Use the Color By feature to group the plotted points by species type. The Y-axis for either plot can be found on the far right of the chart and the X-axis for either plot can be found at the bottom. Graphs on either side of the diagonal represent a pairwise relationship between the two variables, petal_width and sepal_width. To add a variable to the matrix, select the desired variable under Filters. To remove a variable from the matrix, unselect the desired variable under Filters. Histograms of the selected variables appear on the diagonal. Select the Petal_width and Sepal_width variables, and click Finish. Select Scatterplot Matrix, and click Next.
Select a cell within the data set, and on the Data Mining ribbon, from the Data Analysis tab, select Explore - Chart Wizard to open the Chart Wizard dialog. On the Data Mining ribbon, from the Applying Your Model tab, select Help - Examples to open Iris.xlsx.
zssmp_ex1.f, pzssmp_ex1.f, pzssmp_ex2.f, pzssmp_ex3.F (tests for serial and parallel symmetric complex interface).zgsmp_ex1.f, pzgsmp_ex1.f, pzgsmp_ex2.f (tests for complex unsymmetric direct solvers).pwssmp_ex1.f, pwssmp_ex2.f, pwssmp_ex3.f (tests for message-passing symmetric solver).pwgsmp_ex1.f (test for the distributed-memory parallel unsymmetric solver).wssmp_ex1.f, wssmp_ex2.f (small tests for the symmetric solver).wgsmp_ex1.f, wgsmp_ex2.f (small tests for the unsymmetric solver).triplet2cs.c (to convert a sparse matrix from triplet to CSC/CSR format).memchk.c (to check the amount of stack and heap space available to you).In such cases, a reasonable value between 1000000000 could be used. However, setting MALLOC_TRIM_THRESHOLD_ to -1 can lead to memory problems when multiple processes are running on the same node/machine. On Linux, setting the environment variables MALLOC_TRIM_THRESHOLD_ and MALLOC_MMAP_MAX_ to -1 and 0, respectively, yields the best performance, especially for the unsymmetric and symmetric indefinite solvers.Please refer to the section titled "Recent Changes and Other Important Notes" in the Users' Guide before upgrading to a newer version of the software.MPI_THREAD_MULTIPLE thread support must be requested from mpi_init_thread for the distributed-memory unsymmetric solver.Please send e-mail to (wsmp |AT| us |DOT| ibm |DOT| com) if you are unable to run the example programs. The Linux libraries included in the standard distribution below may not work due to compiler, glibc, or MPI incompatibility.Parallel Preconditioners for Sparse Iterative Methods.Shared- and distributed-memory parallel general solver.Adaptive techniques for improving the performance of incomplete factorization preconditioning.Comparison of iterative solvers for SPD systems.Sparse matrix factorization on massively parallel computers.Blocked ILU-based preconditioners and solvers.WSMP Users' Guide Part III - Iterative Solvers.WSMP Users' Guide Part II - General Direct Solvers.WSMP Users' Guide Part I - Symmetric Direct Solvers.MPI-based distributed-memory parallel versions of the iterative solvers are under development.If you need WSMP and PWSMP libraries for a platform-compiler combination missing here, please send e-mail to (wsmp |AT| us |DOT| ibm |DOT| com).WSMP Version 20.12 What's New and What's Coming Up
This high-performance, robust, and easy-to-use software can be used as a serial package, or in a shared-memory multiprocessor environment, or as a scalable parallel solver in a message-passing environment, where each process can be either serial or multithreaded. Watson Sparse Matrix Package (WSMP) is a collection of algorithms for efficiently solving large sparse systems of linear equations.
Note: Please use the "WSMP temporary license files" tab above to download just the license files without upgrading the software. Watson Sparse Matrix Package (WSMP) - overview