Cardinal

Darshan

Darshan is a lightweight "scalable HPC I/O characterization tool".  It is intended to profile I/O by emitting log files to a consistent log location for systems administrators, and also provides scripts to create summary PDFs to characterize I/O in MPI-based programs.

Availability and Restrictions

Versions

The following versions of Darshan are available on OSC clusters:

Spark

Apache Spark is an open source cluster-computing framework originally developed in the AMPLab at University of California, Berkeley but was later donated to the Apache Software Foundation where it remains today. In contrast to Hadoop's disk-based analytics paradigm, Spark has multi-stage in-memory analytics. Spark can run programs up-to 100x faster than Hadoop’s MapReduce in memory or 10x faster on disk. Spark support applications written in python, java, scala and R.

R and Rstudio

R is a language and environment for statistical computing and graphics. It is an integrated suite of software facilities for data manipulation, calculation, and graphical display. It includes

  • an effective data handling and storage facility,
  • a suite of operators for calculations on arrays, in particular matrices,
  • a large, coherent, integrated collection of intermediate tools for data analysis,
  • graphical facilities for data analysis and display either on-screen or on hardcopy, and
  • a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input, and output facilities

More information can be found here.

Bowtie2

Bowtie2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 GB. Bowtie 2 supports gapped, local, and paired-end alignment modes.

HOWTO: Create and Manage Python Environments

While our Python installations come with many popular packages installed, you may come upon a case in which you need an additional package that is not installed. If the specific package you are looking for is available from anaconda.org (formerly binstar.org), you can easily install it and required dependencies by using the conda package manager.

Procedure

The following steps are an example of how to set up a Python environment and install packages to a local directory using conda. We use the name  local for the environment, but you may use any other name.

HOWTO: Install Python packages from source

HOWTO: Locally Installing Software

Sometimes the best way to get access to a piece of software on the HPC systems is to install it yourself as a "local install". This document will walk you through the OSC-recommended procedure for maintaining local installs in your home directory or project space. The majority of this document describes the process of "manually" building and installing your software. We also show a partially automated approach through the use of a bash script in the Install Script section near the end.

STAR

STAR: Spliced Transcripts Alignment to a Reference.

Availability and Restrictions

Versions

The following versions of STAR are available on OSC clusters:

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