Abstract: An interface includes a first hardware register field to store respective chunks of a command directed to a device and respective chunks of a response to the command from the device. The interface also includes a second hardware register field to store a size of the command and a size of the response. The first and second hardware register fields are accessible by the device and by a processor external to the device that generates the command, in response to memory not being available to buffer the command and the response.

Abstract: An approach and a method for efficient execution of nested map-reduce framework workloads to take advantage of the combined execution of central processing units (CPUs) and graphics processing units (GPUs) and lower latency of data access in accelerated processing units (APUs) is described. In embodiments, metrics are generated to determine whether a map or reduce function is more efficiently processed on a CPU or a GPU. A first metric is based on ratio of a number of branch instructions to a number of non-branch instructions, and a second metric is based on the comparison of execution times on each of the CPU and the GPU. Selecting execution of map and reduce functions based on the first and second metrics result in accelerated computations. Some embodiments include scheduling pipelined executions of functions on the CPU and functions on the GPU concurrently to achieve power-efficient nested map reduce framework execution.

Abstract: The present disclosure relates to a method, system, and apparatus for configuring a computing system, such as a cloud computing system. A method includes, based on user selections received via a user interface, configuring a cluster of nodes by selecting the cluster of nodes from a plurality of available nodes, selecting a workload container module from a plurality of available workload container modules for operation on each node of the selected cluster of nodes, and selecting a workload for execution with the workload container on the cluster of nodes. Each node of the cluster of nodes includes at least one processing device and memory, and the cluster of nodes is operative to share processing of a workload.

Abstract: A system and method for efficient automatic scheduling of the execution of work units between multiple heterogeneous processor cores. A processing node includes a first processor core with a general-purpose micro-architecture and a second processor core with a single instruction multiple data micro-architecture. A computer program comprises one or more compute kernels, or function calls. A compiler computes pre-runtime information of the given function call. A runtime scheduler produces one or more work units by matching each of the one or more kernels with an associated record of data. The scheduler assigns work units either to the first or to the second processor core based at least in part on the computed pre-runtime information. In addition, the scheduler is able to change an original assignment for a waiting work unit based on dynamic runtime behavior of other work units corresponding to a same kernel as the waiting work unit.

Abstract: Systems and methods are provided that utilize non-shared page tables to allow an accelerator device to share physical memory of a computer system that is managed by and operates under control of an operating system. The computer system can include a multi-core central processor unit. The accelerator device can be, for example, an isolated core processor device of the multi-core central processor unit that is sequestered for use independently of the operating system, or an external device that is communicatively coupled to the computer system.

Abstract: A system and method for automatically migrating the execution of work units between multiple heterogeneous cores. A computing system includes a first processor core with a single instruction multiple data micro-architecture and a second processor core with a general-purpose micro-architecture. A compiler predicts execution of a function call in a program migrates at a given location to a different processor core. The compiler creates a data structure to support moving live values associated with the execution of the function call at the given location. An operating system (OS) scheduler schedules at least code before the given location in program order to the first processor core. In response to receiving an indication that a condition for migration is satisfied, the OS scheduler moves the live values to a location indicated by the data structure for access by the second processor core and schedules code after the given location to the second processor core.

Abstract: The present disclosure relates to a method, system, and apparatus for configuring a computing system, such as a cloud computing system. A method includes, based on user selections received via a user interface, configuring a cluster of nodes by selecting the cluster of nodes from a plurality of available nodes, selecting a workload container module from a plurality of available workload container modules for operation on each node of the selected cluster of nodes, and selecting a workload for execution with the workload container on the cluster of nodes. Each node of the cluster of nodes includes at least one processing device and memory, and the cluster of nodes is operative to share processing of a workload.

Abstract: The present disclosure relates to a method and system for configuring a computing system, such as a cloud computing system. A method includes selecting, based on a user selection received via a user interface, a workload for execution on a cluster of nodes of the computing system. The workload is selected from a plurality of available workloads including an actual workload and a synthetic test workload. The method further includes configuring the cluster of nodes of the computing system to execute the selected workload such that processing of the selected workload is distributed across the cluster of nodes. The synthetic test workload may be generated by a code synthesizer based on a set of user-defined workload parameters provided via a user interface that identify execution characteristics of the synthetic test workload.

Abstract: The present disclosure relates to a method and system for configuring a computing system, such as a cloud computing system. A method includes selecting a cluster of nodes for the computing system from a plurality of available nodes coupled to a communication network based on a comparison of a communication network configuration of an emulated node cluster and an actual communication network configuration of the plurality of available nodes. The method further includes modifying a network configuration of at least one node of a cluster of nodes to modify network performance of the at least one node on a communication network coupled to the cluster of nodes.

Abstract: The present disclosure relates to a method and system for configuring a computing system, such as a cloud computing system. A method includes determining, based on a shared execution of a workload by a cluster of nodes of the computing system, that at least one node of the cluster of nodes operated at less than a threshold operating capacity during the shared execution of the workload. The method further includes selecting a modified hardware configuration of the cluster of nodes based on the determining such that the cluster of nodes with the modified hardware configuration has at least one of a reduced computing capacity and a reduced storage capacity.

Abstract: The present disclosure relates to a method and system for configuring a computing system, such as a cloud computing system. A method includes providing a user interface comprising selectable boot-time configuration data and selecting, based on at least one user selection of the boot-time configuration data, a boot-time configuration for at least one node of a cluster of nodes of the computing system. The method further includes configuring the at least one node of the cluster of nodes with the selected boot-time configuration to modify at least one boot-time parameter of the at least one node.

Abstract: The present disclosure relates to a method and system for configuring a computing system, such as a cloud computing system. A method includes initiating a plurality of executions of a workload on a cluster of nodes based on a plurality of different sets of configuration parameters. The configuration parameters include at least one of an operational parameter of a workload container, a boot-time parameter of at least one node, and a hardware configuration parameter of at least one node. A set of configuration parameters is selected for the cluster of nodes from the plurality of different sets of configuration parameters based on a comparison of at least one performance characteristic of the cluster of nodes monitored during each execution of the workload and at least one desired performance characteristic. The workload is provided to the cluster of nodes for execution.

Abstract: The present disclosure relates to a method and system for configuring a computing system, such as a cloud computing system. A method includes initiating a hardware performance assessment test on a group of available nodes to obtain actual hardware performance characteristics of the group of available nodes. The method further includes selecting a subset of nodes for the computing system from the group of available nodes based on a comparison of the actual hardware performance characteristics of the group of available nodes and desired hardware performance characteristics.

Abstract: A system and method for automatically optimizing parallel execution of multiple work units in a processor by reducing a number of branch instructions. A computing system includes a first processor core with a general-purpose micro-architecture and a second processor core with a same instruction multiple data (SIMD) micro-architecture. A compiler detects and evaluates branches within function calls with one or more records of data used to determine one or more outcomes. Multiple compute sub-kernels are generated, each comprising code from the function corresponding to a unique outcome of the branch. Multiple work units are produced by assigning one or more records of data corresponding to a given outcome of the branch to one of the multiple compute sub-kernels associated with the given outcome. The branch is removed. An operating system scheduler schedules each of the one or more compute sub-kernels to the first processor core or to the second processor core.

Abstract: A system and method for automatically migrating the execution of work units between multiple heterogeneous cores. A computing system includes a first processor core with a single instruction multiple data micro-architecture and a second processor core with a general-purpose micro-architecture. A compiler predicts execution of a function call in a program migrates at a given location to a different processor core. The compiler creates a data structure to support moving live values associated with the execution of the function call at the given location. An operating system (OS) scheduler schedules at least code before the given location in program order to the first processor core. In response to receiving an indication that a condition for migration is satisfied, the OS scheduler moves the live values to a location indicated by the data structure for access by the second processor core and schedules code after the given location to the second processor core.

Abstract: A system and method for efficient automatic scheduling of the execution of work units between multiple heterogeneous processor cores. A processing node includes a first processor core with a general-purpose micro-architecture and a second processor core with a single instruction multiple data micro-architecture. A computer program comprises one or more compute kernels, or function calls. A compiler computes pre-runtime information of the given function call. A runtime scheduler produces one or more work units by matching each of the one or more kernels with an associated record of data. The scheduler assigns work units either to the first or to the second processor core based at least in part on the computed pre-runtime information. In addition, the scheduler is able to change an original assignment for a waiting work unit based on dynamic runtime behavior of other work units corresponding to a same kernel as the waiting work unit.

Abstract: A system and method for allocating memory to multi-threaded programs on a Non-Uniform Memory Access (NUMA) computer system using a NUMA-aware memory heap manager is disclosed. In embodiments, a NUMA-aware memory heap manager may attempt to maximize the locality of memory allocations in a NUMA system by allocating memory blocks that are near, or on the same node, as the thread that requested the memory allocation. A heap manager may keep track of each memory block's location and satisfy allocation requests by determining an allocation node dependent, at least in part, on its locality to that of the requesting thread. When possible, a heap manger may attempt to allocate memory on the same node as the requesting thread. The heap manager may be non-application-specific, may employ multiple levels of free block caching, and/or may employ various listings that associate given memory blocks with each NUMA node.

Abstract: Systems and methods are provided that utilize shared page tables to allow an accelerator device to share physical memory of a computer system that is managed by and operates under control of an operating system. The computer system can include a multi-core central processor unit. The accelerator device can be, for example, an isolated core processor device of the multi-core central processor unit that is sequestered for use independently of the operating system, or an external device that is communicatively coupled to the computer system.

Abstract: Systems and methods are provided that utilize non-shared page tables to allow an accelerator device to share physical memory of a computer system that is managed by and operates under control of an operating system. The computer system can include a multi-core central processor unit. The accelerator device can be, for example, an isolated core processor device of the multi-core central processor unit that is sequestered for use independently of the operating system, or an external device that is communicatively coupled to the computer system.

Abstract: Techniques for utilizing processor cores include sequestering processor cores for use independently from an operating system. In at least one embodiment of the invention, a method includes executing an operating system on a first subset of cores including one or more cores of a plurality of cores of a computer system. The operating system executes as a guest under control of a virtual machine monitor. The method includes executing work for an application on a second subset of cores including one or more cores of the plurality of cores. The first and second subsets of cores are mutually exclusive and the second subset of cores is not visible to the operating system. In at least one embodiment, the method includes sequestering the second subset of cores from the operating system.