Installing own software

This topic is about installing your own software on the CSC servers.

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The one-slide lecture

Code categories

  • Start by reading the software documentation
    • The installation method depends on code type (or category)
    • Instructions found online rarely work as “copy/paste” in an HPC environment
  • Before doing a lot of work, check if an alternative software is already available in the CSC application list
    • Also check with module spider

Binaries

  • If you have ready-made binaries, you can simply try to run them
  • The problem with ready-made binaries is that they are hardly ever optimal for the computer they are used on
    • Especially MPI codes should always be re-compiled for best performance
  • Ready binaries can be considered if
    • The source code is not available
    • The software is compiled on an identical computer
    • The software is for relatively light (serial or threaded) computation

Interpreted languages

  • Examples of high-level interpreted languages are
    • Python, Java, Perl, R, etc.
  • These languages do not need to be compiled, but they often can be
  • Running these programs usually requires loading a suitable module for the language
    • Loading modules ensures also that the software will run the same on the compute nodes

High-performance computing languages

  • Programming languages that need to be compiled
    • Typical examples are e.g. C, C++ and Fortran
  • Most resource-intensive software have been programmed using these
  • As a researcher, you typically only need to compile a software (unless available pre-installed)

About compilers and profiling

  • A compiler is a special program that reads, analyses and translates a human-readable source code into a machine-readable object code
  • It performs 4 steps: Lexical analysis, syntactic and semantic analysis, optimization and output code generation
  • Compilers target specific operating systems and computer architectures and are usually programming language-specific
  • Code profiling: Analysis of an application (memory, CPU, network utilized) to understand its performance
    • Checking how much time is spent in different software routines is important to identify performance bottlenecks (don’t optimize before this!)

Some general notes

  • No sudo available for users on the CSC supercomputers
    • You can’t use package managers (apt, yum, etc.)
    • You can’t install into “standard” locations (/usr/bin, /usr/lib, etc.)
      • Set the installation directory to /projappl or similar
  • Start by loading a suitable compiler suite or language module
    • Many commonly used HPC libraries (e.g. OpenMPI, ScaLAPACK, FFTW) are available as modules (search with module spider)
  • Compile on the fast local disk ($TMPDIR) to avoid stressing Lustre
  • New software is not automatically added to $PATH
    • Include the full path or add with export PATH="/path/to/my/sw:$PATH"

Installation methods: Native installations

  • Installing directly to the system
  • Usually the preferred way for software with few or no dependencies

Installation methods: Containers

  • Containerization is an efficient method to install software and their dependencies
    • Very easy if a ready-made container is available
  • Recommended particularly for software with complex dependencies

Installation methods: Conda

  • Conda is a common installation system, but it is very problematic on HPC systems
    • Creates a huge number of files and leads to poor performance on the Lustre parallel file system
    • Installations easily break when the system changes
  • Containerization is required if you intend to use Conda environments on CSC supercomputers (see usage policy)
    • Wrapping Conda installations into a container alleviates problems since the number of files is dramatically decreased from the FS point of view
    • CSC has created a tool called Tykky which does the containerization automatically and transparently

Testing – it’s important to test first

  • Construct a batch job script for a short and simple test run
    • Use known example/benchmark data provided, e.g., by the code developer (if you did not develop the code yourself)
    • Run a tutorial provided with the software
  • Run your test in the test queue or in an interactive session directly from the command-line
  • Compare performance and results to existing data (your old data, online references, etc.)

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