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Ascend, Cardinal, Pitzer
This page outlines how to use the Jupyter interactive app on OnDemand.
Launching Jupyter App
Cardinal, Pitzer
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
We have prepared "Getting Started with Cardinal" course on the ScarletCanvas platform. This course offers essential guidance for migrating jobs from other clusters to the Cardinal cluster at the Ohio Supercomputer Center (OSC).
Cardinal
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.
Cardinal
Overview of the High Bandwidth Memory on Cardinal's Dense compute nodes
Cardinal
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.
Cardinal
These are the public key fingerprints for Cardinal:
cardinal: ssh_host_rsa_key.pub = 73:f2:07:6c:76:b4:68:49:86:ed:ef:a3:55:90:58:1b
cardinal: ssh_host_ed25519_key.pub = 93:76:68:f0:be:f1:4a:89:30:e2:86:27:1e:64:9c:09
cardinal: ssh_host_ecdsa_key.pub = e0:83:14:8f:d4:c3:c5:6c:c6:b6:0a:f7:df:bc:e9:2e
PyTorch Fully Sharded Data Parallel (FSDP) is used to speed-up model training time by parallelizing training data as well as sharding model parameters, optimizer states, and gradients across multiple pytorch instances.
Pitzer
CUDA Quantum is a platform for developing quantum-classical applications that leverages NVIDIA's CUDA technology. This platform provides a framework to create and execute quantum algorithms on quantum processors while integrating with classical computing resources. It is designed to accelerate quantum computing tasks and support hybrid quantum-classical workflows, making it an essential tool for researchers and developers in the field of quantum computing.
