International Business Machines Corporation

Universiti Brunei Darussalam

Vaibhav Saxena

Yogish Sabharwal

Thomas George

Ashish Verma

Jayaram Kallapalayam Radhakrishnan

Parikshit Ram

Monu Kedia

Anamitra Roy Choudhury

Saurav Basu

Rashmi Mittal

Dakuo Wang

Sijia Liu

Horst Cornelius Samulowitz

Anthony Paul Praino

Lloyd Alan Treinish

Gregory Bramble

Deepak Vijaykeerthy

Aswin Kannan

Alexander Gray

Saiful A Husain

James Peter Cipriani

Prashant Agrawal

Saurabh Manish Raje

Chandrasekar Radhakrishnan

Arunima Chaudhary

The exemplary embodiments disclose a method, a computer program product, and a computer system for determining that one or more model pipelines satisfy one or more constraints. The exemplary embodiments may include detecting a user uploading data, one or more constraints, and one or more model pipelines, collecting the data, the one or more constraints, and the one or more model pipelines, and determining that one or more of the model pipelines satisfies all of the one or more constraints based on applying one or more algorithms to the collected data, constraints, and model pipelines.

Machine learning with multiple constraints

A system, computer program product, and method are presented for performing multi-objective automated machine learning, and, more specifically, to identifying a plurality of machine learning pipelines as Pareto-optimal solutions to optimize a plurality of objectives. The method includes receiving input data directed toward one or more subjects of interest and determining a plurality of objectives to be optimized. The method also includes ingesting at least a portion of the input data through one or more machine learning (ML) models. The method further includes aggregating the plurality of objectives into one or more aggregated single objectives. The method also includes determining a plurality of Pareto-optimal solutions, thereby defining a plurality of ML pipelines that optimize the one or more aggregated single objectives. The method further includes selecting one ML pipeline from the plurality of ML pipelines.

Multi-objective automated machine learning

One embodiment provides a method, including: receiving at least one deep learning job for scheduling and running on a distributed system comprising a plurality of nodes; receiving a batch size range indicating a minimum batch size and a maximum batch size that can be utilized for running the at least one deep learning job; determining a plurality of runtime estimations for running the at least one deep learning job; creating a list of optimal combinations of (i) batch sizes and (ii) numbers of the plurality of nodes for running both (a) the at least one deep learning job and (b) current deep learning jobs; and scheduling the at least one deep-learning job at the distributed system, responsive to identifying, by utilizing the list, that the distributed system has necessary processing resources for running both (iii) the at least one deep learning job and (iv) the current deep learning jobs.

Application aware resource allocation for deep learning job scheduling

Methods, systems, and computer program products for determining optimal compute resources for distributed batch based optimization applications are provided herein. A method includes obtaining a size of an input dataset, a size of a model, and a set of batch sizes corresponding to a job to be processed using a distributed computing system; computing, based at least in part on the set of batch sizes, one or more node counts corresponding to a number of nodes that can be used for processing said job; estimating, for each given one of the node counts, an execution time to process the job based on an average computation time for a batch of said input dataset and an average communication time for said batch of said input dataset; and selecting, based at least in part on said estimating, at least one of said node counts for processing the job.

Determining optimal compute resources for distributed batch based optimization applications

One embodiment provides a method for determining at least one recommended vehicular travel route, the method including collecting a plurality of travel route inputs from a plurality of sources, relative to a travel route comprising a starting point and an ending point; determining at least one travel route recommendation by (i) assessing the collected plurality of travel route inputs and (ii) taking in to consideration vehicle emission impact parameters; and tracking vehicle adherence. Other variants and embodiments are broadly contemplated herein.

Travel planning based on minimizing impact of vehicular emission

Methods and arrangements for automating weather model configurations. Characteristics of a target geographical area are received, as are a plurality of existing weather model configurations which are implemented for undertaking weather modeling in other geographical areas. A subset of the existing weather model configurations is selected, the subset comprising configurations corresponding to geographical areas having characteristics similar to the characteristics of the target area. A weather model is run with respect to each configuration in the subset of existing weather model configurations. Based on the running of a weather model with respect to each configuration, an output set of weather model configurations is selected for undertaking weather modeling in the target area.

Automating weather model configurations

Techniques for transferring a matrix for performing one or more operations are provided. The techniques include applying a permutation on at least one of one or more columns and one or more rows of a matrix to group each of at least one of one or more columns and one or more rows of the matrix with a same alignment, blocking at least one of the grouped columns and grouped rows, and performing one or more operations on each matrix block.

Efficient transfer of matrices for matrix based operations

A computer-implemented method of load balancing including calculating an expected cost set associated with an application-specific task of an application executing on a processing resource in a cloud computing environment, and communicating the expected cost set from the processing resource to a cloud management system. Resource mapping of applications currently executing in the cloud computing environment are retrieved, the application-specific task is assigned to a specific computational resource in the cloud computing environment based on the expected cost set and the resource mapping of applications currently executing in the cloud computing environment. A task to VM (virtual machine) assignment is determined based on the assignment of the application-specific task to the specific computational resource. The task to VM assignment is transferred from the cloud management system to the application executing on the processing resource in the cloud computing environment.

Method for improving the performance of high performance computing applications on Cloud using integrated load balancing

A non-transitory computer-implemented method of load balancing includes calculating an expected cost set associated with an application-specific task of an application executing on a processing resource in a cloud computing environment, and communicating the expected cost set from the processing resource to a cloud management system. Resource mapping of applications currently executing in the cloud computing environment are retrieved, and the application-specific task is assigned to a specific computational resource in the cloud computing environment based on the expected cost set and the resource mapping of applications currently executing in the cloud computing environment. A task to VM (virtual machine) assignment is determined based on the assignment of the application-specific task to the specific computational resource. The task to VM assignment is transferred from the cloud management system to the application executing on the processing resource in the cloud computing environment.

System for improving the performance of high performance computing applications on cloud using integrated load balancing

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New Delhi, India

Vaibhav Saxena