结果。广泛使用的例子是METIS,[7]的曲线图分割器,和hMETIS(超图的相应分区)。另一种源于[9]并在scikit-learn中实现的方法是谱聚类,分区由LOBPCG求解器计算的原始图的Laplacian矩阵的特征向量决定,并采用多网格预设(multigridpreconditioning)。 谱分区和谱...
显然,为了解决这一问题,一个思路就是将图数据分布式的存储在集群中,后进行分布式的训练。比如,我们可以直接利用流行的图分割算法,如METIS等,将图划分为互不重叠的多个簇。之后,集群中的一台机器仅存储一个簇的数据,并只更新该簇中节点的表达。然而,这个简单的思路可能会使簇中节点丢失部分一阶邻居,致使信息损失、...
For partitioning into 2 domains, gpmetis without ubvec would be happy to put all 8 million cells of the coarsest level into one domain, and the rest into the other. The weighted sum of cells checks out, but load balancing for our algorithm is crap. ...
NetworkX-METIS is a NetworkX addon that enables graph partitioning with METIS. NetworkXis a Python package for the creation, manipulation and study of the structure, dynamics, and functions of complex networkx.METISis a C library written for partitioning graphs, partitioning finite element meshes, an...
Our experiments, in which we compare sequential and parallel Bubble-FOS/C implementations to the state-of-the-art libraries Metis and Jostle, reveal that our new heuristic is slower, but generates high-quality solutions that are often superior....
METIS -- Unstructured Graph Partitioning and Sparse Matrix Ordering System, Version 2.0 This chapter discusses the use of metals in the treatment of different diseases. Antimony is used quite extensively in the treatment of leishmaniosis and i... G Karypis,V Kumar - 《Side Effects of Drugs Annua...
=metis.OK:raiseValueError When you compile your Cython code, you don't need to worry about linking to METIS, Python will load the correct symbol in runtime. The native Python mode supports parallel partitioning of a static graph or mesh. The underlying routines are:...
在Hermes中,初始分区是用哈希或Metis执行的,而当图的顶点集发生变化时,会触发它们的重新分区算法。 重分区使用迭代的方式对平衡度和edge-cut进行局部改进。只有在改进过程完成时,才会移动关联的数据 。在他们对真实世界工作负载的实验中,他们的算法产生了大致相同的edge-cut,同时与Metis(从头开始重新分区)相比,迁移的...