Lucie is a cluster installation and configuration tool. It enables parallel network installation of large numbers of nodes from one single administration server. The Lucie installer performs HDD partitioning and installations of the Linux kernel and required software packages. The Lucie configurator then generates system and software configurations. Lucie is designed to be scalable and efficient, so a complete Linux cluster can be built from scratch in a short amount of time. Moreover, the whole installation process is designed to be fully automated.
OpenMPF is a library for solving large, dense, multi-RHS linear systems. It is based on MPI/openMP parallelism, and relies on BLAS/LAPACK/MUMPS for the single node computations. It implements direct and iterative solvers, out-of-core matrices and vectors, and is easily accessible through a Python interface.
Robinhood Policy Engine is a multi-purpose tool for managing the content of large filesystems. It can audit filesystem content, perform accounting, remove old unused files according to admin-defined policies, show customizable alerts based on file properties, backup data to external storage, and more. It has advanced capabilities for Lustre filesystems. It leverages OST usage, and lists or purges files per OST, with policy criteria based on pools and OST index. It can also process MDT changelogs with Lustre v2. Originally developped for HPC, it has been designed to perform all of its tasks in parallel, so it is particularly adapted for running on large filesystems with millions of entries and petabytes of data. But you can nonetheless take advantage of all of its features for managing smaller filesystems.
LifeV is a finite element (FE) library providing implementations of state of the art mathematical and numerical methods. It serves both as a research and production library. It has already been used in medical and industrial contexts to simulate fluid structure interaction and mass transport. LifeV is the joint collaboration between four institutions: École Polytechnique Fédérale de Lausanne (CMCS) in Switzerland, Politecnico di Milano (MOX) in Italy, INRIA (REO, ESTIME) in France, and Emory University (Sc. Comp) in the U.S.A.
LavaFlow creates useful reports on the usage of high-performance computing clusters. It takes data from the batch scheduling system, monitoring, and other tooling, and creates reports which help administrators, managers, and end users better understand their cluster environment. The reports are modular, and new modules are easy to create using templates and Django's query set API. LavaFlow uses human-readable RESTful URLs, making it easy to automate and share links to reports.
Strategico is an engine for running statistical analysis over groups of time series. It can manage one or more groups (projects) of time series: by default, you can get data from a database or CSV files, normalize them, and then save them inside the engine. The first statistical analysis implemented inside Strategico is the "Long Term Prediction": it automatically finds the best model that fits each time series. Some of the models implemented are mean, trend, linear, exponential smoothing, and Arima. Strategico is scalable: the statistical analysis over each time series (of a project) can be run separately and independently. It is suggested that you set up an HPC Cluster (High Performance Computing) and/or use a resource scheduler like slurm. It is developed with R, one of the most famous statistical languages.
The Palabos library offers a framework in C++ for fluid flow simulations with the lattice Boltzmann (LB) method. Originally conceived as a research tool for lattice Boltzmann models, the code has evolved into a general-purpose program for computational fluid dynamics. The programming interface is straightforward and offers an access to the rich world of lattice Boltzmann, even to an audience with restricted theoretical knowledge of this method. A special emphasis is put on high performance and parallel computing.
StarCluster is a utility for creating traditional computing clusters used in research labs or for general distributed computing applications on Amazon's Elastic Compute Cloud (EC2). It uses a simple configuration file provided by the user to request cloud resources from Amazon and to automatically configure them with a queuing system, an NFS shared /home directory, passwordless SSH, OpenMPI, and ~140GB scratch disk space. It consists of a Python library and a simple command line interface to the library. For end-users, the command line interface provides simple intuitive options for getting started with distributed computing on EC2 (i.e. starting/stopping clusters, managing AMIs, etc). For developers, the library wraps the EC2 API to provide a simplified interface for launching/terminating nodes, executing commands on the nodes, copying files to/from the nodes, etc.
iLAP (Laboratory data management, Analysis, and Protocol development) is a workflow-driven information management system specifically designed to create and manage experimental protocols and to analyze and share laboratory data. The system combines experimental protocol development, wizard-based data acquisition, and high-throughput data analysis into a single, integrated system.