Abstract: Methods, systems, and devices for loading data in a tiered memory system are described. A respective allocation of computing resources may be determined for each node in a cluster, where at least one of the nodes may include multiple memory tiers, and a data set to be processed by the nodes may be analyzed. Based on the allocation of computing resources and the analysis of the data set, respective data processing instructions indicating respective portions of the data set to be processed by respective nodes may be generated and sent to the respective nodes. The respective data processing instructions may also indicate a respective distribution of subsets of the respective portions of the data set across the multiple memory tiers at the respective nodes.

Abstract: Various embodiments are provided for managing cryptographic bottlenecks for distributed multi-signature blockchain contracts in a computing environment. One or more cryptographic bottlenecks of cryptographic requests at a cryptographic accelerator may be resolved by switching between a blockchain node cryptographic library and an accelerator cryptographic library upon a number of the cryptographic requests at the accelerator exceeding a defined threshold.

Abstract: Various embodiments are provided for managing cryptographic bottlenecks for distributed multi-signature blockchain contracts in a computing environment. One or more cryptographic bottlenecks of cryptographic requests at a cryptographic accelerator may be resolved by switching between a blockchain node cryptographic library and an accelerator cryptographic library upon a number of the cryptographic requests at the accelerator exceeding a defined threshold.

Abstract: A method for query evaluation comprises receiving a query over a set of distributed data sources, decomposing the query into a set of sub-queries of the query, evaluating each sub-query in the set of sub-queries with respect to each data source in the set of distributed data sources, wherein evaluating comprises determining which data sources in the set of distributed data sources are capable of answering each sub-query and at what cost, computing a set of distributed plans by composing one or more of the sub-queries in one or more of the data sources, evaluating each plan in the set of distributed plans, selecting a sub-set of plans from the set of distributed plans to be executed for responding to the query, executing the selected sub-set of plans, and returning results of the query.

Abstract: A method for query evaluation comprises receiving a query over a set of distributed data sources, decomposing the query into a set of sub-queries of the query, evaluating each sub-query in the set of sub-queries with respect to each data source in the set of distributed data sources, wherein evaluating comprises determining which data sources in the set of distributed data sources are capable of answering each sub-query and at what cost, computing a set of distributed plans by composing one or more of the sub-queries in one or more of the data sources, evaluating each plan in the set of distributed plans, selecting a sub-set of plans from the set of distributed plans to be executed for responding to the query, executing the selected sub-set of plans, and returning results of the query.

Abstract: A graphics processing unit controller configured to be communicatively coupled to one or more graphics processing units and one or more virtual machines, comprising: a unit collection module which is configured to store information regarding the available resources of one or more graphics processing units; and a scheduler module configured to allocate at least part of the one or more graphics processing units to the execution of a compute kernel in response to receipt of a request for the execution of the compute kernel, wherein the scheduler is configured to allocate at least part of the one or more graphics processing units based on a comparison between the available resources of the one or more graphics processing units and required resources for the execution of the compute kernel.

Abstract: A method for query evaluation comprises receiving a query over a set of distributed data sources, decomposing the query into a set of sub-queries of the query, evaluating each sub-query in the set of sub-queries with respect to each data source in the set of distributed data sources, wherein evaluating comprises determining which data sources in the set of distributed data sources are capable of answering each sub-query and at what cost, computing a set of distributed plans by composing one or more of the sub-queries in one or more of the data sources, evaluating each plan in the set of distributed plans, selecting a sub-set of plans from the set of distributed plans to be executed for responding to the query, executing the selected sub-set of plans, and returning results of the query.

Abstract: A method for query evaluation comprises receiving a query over a set of distributed data sources, decomposing the query into a set of sub-queries of the query, evaluating each sub-query in the set of sub-queries with respect to each data source in the set of distributed data sources, wherein evaluating comprises determining which data sources in the set of distributed data sources are capable of answering each sub-query and at what cost, computing a set of distributed plans by composing one or more of the sub-queries in one or more of the data sources, evaluating each plan in the set of distributed plans, selecting a sub-set of plans from the set of distributed plans to be executed for responding to the query, executing the selected sub-set of plans, and returning results of the query.

Abstract: A graphics processing unit controller, host system, and methods A graphics processing unit controller configured to be communicatively coupled to one or more graphics processing units and one or more virtual machines, the controller comprising: a scheduler module configured to allocate at least part of one or more graphics processing units to the execution of a compute kernel in response to receipt of a request for the execution of the compute kernel during the running of an application by a virtual machine.

Abstract: A graphics processing unit controller configured to be communicatively coupled to one or more graphics processing units and one or more virtual machines, comprising: a unit collection module which is configured to store information regarding the available resources of one or more graphics processing units; and a scheduler module configured to allocate at least part of the one or more graphics processing units to the execution of a compute kernel in response to receipt of a request for the execution of the compute kernel, wherein the scheduler is configured to allocate at least part of the one or more graphics processing units based on a comparison between the available resources of the one or more graphics processing units and required resources for the execution of the compute kernel.

Abstract: A system and method for scheduling client-server applications onto heterogeneous clusters includes storing at least one client request of at least one application in a pending request list on a computer readable storage medium. A priority metric is computed for each application, where the computed priority metric is applied to each client request belonging to that application. The priority metric is determined based on estimated performance of the client request and load on the pending request list. The at least one client request of the at least one application is scheduled based on the priority metric onto one or more heterogeneous resources.

Abstract: A system and method for scheduling client-server applications onto heterogeneous clusters includes storing at least one client request of at least one application in a pending request list on a computer readable storage medium. A priority metric is computed for each application, where the computed priority metric is applied to each client request belonging to that application. The priority metric is determined based on estimated performance of the client request and load on the pending request list. The at least one client request of the at least one application is scheduled based on the priority metric onto one or more heterogeneous resources.