Abstract: Systems and methods are provided for a model-centric approach that can be used to measure a computer's performance based on metrics obtained during and/or from training a machine learning (ML) model. Examples include building a training data set by generating first matrices and second matrices and deriving third matrices from the first and second matrices. Examples also include training, at a plurality of computer systems, a plurality of machine learning (ML) models by applying the first and third matrices to a plurality of ML algorithms and obtaining performance metrics based on training. The performance metrics can then be set as benchmarks for the plurality of computing systems to facilitate assessing a relative performance amongst the plurality of computing systems.

Abstract: Systems and methods are provided for an optical transport implementation of an inference engine capable of performing inferences in the optical domain. Examples include an optical device that includes photon directing devices disposed along an optical axis, each photon directing device corresponds to a layer of a trained machine learning model. Lenses are provided for each photon directing device, which are formed based on weights of a layer of the trained machine learning model corresponding to the respective photon directing device. The examples may also include optical sensors that correspond to inferences of the trained machine learning model, and the photon directing devices may be configured to receive light of an input and direct the light to one of the optical sensors according to the trained machine learning model.

Abstract: Examples disclosed herein relate to selection of a set of nodes in a HPC system for running one or more computational jobs. In some examples, the selection of the set of nodes includes gathering information about a cluster of nodes in a high-performance computing system. The HPC system may be in a production state with one or more computational workloads getting executed thereon. In some examples, periodically sending one or more test-computing jobs for execution on each node to measure one or more performance metrics thereof. Receiving measured performance metrics from each node, in response to the one or more test-computing jobs executed thereon. Recording in a database, the measured performance metrics received from each node. Selecting the set of nodes from the cluster of nodes, based on the database, and based on a request received to nm one or more computational jobs on the HPC system.

Abstract: Systems and methods are provided for implementing a machine learning (ML) model based compiler, language translator, and/or a decompiler. For example, the system may receive a first source code in a first programming language, tokenize the first source code file forming tokenized code, generate a sequence vector of tokenized code, and provide the sequence vector of tokenized code as input to a trained ML model compiler. The output of the trained ML model compiler may create a second executable file or the source code in a second programming language.

Abstract: Systems and methods are provided for implementing a machine learning (ML) model based compiler, language translator, and/or a decompiler. For example, the system may receive a first source code in a first programming language, tokenize the first source code file forming tokenized code, generate a sequence vector of tokenized code, and provide the sequence vector of tokenized code as input to a trained ML model compiler. The output of the trained ML model compiler may create a second executable file or the source code in a second programming language.

Abstract: Examples disclosed herein relate to selection of a set of nodes in a HPC system for running one or more computational jobs. In some examples, the selection of the set of nodes includes gathering information about a cluster of nodes in a high-performance computing system. The HPC system may be in a production state with one or more computational workloads getting executed thereon. In some examples, periodically sending one or more test-computing jobs for execution on each node to measure one or more performance metrics thereof. Receiving measured performance metrics from each node, in response to the one or more test-computing jobs executed thereon. Recording in a database, the measured performance metrics received from each node. Selecting the set of nodes from the cluster of nodes, based on the database, and based on a request received to nm one or more computational jobs on the HPC system.