Which scientific computing applications in scientific research HPC are suitable for GPU accelerated computing?
GPU accelerated computing is suitable for applications that require massive parallel computing, including but not limited to the following application areas:
Deep learning: Neural network is the basis of deep learning, and GPU can accelerate the training and inference calculation of neural network, improving the accuracy and training speed of the model.
Machine Learning and Deep Learning: TensorFlow, PyTorch, Keras, Caffe, etc.
Computer vision: Computer vision requires a large number of feature extraction, classification and recognition calculations on images or videos. These calculations can be accelerated by GPU to improve processing speed and accuracy.
Scientific computing: Scientific computing requires efficient numerical calculation and simulation of large-scale data. GPUs can accelerate calculations such as matrix operations, FFT calculations, fluid dynamics simulations, and molecular dynamics simulations.
Cryptography: Cryptography involves a large number of encryption and decryption calculations, some of which can be accelerated by GPUs to improve encryption and decryption speed and security.
It should be noted that not all applications are suitable for GPU accelerated computing . GPU-accelerated computing usually requires special optimization and parallelization of the code, and although the computing speed of the GPU is faster than that of the CPU, the memory capacity and computing power are relatively weak, so you need to consider when using GPU-accelerated computing The characteristics and computing needs of the application.
Which scientific computing applications in scientific research HPC are suitable for CPU accelerated computing?
The following are some common computing applications that can use GPU acceleration:
Molecular dynamics simulation: AMBER, GROMACS, NAMD, LAMMPS, etc.
Computational Fluid Dynamics: OpenFOAM, ANSYS Fluent, STAR-CCM+, etc.
Machine learning and deep learning: TensorFlow, PyTorch, Keras, Caffe, etc. In most cases, machine learning and deep learning do not need to rely on the computing power of the CPU. GPUs can be used to accelerate computing, but some still need to rely on the computing power of the CPU , There are certain requirements for the core and main frequency of the CPU.
Computational structural mechanics: ABAQUS, Ansys, LS-DYNA, etc.
Quantum chemical calculations: Gaussian, NWChem, ORCA, VASP, etc.
