Owens

MPI is a standard library for performing parallel processing using a distributed memory model. The Ruby, Owens, and Pitzer clusters at OSC can use the OpenMPI implementation of the Message Passing Interface (MPI).

ParMETIS (Parallel Graph Partitioning and Fill-reducing Matrix Ordering) is an MPI-based parallel library that implements a variety of algorithms for partitioning unstructured graphs, meshes, and for computing fill-reducing orderings of sparse matrices. ParMETIS extends the functionality provided by METIS and includes routines that are especially suited for parallel AMR computations and large scale numerical simulations. The algorithms implemented in ParMETIS are based on the parallel multilevel k-way graph-partitioning, adaptive repartitioning, and parallel multi-constrained partitioning schemes developed in Karypis lab.

METIS (Serial Graph Partitioning and Fill-reducing Matrix Ordering) is a set of serial programs for partitioning graphs, partitioning finite element meshes, and producing fill reducing orderings for sparse matrices. The algorithms implemented in METIS are based on the multilevel recursive-bisection, multilevel k-way, and multi-constraint partitioning schemes developed in Karypis lab.

SuiteSparse is a suite of sparse matrix algorithms, including: UMFPACK(multifrontal LU factorization), CHOLMOD(supernodal Cholesky, with CUDA acceleration), SPQR(multifrontal QR) and many other packages.

There are many instances where it is necessary to run the same serial program many times with slightly different input. Parametric runs such as these either end up running in a sequential fashion in a single batch job, or a batch job is submitted for each parameter that is varied (or somewhere in between.) One alternative to this is to allocate a number of nodes/processors to running a large number of serial processes for some period of time. The command parallel-command-processor allows the execution of large number of independent serial processes in parallel.