maximal independent set problemA simple parallel randomized algorithm to find a maximal independent set in a graph G = (V, E) on n vertices is presented. Its expected running time on a concurrent-read concurrent-write PRAM with O(|E|dmax) processors is O(log n), where dmax denotes the ...
pageJob.setJobName("Find a MIS"); pageJob.setMaxIteration(30); pageJob.setVertexClass(MISVertex.class); pageJob.setInputPath(new Path(args[0])); pageJob.setOutputPath(new Path(args[1])); pageJob.setVertexIDClass(LongWritable.class); pageJob.setVertexValueClass(LongWritable.class); page...
Warning:Before you leave note that This is not my original account, I am not newbie, I am GM irl I think I solved maximal independent set problem which was called NP hard problem. My Solution works in O(log(n)*n*sqrt(n))) Time and alot of memory.I think I have created some of ...
Parallelization of BiteOpt algorithm is technically possible, but may be counter-productive (increases convergence time considerably). It is more efficient to run several optimizers in parallel with different random seeds. Specifically saying, it is possible (tested to be working on some code commits...
a maximal-space algorithm for the container loading problem:集装箱装载问题的最大空间算法 热度: Abstract A Data Distributed Parallel Algorithm for Nonrigid Image Registration 热度: A Local Facility Location Algorithm for Large-scale Distributed Systems ...
The Combinatorial BLAS (CombBLAS) is an extensible distributed-memory parallel graph library offering a small but powerful set of linear algebra primitives specifically targeting graph analytics. - PASSIONLab/CombBLAS
First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction ...
First, we make a remark on using Algorithm 3.1 in practice. In Step 2, the algorithm requires the value of M, the size of a maximal independent set. However, in practice, we need not calculate the value of M beforehand. Instead, we continue the iteration while E(s) is nonempty, which...
empowered by deep learning frameworks. By introducing Gumbel-softmax technique, we can optimize the objective function directly by gradient descent algorithm regardless of the discrete nature of variables. We also introduce evolution strategy to parallel version of our algorithm. We test our algorithm ...
While this "parallel-3" arrangement is currently not used in the hash function implementations, it is also working fine with the core function. For example, while the "minimal PRNG" described earlier has0.90cycles/byte performance, the "parallel" arrangement has a PRNG performance of0.35cycles/by...