Zoltan is a toolkit of parallel algorithms for dynamic load balancing, geometric and hypergraph-based partitioning, graph coloring, matrix ordering, and distributed directories.

Zoltan is open-source software, distributed both as part of the Trilinos solver framework and as a stand-alone toolkit.

http://www.cs.sandia.gov/Zoltan/

Modern high performance computing (HPC) nodes have diverse and heterogeneous types of cores and memory. For applications and domain-specific libraries/languages to scale, port, and perform well on these next generation architectures, their on-node algorithms must be re-engineered for thread scalability and performance portability.

The Kokkos programming model and C++ library implementation helps HPC applications and domain libraries implement intra-node thread-scalable algorithms that are performance portable across diverse manycore architectures such as multicore CPUs, Intel Xeon Phi, NVIDIA GPU, and AMD GPU.

https://github.com/kokkos/kokkos

The Trilinos Project is an effort to develop algorithms and enabling technologies within an object-oriented software framework for the solution of large-scale, complex multi-physics engineering and scientific problems. A unique design feature of Trilinos is its focus on packages. https://trilinos.org/

In data analytics, Anasazi is used for distributed memory algebraic modeling of graphs.

Anasazi is an extensible and interoperable framework for large-scale eigenvalue algorithms. The motivation for this framework is to provide a generic interface to a collection of algorithms for solving large-scale eigenvalue problems. Anasazi is interoperable because both the matrix and vectors (defining the eigenspace) are considered to be opaque objects—only knowledge of the matrix and vectors via elementary operations is necessary. One of the goals of Anasazi is to allow the user the flexibility to specify the data representation for the matrix and vectors and so leverage any existing software investment.

https://trilinos.org/packages/anasazi/

MR-MPI is an open-source implementation of MapReduce written for distributed-memory parallel machines on top of standard MPI message passing.

MapReduce is the programming paradigm, popularized by Google, which is widely used for processing large data sets in parallel. Its salient feature is that if a task can be formulated as a MapReduce, the user can perform it in parallel without writing any parallel code. Instead the user writes serial functions (maps and reduces) which operate on portions of the data set independently. The data-movement and other necessary parallel operations can be performed in an application-independent fashion, in this case by the MR-MPI library.

The MR-MPI library was developed to solve informatics problems on traditional distributed-memory parallel computers. It includes C++ and C interfaces callable from most hi-level languages, and also a Python wrapper and our own OINK scripting wrapper, which can be used to develop and chain MapReduce operations together.

http://mapreduce.sandia.gov/

ParaView is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaView’s batch processing capabilities.

ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of exascale size as well as on laptops for smaller data.

ParaView is maintained by Kitware, Inc. Sandia collaborates with Kitware to address our large-scale data analysis and visualization needs through ParaView.

http://www.paraview.org/

Tensors (also known as multidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to network analysis. The Tensor Toolbox provides the following classes for manipulating dense, sparse, and structured tensors using MATLAB's object-oriented features:

• tensor - A (dense) multidimensional array (extends MATLAB's current capabilities).
• sptensor - A sparse multidimensional array.
• tenmat - Store a tensor as a matrix, with extra information so that it can be converted back into a tensor.
• sptenmat - Store an sptensor as a sparse matrix in coordinate format, with extra information so that it can be converted back into an sptensor.
• ttensor - Store a tensor decomposed as a Tucker operator
• ktensor - Store a tensor decomposed as a Kruskal operator

http://www.sandia.gov/~tgkolda/TensorToolbox/index-2.6.html