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Owens, Pitzer

If you plan on using GPUs in tensorflow or pytorch see HOWTO: Use GPU with Tensorflow and PyTorch

This is an exmaple to utilize a GPU to improve performace in our python computations. We will make use of the Numba python library. Numba provides numerious tools to improve perfromace of your python code including GPU support.

Owens, Pitzer

Mathematica is a mathematical computation program. It is capable in many areas of technical computing including but not limited to neural networks, machine learning, image processing, geometry, data science and visualizations.

Owens, Pitzer

Tinker is a molecular modeling package. Tinker provides a general set of tools for molecular mechanics and molecular dynamics.

Pitzer

MRIcroGL is medical image viewer that allows you to load overlays (e.g. statistical maps), draw regions of interest (e.g. create lesion maps).

Availability and Restrictions

Versions

MRIcroGL is available on Pitzer cluster. These are the versions currently available:

Pitzer

dcm2niix is designed to convert neuroimaging data from the DICOM format to the NIfTI format. The DICOM format is the standard image format generated by modern medical imaging devices. However, DICOM is very complicated and has been interpreted differently by different vendors. The NIfTI format is popular with scientists, it is very simple and explicit. However, this simplicity also imposes limitations (e.g. it demands equidistant slices).

SUG Conference - April 20, 2023

Agenda

Ascend

The following are technical specifications for Ascend.  

Number of Nodes

24 nodes

Number of CPU Sockets

48 (2 sockets/node)

Number of CPU Cores

2,304 (96 cores/node)

Cores Per Node

96 cores/node (88 usable cores/node)

Internal Storage

12.8 TB NVMe internal storage

Ascend

For more information about citations of OSC, visit https://www.osc.edu/citation.

To cite Ascend, please use the following Archival Resource Key:

ark:/19495/hpc3ww9d

Please adjust this citation to fit the citation style guidelines required.

Ascend

AMDBLIS is a portable, open-source software framework for instantiating high-performance Basic Linear Algebra Subprograms (BLAS), such as dense linear algebra libraries. The framework was designed to isolate essential kernels of computation that, when optimized, immediately enable optimized implementations of most of the commonly used and computationally-intensive operations.

Ascend

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.

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