Abstract: In a networked computer system that includes multiple nodes and multiple networks interconnecting the nodes, a database query optimizer takes network choice into consideration when optimizing a query. The query optimizer logs historical information for queries that includes network information. A network monitor determines network characteristics of the multiple networks. The query optimizer then selects a network for a query based on the network characteristics, and optionally based on the historical information as well. The result is a query optimizer that takes network choice into consideration, thereby providing a query that is optimized according to the characteristics of the networks.

Abstract: In a networked computer system that includes multiple nodes and multiple networks interconnecting the nodes, a database query optimizer takes network choice into consideration when optimizing a query. The query optimizer logs historical information for queries that includes network information. A network monitor determines network characteristics of the multiple networks. The query optimizer then selects a network for a query based on the network characteristics, and optionally based on the historical information as well. The result is a query optimizer that takes network choice into consideration, thereby providing a query that is optimized according to the characteristics of the networks.

Abstract: Embodiments of the invention provide for controlling message flow across a parallel computer system having multiple compute nodes by selectively grouping compute nodes of such a system into node pools and assigning message flow control policies to nodes in the node pools. The message flow control policies specify logging and/or tracing activities to be performed by instances of applications running on nodes assigned to the node pools. As the application is executed, logging and/or tracing messages are generated on the compute nodes according to message flow control policies assigned to the nodes. Optionally, the message flow is analyzed, the message flow control policies are adjusted, and duplicate messages are eliminated.

Abstract: Efficient application checkpointing uses checkpointing characteristics of a job to determine how to schedule jobs for execution on a multi-node computer system. A checkpoint profile in the job description includes information on the expected frequency and duration of a check point cycle for the application. The checkpoint profile may be based on a user/administrator input as well as historical information. The job scheduler will attempt to group applications (jobs) that have the same checkpoint profile, on the same nodes or group of nodes. Additionally, the job scheduler may control when new jobs start based on when the next checkpoint cycle(s) are expected. The checkpoint monitor will monitor the checkpoint cycles, updating the checkpoint profiles of running jobs. The checkpoint monitor will also keep track of an overall system checkpoint profile to determine the available checkpointing capacity before scheduling jobs on the cluster.

Abstract: Methods, systems, and products are disclosed for configuring an application for execution on a parallel computer that include: booting up a first subset of a plurality of nodes in a serial processing mode; booting up a second subset of the plurality of nodes in a parallel processing mode; profiling, prior to application deployment on the parallel computer, the application to identify the serial segments and the parallel segments of the application; and deploying the application for execution on the parallel computer in dependence upon the profile of the application and proximity within the data communications network of the nodes in the first subset relative to the nodes in the second subset.

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 loader loads data to an in-memory database across multiple nodes in a parallel computing system. The database loader uses SQL flags, historical information gained from monitoring prior query execution times and patterns, and node and network configuration to determine how to effectively cluster data attributes across multiple nodes. The database loader may also allow a system administrator to force placement of database structures in particular nodes.

Abstract: A database loader loads data to an in-memory database across multiple nodes in a parallel computing system. The database loader uses SQL flags, historical information gained from monitoring prior query execution times and patterns, and node and network configuration to determine how to effectively cluster data attributes across multiple nodes. The database loader may also allow a system administrator to force placement of database structures in particular nodes.

Abstract: I/O nodes are dynamically distributed on a multi-node computing system. An I/O configuration mechanism located in the service node of a multi-node computer system controls the distribution of the I/O nodes. The I/O configuration mechanism uses job information located in a job record to initially configure the I/O node distribution. The I/O configuration mechanism further monitors the I/O performance of the executing job to then dynamically adjusts the I/O node distribution based on the I/O performance of the executing job.

Abstract: A method and apparatus performs peer-to-peer file transfers on a High Performance Computing (HPC) cluster such as a Beowulf cluster. A peer-to-peer file tracker (PPFT) allows operating system, application and data files to be moved from a pre-loaded node to another node of the HPC cluster. A peer-to-peer (PTP) client is loaded into the nodes to facilitate PTP file transfers to reduce loading on networks, network switches and file servers to reduce the time needed to load the nodes with these files to increase overall efficiency of the multi-node computing system. The selection of the nodes participating in file transfers can be based on network topology, network utilization, job status and predicted network/computer utilization. This selection can be dynamic, changing during the file transfers as resource conditions change. The policies used to choose resources can be configured by an administrator.

Abstract: Methods, systems, and products are provided for monitoring the temperature of a high powered computing component. The high powered computing component has a thermal sensor and the high powered computing component in thermal communication with a liquid cooled heatsink. Embodiments include determining, by a thermal monitoring module, a temperature of the thermal sensor; determining, by the thermal monitoring module, a temperature of the heatsink; determining, by the thermal monitoring module, a power delivered to the high powered computing component; and calculating, by the thermal monitoring module, a thermal value in dependence upon the temperature of the thermal sensor, the temperature of the heatsink, and the power delivered to the high powered computing component.

Abstract: Methods, systems, and products are provided for monitoring the temperature of a high powered computing component. The high powered computing component has a thermal sensor and the high powered computing component in thermal communication with a liquid cooled heatsink. Embodiments include determining, by a thermal monitoring module, a temperature of the thermal sensor; determining, by the thermal monitoring module, a temperature of the heatsink; determining, by the thermal monitoring module, a power delivered to the high powered computing component; and calculating, by the thermal monitoring module, a thermal value in dependence upon the temperature of the thermal sensor, the temperature of the heatsink, and the power delivered to the high powered computing component.

Abstract: A method, apparatus, and program product manage scheduling of a plurality of jobs in a parallel computing system of the type that includes a plurality of computing nodes and is disposed in a data center. The plurality of jobs are scheduled for execution on a group of computing nodes from the plurality of computing nodes based on the physical locations of the plurality of computing nodes in the data center. The group of computing nodes is further selected so as to distribute at least one of a heat load and an energy load within the data center. The plurality of jobs may be additionally scheduled based upon an estimated processing requirement for each job of the plurality of jobs.

Abstract: A hybrid node of a High Performance Computing (HPC) cluster uses accelerator nodes for checkpointing to increase overall efficiency of the multi-node computing system. The host node or processor node reads/writes checkpoint data to the accelerators. After offloading the checkpoint data to the accelerators, the host processor can continue processing while the accelerators communicate the checkpoint data with the host or wait for the next checkpoint. The accelerators may also perform dynamic compression and decompression of the checkpoint data to reduce the checkpoint size and reduce network loading. The accelerators may also communicate with other node accelerators to compare checkpoint data to reduce the amount of checkpoint data stored to the host.

Abstract: Methods, systems, and products are disclosed for executing a distributed JAVA application on a plurality of compute nodes. The JAVA application includes a plurality of jobs distributed among the plurality of compute nodes. The plurality of compute nodes are connected together for data communications through a data communication network. Each of the plurality of compute nodes has installed upon it a JAVA Virtual Machine (‘JVM’) capable of supporting at least one job of the JAVA application. Executing a distributed JAVA application on a plurality of compute nodes includes: tracking, by an application manager, JVM environment variables for the JVMs installed on the plurality of compute nodes; and configuring, by the application manager, the plurality of jobs for execution on the plurality of compute nodes in dependence upon the JVM environment variables for the JVMs installed on the plurality of compute nodes.

Abstract: Embodiments of the invention provide techniques for scheduling jobs on a multi-node computing system based on the predicted environmental impact of executing the jobs. In one embodiment, a plurality of job plans may be generated for processing a requested job on the multi-node computing system. The environmental impacts resulting from executing each job plan may be estimated by matching the job plans to stored data based on standardized executions of job plans. Further, environmental impacts may be estimated by matching the job plans to stored data based on actual environmental measurements obtained during prior executions of the job plan on the multi-node computer system. The job may be executed using a job plan selected based on predicted environmental impacts and time performance.

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 method and apparatus loads data to an in-memory database across multiple nodes in a parallel computing system. A database loader uses SQL flags, historical information gained from monitoring prior query execution times and patterns, and node and network configuration to determine how to effectively cluster data attributes across multiple nodes. The database loader may also allow a system administrator to force placement of database structures in particular nodes.