Search Results for "parallel"

Freemat is an interpreted, matrix-oriented development environment for engineering and scientific applications, similar to the commercial package MATLAB. Freemat provides visualization, image manipulation, and plotting as well as parallel programming.

jAgg is a Java 5.0 API that supports “group by” operations on Lists of Java objects: aggregate operations such as count, sum, max, min, avg, and many more. It supports Super Aggregation: Rollups, Cube, and Grouping Sets. It supports analytic operations such as lag/lead and row number and more. It also allows custom aggregate and analytic operations.

ProC 3.0 is a scientific workflow engine to build, manage and execute workflows (pipelines) in heterogeneous environments, supporting GRID and other means of parallel processing. It includes a data management component (DMC) to transparently access databases for storage of results and automatically adds metadata to track the processing of data products, so that at every time a full processing history is available. The software was developed and used within the ESA Planck satellite mission.

...CRF#'s mainly algorithm is the same as CRF++ written by Taku Kudo. It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. ...

Cloudster is a generic and parallel implementation of the k-means algorithm. It is written in C# and uses the Windows Azure environment.

Parallel IBDWS is an upgrade from IBDWS that utilizes parallel processing. Also, the incorporation of DNA sequence data has been added since the last code release

Weka-Parallel is a modification to Weka, created with the intention of being able to harness the power of Weka and the speed of parallel processing to be able to run a number of data mining and machine learning algorithms quickly.