PyCOMPSs implementation of Probabilistic Tsunami Forecast (PTF). PTF explicitly treats data- and forecast-uncertainties, enabling alert level definitions according to any predefined level of conservatism, which is connected to the average balance of missed-vs-false-alarms. Run of the Kos-Bodrum 2017 event test-case with 1000 scenarios, 8h tsunami simulation for each and forecast calculations for partial and full ensembles with focal mechanism and tsunami data updates.

PyCOMPSs implementation of Probabilistic Tsunami Forecast (PTF). PTF explicitly treats data- and forecast-uncertainties, enabling alert level definitions according to any predefined level of conservatism, which is connected to the average balance of missed-vs-false-alarms. Run of the Boumerdes-2003 event test-case with 1000 scenarios, 8h tsunami simulation for each and forecast calculations for partial and full ensembles with focal mechanism and tsunami data updates.

Name: Java Wordcount Contact Person: support-compss@bsc.es Access Level: public License Agreement: Apache2 Platform: COMPSs

Description

Wordcount application. There are two versions of Wordcount, depending on how the input data is given.

Version 1

''Single input file'', where all the text is given in the same file and the chunks are calculated with a BLOCK_SIZE parameter.

Version 2

''Multiple input files'', where the text fragments are already in different files under ...

Name: K-means Contact Person: support-compss@bsc.es Access Level: Public License Agreement: Apache2 Platform: COMPSs

Description

K-means clustering is a method of cluster analysis that aims to partition ''n'' points into ''k'' clusters in which each point belongs to the cluster with the nearest mean. It follows an iterative refinement strategy to find the centers of natural clusters in the data.

When executed with COMPSs, K-means first generates the input points by means of ...

Name: SparseLU Contact Person: support-compss@bsc.es Access Level: public License Agreement: Apache2 Platform: COMPSs

Description

The Sparse LU application computes an LU matrix factorization on a sparse blocked matrix. The matrix size (number of blocks) and the block size are parameters of the application.

As the algorithm progresses, the area of the matrix that is accessed is smaller; concretely, at each iteration, the 0th row and column of the current matrix are discarded. ...

Name: Matrix Multiplication Contact Person: support-compss@bsc.es Access Level: public License Agreement: Apache2 Platform: COMPSs

Description

Matrix multiplication is a binary operation that takes a pair of matrices and produces another matrix.

If A is an n×m matrix and B is an m×p matrix, the result AB of their multiplication is an n×p matrix defined only if the number of columns m in A is equal to the number of rows m in B. When multiplying A and B, the elements of the ...