Ruby
Ruby is a dynamic, open source programming language with a focus on simplicity and productivity.
Availability and Restrictions
Versions
The following versions of ruby are available on OSC clusters:
Programming
Software Environment
The Next Gen Ascend (hereafter referred to as “Ascend”) cluster is now running on Red Hat Enterprise Linux (RHEL) 9, introducing several software-related changes compared to the RHEL 7/8 environment used on the Pitzer and original Ascend cluster. These updates provide access to modern tools and libraries but may also require adjustments to your workflows. Key software changes and available software are outlined in the following sections.
Rust
Rust is a general-purpose programming language with an emphasis on performance, type safety, and concurrency. It enforces memory safety without a traditional garbage collector, preventing data races and memory safety errors via the "borrow checker". The Rust module provides rustc and cargo.
Availability and Restrictions
Versions
The following versions of Rust are available on OSC clusters:
Cardinal Software Environment
The Cardinal cluster is now running on Red Hat Enterprise Linux (RHEL) 9, introducing several software-related changes compared to the RHEL 7 environment used on the Pitzer cluster. These updates provide access to modern tools and libraries but may also require adjustments to your workflows. Key software changes and available software are outlined in the following sections.
High Bandwidth Memory
Overview of the High Bandwidth Memory on Cardinal's Dense compute nodes
Cardinal Programming Environment
Compilers
The Cardinal cluster supports C, C++, and Fortran programming languages. The available compiler suites include Intel, oneAPI, and GCC. By default, the Intel development toolchain is loaded. The table below lists the compiler commands and recommended options for compiling serial programs. For more details and best practices, please refer to our compilation guide.
MVAPICH
MVAPICH is a standard library for performing parallel processing using a distributed-memory model.
HOWTO: Run Python in Parallel
We can improve performace of python calculation by running python in parallel. In this turtorial we will be making use of the multithreading library to run python code in parallel.
NVHPC
NVHPC, or NVIDIA HPC SDK, C, C++, and Fortran compilers support GPU acceleration of HPC modeling and simulation applications with standard C++ and Fortran, OpenACC® directives, and CUDA®. GPU-accelerated math libraries maximize performance on common HPC algorithms, and optimized communications libraries enable standards-based multi-GPU and scalable systems programming. Performance profiling and debugging tools simplify porting and optimization of HPC applications, and containerization tools enable easy deployment on-premises or in the cloud.
HOWTO: Use a Conda/Virtual Environment With Jupyter
The IPython kernel for a Conda/virtual environment must be installed on Jupyter prior to use. This tutorial will walk you though the installation and setup procedure.
