Abstract: Methods, apparatus, and products are disclosed for thread selection during context switching on a plurality of compute nodes that includes: executing, by a compute node, an application using a plurality of threads of execution, including executing one or more of the threads of execution; selecting, by the compute node from a plurality of available threads of execution for the application, a next thread of execution in dependence upon power characteristics for each of the available threads; determining, by the compute node, whether criteria for a thread context switch are satisfied; and performing, by the compute node, the thread context switch if the criteria for a thread context switch are satisfied, including executing the next thread of execution.

Abstract: Methods, systems, and products are disclosed for optimizing just-in-time (‘JIT’) compiling for a Java application executing on a compute node, the compute node having installed upon it a Java Virtual Machine (‘JVM’) capable of supporting the Java application, that include: identifying, by an application manager, a particular portion of the Java application; assigning, by the application manager, a JIT level to the particular portion of the Java application; and jitting, by the JVM installed on the compute node, the particular portion of the Java application in dependence upon the JIT level assigned to that particular portion of the Java application.

Abstract: Methods, systems, and products are disclosed for optimizing just-in-time (‘JIT’) compiling for a Java application executing on a compute node, the compute node having installed upon it a Java Virtual Machine (‘JVM’) capable of supporting the Java application, that include: identifying, by an application manager, a particular portion of the Java application; assigning, by the application manager, a JIT level to the particular portion of the Java application; and jitting, by the JVM installed on the compute node, the particular portion of the Java application in dependence upon the JIT level assigned to that particular portion of the Java application.

Abstract: A database query optimizer optimizes a query that uses multiple networks. The query optimizer optimizes a query to reduce network traffic on a network or node that is overloaded or above an established parameter in a node/network attribute table. The query optimization to reduce network traffic may result in a sub-optimal query in other respects such as execution time. The result is a query optimizer that rewrites or optimizes a query to execute on multiple nodes or networks to reduce traffic on a network or node according to the loading characteristics and assigned attributes of a node or network.

Abstract: A database query optimizer optimizes a query that uses multiple networks. The query optimizer optimizes a query to reduce network traffic on a network or node that is overloaded or above an established parameter in a node/network attribute table. The query optimization to reduce network traffic may result in a sub-optimal query in other respects such as execution time. The result is a query optimizer that rewrites or optimizes a query to execute on multiple nodes or networks to reduce traffic on a network or node according to the loading characteristics and assigned attributes of a node or network.

Abstract: A database query optimizer optimizes a query that uses multiple networks. The database query optimizer optimizes a query that uses multiple networks to satisfy the query by splitting the query execution to use multiple networks. Thus, the query optimizer rewrites or optimizes a query to execute on multiple nodes or networks to more efficiently execute the query and reduce network traffic on a network. The query optimizer uses plan cache statistics to determine whether to use multiple networks to optimize the query.

Abstract: Methods, apparatus, and products are disclosed for balancing a data processing load among a plurality of compute nodes in a parallel computer that include: partitioning application data for processing on the plurality of compute nodes into data chunks; receiving, by each compute node, at least one of the data chunks for processing; estimating, by each compute node, processing time involved in processing the data chunks received by that compute node for processing; and redistributing, by at least one of the compute nodes to at least one of the other compute nodes, a portion of the data chunks received by that compute node in dependence upon the processing time estimated by that compute node.

Abstract: A database query execution monitor determines if a network error or low performance condition exists and then where possible modifies the query. The query execution monitor then determines an alternate query execution plan to continue execution of the query. The query optimizer can re-optimize the query to use a different network or node. Thus, the query execution monitor allows autonomic error recovery for network failures using an alternate query execution. The alternate query execution could also be determined at the initial optimization time and then this alternate plan used to execute a query in the case of a particular network failure.

Abstract: A database query execution monitor determines if an network error or low performance condition exists and then where possible modifies the query. The query execution monitor then determines an alternate query execution plan to continue execution of the query. The query optimizer can re-optimize the query to use a different network or node. Thus, the query execution monitor allows autonomic error recovery for network failures using an alternate query execution. The alternate query execution could also be determined at the initial optimization time and then this alternate plan used to execute a query in the case of a particular network failure.

Abstract: A database query optimizer optimizes a query that uses multiple networks. The database query optimizer optimizes a query that uses multiple networks to satisfy the query by splitting the query execution to use multiple networks. Thus, the query optimizer rewrites or optimizes a query to execute on multiple nodes or networks to more efficiently execute the query and reduce network traffic on a network. The query optimizer uses plan cache statistics to determine whether to use multiple networks to optimize the query.