# Software tools developed by the lab Over the years, the research in the lab has resulted in the development of a number of software tools and libraries for key problems in the areas of parallel processing, data mining, bioinformatics, and collaborative filtering. It is our general policy to make these tools available to the research community for use in their own research and/or non-commercial applications. ## METIS: A Family of Multilevel Partitioning Algorithms This is a collection of serial and parallel programs & libraries that can be used to partitioning unstructured graphs, finite element meshes, and hypergraphs, both on serial as well as on parallel computers. Additional information can be found [here](./metis/overview.md). ## CLUTO: Software for Clustering High-Dimensional DataSets This is a collection of computationally efficient and high-quality data clustering and cluster analysis programs & libraries, that are well suited for high-dimensional data sets. Additional information can be found [here](./cluto/overview.md). ## BDMPI: Big Data Message Passing Interface BDMPI is a message passing library and associated runtime system for developing out-of-core distributed computing applications for problems whose aggregate memory requirements exceed the amount of memory that is available on the underlying computing cluster. Additional information can be found [here](./bdmpi/overview.md). ## SLIM - Sparse Linear Methods for Top-N Recommender Systems This is a library that implements a set of top-N recommendation methods that learn an item-item similarity matrix using sparse linear models. SLIM is available on [Github](https://github.com/KarypisLab/SLIM). ## NERSTRAND - Multi-threaded modularity-based graph clustering This is a program that implements various serial and parallel modularity-based graph clustering algorithms based on the multilevel paradigm. These algorithms can produce high-quality clustering solutions and can scale to very large graphs. NERSTRAND is available on [Github](https://github.com/dlasalle/nerstrand). ## SPLATT - Parallel Sparse Tensor Decomposition This is a program that implements various serial and parallel modularity-based graph clustering algorithms based on the multilevel paradigm. These algorithms can produce high-quality clustering solutions and can scale to very large graphs. SPLATT is available on [Github](https://github.com/ShadenSmith/splatt). ## L2AP - Fast Cosine Similarity Search With Prefix L-2 Norm Bounds This is a program that implements various fast algorithms for for finding the set of all pairs of similar vectors (e.g., documents) whose similarity is greater than a user-specified threshold. L2AP is available [here](https://davidanastasiu.net/software/l2ap/). ## L2Knng - Fast K-Nearest Neighbor Graph Construction with L2-Norm Pruning This is a program that provides high-performance implementations of several methods for constructing the K-nearest neighbor graph of a set of vectors based on cosine similarity. L2Knng is available [here](https://davidanastasiu.net/software/l2knng/). ## PAFI: Software for Finding Patterns in Diverse Datasets This is a collection of computationally efficient programs for finding frequent patterns in transactional, sequential, and graph datasets. Additional information can be found [here](./pafi/overview.md). ## AFGen: Fragment-based Descriptors for Chemical Compounds AFGen is a program that takes as input a set of chemical compounds and generates their vector-space representation based on the set of fragment-based descriptors they contain. The descriptor space consists of graph fragments that can have three different types of topologies: paths (PF), acyclic subgraphs (AF), and arbitrary topology subgraphs (GF). This vector-based representation can be used for different tasks in cheminformatics including similarity search, virtual screening, and library design. These descriptors are quite effective in capturing the structural characteristics of chemical compounds. Experiments in the context of SVM-based classification and ranked-retrieval show that these descriptors consistently and statistically outperform previously developed schemes based on the widely used fingerprint- and Maccs keys-based descriptors, as well as recently introduced descriptors obtained by mining and analyzing the structure of the molecular graphs. > Getting the latest release: > - [afgen-2.0.0.tar.gz Linux (i686/x86_64)](files/afgen/afgen-2.0.0.tar.gz) > > On Unix systems, after downloading AFGen you need to uncompress and untar it. This is > achieved by executing the following command: > > gunzip afgen-2.0.0.tar.gz > tar -xvf afgen-2.0.0.tar > > At this point you should have a directory named afgen-2.0.0. This directory contains > AFGen's stand-alone programs, its documentation, and a sample dataset. > > Instructions describing how to use AFGen can be found at > [afgen-2.0/doc/index.html](/glaros/files/sw/afgen/doc/index.html). ## SUGGEST: A top-N Recommender Engine SUGGEST is a Top-N recommendation engine that implements a variety of recommendation algorithms. Top-N recommender systems, a personalized information filtering technology, are used to identify a set of N items that will be of interest to a certain user. In recent years, top-N recommender systems have been used in a number of different applications such to recommend products a customer will most likely buy; recommend movies, TV programs, or music a user will find enjoyable; identify web-pages that will be of interest; or even suggest alternate ways of searching for information. The algorithms implemented by SUGGEST are based on collaborative filtering that is the most successful and widely used framework for building recommender systems. SUGGEST implements two classes of collaborative filtering-based top-N recommendation algorithms, called user-based and item-based. > SUGGEST is currently distributed in a binary format and consists a stand-alone > executable program and a library, which can be used to call SUGGEST's routines > directly from another application. > > The first step in using SUGGEST is to download the distribution file for your architecture. > > - [suggest-1.0-linux.tar.gz](/glaros/files/sw/suggest/suggest-1.0-linux.tar.gz). > - [suggest-1.0-win32.zip](/glaros/files/sw/suggest/suggest-1.0-win32.zip). > > After downloading SUGGEST you need to uncompress and untar it. This is achieved by executing the following command: > > gunzip suggest-1.0-xxxxx.tar.gz > tar -xvf suggest-1.0-xxxxx.tar > > At this point you should have a directory named suggest-1.5-xxxxx. This directory > contains SUGGEST's stand-alone programs and its user-callable library. > > Instructions describing how SUGGEST is used can be found at suggest-1.0-xxxxx/manual.pdf. > You can get a local copy of this manual in PDF format from > [here](/glaros/files/sw/suggest/manual.pdf). ## MGridGen: Multilevel Serial & Parallel Coarse Grid Construction Library MGridGen is a parallel library written entirely in ANSI C that implements (serial) algorithms for obtaining a sequence of successive coarse grids that are well-suited for geometric multigrid methods. The quality of the elements of the coarse grids is optimized using a multilevel framework. It is portable on most Unix systems that have an ANSI C compiler. An MPI-based parallel version of MGridGen, called ParMGridGen, has also been developed that extends the functionality provided by MGridGen and is especially suited for large scale numerical simulations. It is written entirely in ANSI C and MPI and is portable on most parallel computers that support MPI. [Source code](https://github.com/mrklein/ParMGridGen) ## PSPASES: A Parallel Sparse Direct Solver PSPASES (Parallel SPArse Symmetric dirEct Solver) is a high performance, scalable, parallel, MPI-based library, intended for solving linear systems of equations involving sparse symmetric positive definite matrices. The library provides various interfaces to solve the system using four phases of direct method of solution: compute fill-reducing ordering, perform symbolic factorization, compute numerical factorization, and solve triangular systems of equations. The library efficiently implements the scalable parallel algorithms developed by lab members and our collaborators, to compute each of the phases.