International Business Machines Corporation

Chih-Chieh Yang

Guojing Cong

Ruhong Zhou

Eun Kyung Lee

Alessandro Morari

Bilge Acun

Giacomo Domeniconi

Leili Zhang

A computer-implemented method for executing a computation task in a molecular dynamic simulation includes identifying a bonding target on a ligand; constructing a protein structure; rendering an image of the ligand; subsampling data pertaining to the constructed protein structure and the image of the ligand at a particular frequency; rendering a two-dimensional image of the constructed protein structure relative to the ligand from a plurality of viewpoints; computing optical flows of the protein structure relative to the ligand based on the two-dimensional image; analyzing the optical flows to determine a displacement of atoms; simulating a binding state outcome of the protein structure relative to the ligand for each of the plurality of viewpoints; and predicting a probability of the protein structure binding with the ligand, based on the predicted binding state outcome for each of the plurality of viewpoints.

Optical flow based forecasting of binding states in molecular dynamic simulations

Original data for machine learning training can be received. The original data can be divided into baseline data and difference data. The baseline data and the difference data can be stored in different memory devices of the memory hierarchy associated with a computer, wherein the baseline data is stored in a first memory device having faster access speed than a second memory device in which the difference data is stored. The baseline data and the difference data can be loaded from the different memory devices. The original data can be reconstructed from the baseline data and the difference data. The reconstructed original data can be fed to a machine learning model to train the machine learning model.

Similarity-based hierarchical data loading for machine learning training

A data index sequence indexing a dataset is received. A location of a data sample identified by a data index in the data index sequence is determined. A scheme is generated for specifying a data movement based on the location. Responsive to determining that the location is a cache of a process, the data sample in the cache can be reused without having to load the data sample from a storage device.

Locality aware data loading for machine learning

Growing community of inventors

White Plains, NY, United States of America

Chih-Chieh Yang