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Owens, Pitzer
GPU Usage on Tensorflow
Environment Setup
To begin, you need to first create and new conda environment or use an already existing one. See HOWTO: Create Python Environment for more details. In this example we are using python/3.6-conda5.2
Ascend, Owens, Pitzer
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.
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
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.