Abstract: A method, system and computer program product for generating sets of training programs for machine learning models. Fixed values of one or more workload metrics are received from a user, where the workload metrics correspond to low-level program features which define particular low-level application behavior. A profile using the fixed values of the workload metrics is then created. A suite of synthetic applications is generated using the created profile to form a set of training programs which target particular aspects of program behavior. A machine learning model is then trained using the set of training programs. Since the generated synthetic applications provide a broader coverage of the program state-space, the formed set of training programs more accurately targets performance behavior thereby improving the prediction accuracy of the machine learning based predictive models.

Abstract: A method, system and computer program product for load balancing of graph processing workloads. Synthetic proxy graphs are generated to characterize machines' graph processing speeds in a cluster. Each of the graph applications executing in the cluster is profiled using the synthetic graphs to form profiling sets. These formed profiling sets are run among the machines in the cluster to capture the machines' graph processing speeds. A metric for each of the graph applications is computed from a relative speedup among the machines in the cluster and/or the graph processing speeds. A graph file of a natural graph and a graph application are loaded. A metric out of the computed metrics is selected based on the graph application. The natural graph is then partitioned into multiple chunks which is distributed onto two or more machines in the cluster based on the selected metric and a user selected partitioning algorithm.

Abstract: A method, system and computer program product for generating sets of training programs for machine learning models. Fixed values of one or more workload metrics are received from a user, where the workload metrics correspond to low-level program features which define particular low-level application behavior. A profile using the fixed values of the workload metrics is then created. A suite of synthetic applications is generated using the created profile to form a set of training programs which target particular aspects of program behavior. A machine learning model is then trained using the set of training programs. Since the generated synthetic applications provide a broader coverage of the program state-space, the formed set of training programs more accurately targets performance behavior thereby improving the prediction accuracy of the machine learning based predictive models.

Abstract: A method, system and computer program product for load balancing of graph processing workloads. Synthetic proxy graphs are generated to characterize machines' graph processing speeds in a cluster. Each of the graph applications executing in the cluster is profiled using the synthetic graphs to form profiling sets. These formed profiling sets are run among the machines in the cluster to capture the machines' graph processing speeds. A metric for each of the graph applications is computed from a relative speedup among the machines in the cluster and/or the graph processing speeds. A graph file of a natural graph and a graph application are loaded. A metric out of the computed metrics is selected based on the graph application. The natural graph is then partitioned into multiple chunks which is distributed onto two or more machines in the cluster based on the selected metric and a user selected partitioning algorithm.