Abstract: Embodiments include methods, systems and computer program products for providing an extendable job structure for executing instructions on an accelerator. The method includes creating a number of data descriptor blocks, each memory location addresses and a pointer to a next of the number of the data descriptor block. The method further includes creating a last data descriptor block having memory location addresses and a last block indicator. Based on determining that additional memory is required for executing instructions on the accelerator, the method includes modifying the last data descriptor block to become a data extender block having a pointer to one of one or more new data descriptor blocks and creating a new last data descriptor block.

Abstract: Embodiments include methods, systems and computer program products for providing an extendable job structure for executing instructions on an accelerator. The method includes creating a number of data descriptor blocks, each memory location addresses and a pointer to a next of the number of the data descriptor block. The method further includes creating a last data descriptor block having memory location addresses and a last block indicator. Based on determining that additional memory is required for executing instructions on the accelerator, the method includes modifying the last data descriptor block to become a data extender block having a pointer to one of one or more new data descriptor blocks and creating a new last data descriptor block.

Abstract: A data processing system includes a plurality of virtual machines each having associated memory pages; a shared memory page cache that is accessible by each of the plurality of virtual machines; and a global hash map that is accessible by each of the plurality of virtual machines. The data processing system is configured such that, for a particular memory page stored in the shared memory page cache that is associated with two or more of the plurality of virtual machines, there is a single key stored in the global hash map that identifies at least a storage location in the shared memory page cache of the particular memory page. The system can be embodied at least partially in a cloud computing system.

Abstract: Booking information including destination and origin and specifying a desired multi-modal freight shipment is obtained from a user; based on same and on route information from a carrier database, a plurality of feasible multi-modal routes for the desired freight shipment are generated with a route enumeration module. Based on cost information from the carrier database, cost for each of the feasible multi-modal routes is computed with a cost estimation sub-module of a metric computation module. Based on transit time information from the carrier database, transit time for each of the feasible multi-modal routes is computed with a transit time estimation sub-module of the metric computation module. Based on the cost for each of the feasible multi-modal routes and the transit time for each of the feasible multi-modal routes, multi-objective optimization under uncertainty is carried out with an optimization module, to obtain one or more preferred feasible multi-modal routes.

Abstract: Booking information including destination and origin and specifying a desired multi-modal freight shipment is obtained from a user; based on same and on route information from a carrier database, a plurality of feasible multi-modal routes for the desired freight shipment are generated with a route enumeration module. Based on cost information from the carrier database, cost for each of the feasible multi-modal routes is computed with a cost estimation sub-module of a metric computation module. Based on transit time information from the carrier database, transit time for each of the feasible multi-modal routes is computed with a transit time estimation sub-module of the metric computation module. Based on the cost for each of the feasible multi-modal routes and the transit time for each of the feasible multi-modal routes, multi-objective optimization under uncertainty is carried out with an optimization module, to obtain one or more preferred feasible multi-modal routes.

Abstract: Predicting acceleration in a hybrid system may comprise determining a number of cross system calls in a first host-accelerator computer architecture running a workload. Host machine overhead and accelerator overhead in the first host-accelerator computer architecture associated with each of the cross system calls may be determined. Communication delay associated with each of the cross system calls in the first host-accelerator computer architecture running a workload may be determined. An application response time may be predicted for a candidate application to be run in a second host-accelerator computer architecture, based at least on the determined host machine overhead, the accelerator overhead, and the communication delay associated with each of the cross system calls in the first host-accelerator computer architecture running a workload.

Abstract: A method for scaling a cloud infrastructure, comprises receiving at least one of resource-level metrics and application-level metrics, estimating parameters of at least one application based on the received metrics, automatically and dynamically determining directives for scaling application deployment based on the estimated parameters, and providing the directives to a cloud service provider to execute the scaling.

Abstract: A method for scaling a cloud infrastructure, comprises receiving at least one of resource-level metrics and application-level metrics, estimating parameters of at least one application based on the received metrics, automatically and dynamically determining directives for scaling application deployment based on the estimated parameters, and providing the directives to a cloud service provider to execute the scaling.

Abstract: Embodiments include methods, systems and computer program products for providing an extendable job structure for executing instructions on an accelerator. The method includes creating a number of data descriptor blocks, each having a fixed number of memory location addresses and a pointer to a next of the number of the data descriptor block. The method further includes creating a last data descriptor block having the fixed number of memory location addresses and a last block indicator. Based on determining that additional memory is required for executing instructions on the accelerator, the method includes modifying the last data descriptor block to become a data extender block having a pointer to one of one or more new data descriptor blocks and creating a new last data descriptor block.

Abstract: Embodiments include methods, systems and computer program products for offloading multiple processing operations to an accelerator. Aspects include receiving a database query from an application, performing an analysis on the query, and identifying a plurality of available accelerators. Aspects further include retrieving cost information for one or more templates available on each of the plurality of available accelerators, determining a query execution plan based on the cost information and the analysis on the query, and offloading one or more query operations to at least one of the plurality of accelerators based on the query execution plan.

Abstract: A data processing system includes a plurality of virtual machines each having associated memory pages; a shared memory page cache that is accessible by each of the plurality of virtual machines; and a global hash map that is accessible by each of the plurality of virtual machines. The data processing system is configured such that, for a particular memory page stored in the shared memory page cache that is associated with two or more of the plurality of virtual machines, there is a single key stored in the global hash map that identifies at least a storage location in the shared memory page cache of the particular memory page. The system can be embodied at least partially in a cloud computing system.

Abstract: In an exemplary embodiment of this disclosure, a computer-implemented method includes determining that a database query warrants a first projection operation to project a plurality of input rows to a plurality of projected rows, where each of the plurality of input rows has one or more variable-length columns. A first projection control block is constructed, by a computer processor, to describe the first projection operation. The first projection operation is offloaded to a hardware accelerator. The first projection control block is provided to the hardware accelerator, and the first projection control block enables the hardware accelerator to perform the first projection operation at streaming rate.

Abstract: A method for scaling a cloud infrastructure, comprises receiving at least one of resource-level metrics and application-level metrics, estimating parameters of at least one application based on the received metrics, automatically and dynamically determining directives for scaling application deployment based on the estimated parameters, and providing the directives to a cloud service provider to execute the scaling.

Abstract: A method for scaling a cloud infrastructure, comprises receiving at least one of resource-level metrics and application-level metrics, estimating parameters of at least one application based on the received metrics, automatically and dynamically determining directives for scaling application deployment based on the estimated parameters, and providing the directives to a cloud service provider to execute the scaling.

Abstract: In an approach for scheduling the execution of a workload in a computing environment, a computer receives a request for scheduling execution of a computing job, wherein the computing job includes a plurality of computing tasks to be executed in a sequence, and wherein at least one computing task requires access to a set of data. The computer identifies information related to the computing environment, wherein the information comprises at least processors available to execute each computing task of the plurality of computing tasks and storage device proximity to the processors. The computer determines an execution configuration for the computing job based, at least in part, on the received request, the information related to the computing environment, and current utilization of the processors' resources. The computer schedules execution of the execution configuration for the computing job.

Abstract: Methods and arrangements for dynamic memory management. Data are accepted for thread local storage, and memory usage is monitored in thread local storage. A memory block is allocated to thread local storage for storing accepted data, based on the monitored memory usage.

Abstract: In some embodiments, a query recipient is configured to determine that a database query warrants a first projection operation to project a plurality of input rows to a plurality of projected rows. Each of the input rows has one or more variable-length columns. A projection controller is configured to construct a first projection control block to describe the first projection operation. For this construction, the projection controller is configured to construct a plurality of projection control elements, each one corresponding to a corresponding column in the input rows, and a header to specify the order of the projection control elements. The projection controller is further configured to offload the first projection operation to a hardware accelerator, and to provide the first projection control block to the hardware accelerator, where the first projection control block enables the hardware accelerator to perform the first projection operation at streaming rate.

Abstract: Methods and arrangements for mapping a query operation to an accelerator are provided. The method includes receiving, by a processor, a query operation and determining the design logic of the query operation, receiving a configuration of one or more available accelerators and a design logic of each of the one or more available accelerators, and determining if the query operation can be offloaded to one or more of the one or more available accelerators. Based on a determination that the query operation can be offloaded to one or more of the one or more available accelerators, the method also includes creating software structures to interface with a selected accelerator and executing the query operation on the selected accelerator. Based on a determination that the query operation cannot be offloaded to one or more of the one or more available accelerators, the method further includes executing the query operation in software.

Abstract: In an exemplary embodiment of this disclosure, a computer-implemented method includes determining that a database query warrants a first projection operation to project a plurality of input rows to a plurality of projected rows, where each of the plurality of input rows has one or more variable-length columns. A first projection control block is constructed, by a computer processor, to describe the first projection operation. The first projection operation is offloaded to a hardware accelerator. The first projection control block is provided to the hardware accelerator, and the first projection control block enables the hardware accelerator to perform the first projection operation at streaming rate.

Abstract: A method for configuring a large hybrid memory subsystem having a large cache size in a computing system where one or more performance metrics of the computing system are expressed as an explicit function of configuration parameters of the memory subsystem and workload parameters of the memory subsystem. The computing system hosts applications that utilize the memory subsystem, and the performance metrics cover the use of the memory subsystem by the applications. A performance goal containing values for the performance metric is identified for the computing system. These values for the performance metrics are used in the explicit function of performance metrics, configuration parameters and workload parameters to calculate values for the configuration parameters that achieve the identified performance goal. The calculated values of the configuration parameters are implemented in the memory subsystem.