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.
Clara Parabricks is a powerful toolkit designed for genomic analysis. It is primarily designed for GPU computation.
MotionCor2 uses multi-GPU acceleration to correct anisotropic cryo-electron microscopy images at the single pixel level across the whole frame, making it suitable for single particle and tomographic images. Iterative, patch-based motion detection is combined with spatial and temporal constraints and dose weighting.
The following versions are available on OSC clusters:
This document is obsoleted and kept as a reference to previous Owens programming environment. Please refer to here for the latest version.
"Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed Deep Learning fast and easy to use. The primary motivation for this project is to make it easy to take a single-GPU TensorFlow program and successfully train it on many GPUs faster."
Quote from Horovod Github documentation.
PyTorch is an open source machine learning framework with GPU acceleration and deep neural networks that is based on the automatic differentiation in the Torch library of tensors.
VMD is a visulaization program for the display and analysis of molecular systems.
The following versions of VMD are available on OSC clusters:
MAGMA is a collection of next generation linear algebra (LA) GPU accelerated libraries designed and implemented by the team that developed LAPACK and ScaLAPACK. MAGMA is for heterogeneous GPU-based architectures, it supports interfaces to current LA packages and standards, e.g., LAPACK and BLAS, to allow computational scientists to effortlessly port any LA-relying software components.
"Torch is a deep learning framework with wide support for machine learning algorithms. It's open-source, simple to use, and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C / CUDA implementation. Torch offers popular neural network and optimization libraries that are easy to use, yet provide maximum flexibility to build complex neural network topologies. It also runs up to 70% faster on the latest NVIDIA Pascal™ GPUs, so you can now train networks in hours, instead of days."
"TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code."
Quote from TensorFlow Github documentation.
