Abstract: A distributed computing system may incorporate an implementation based on a codelet-based execution model, where a codelet is a high-level dataflow element. In addition to supporting the use of codelets, the system may further provide support for “datalets,” which are an extension of codelets providing better built-in support for static dataflow programming. Such a distributed computing system, implementing computing based on such codelets, may incorporate an implementation of an execution model, locality management schemes, scheduling schemes, a type system, and/or management of heterogeneous systems.

Abstract: The present invention, known as runspace, relates to the field of computing system management, data processing and data communications, and specifically to synergistic methods and systems which provide resource-efficient computation, especially for decomposable many-component tasks executable on multiple processing elements, by using a metric space representation of code and data locality to direct allocation and migration of code and data, by performing analysis to mark code areas that provide opportunities for runtime improvement, and by providing a low-power, local, secure memory management system suitable for distributed invocation of compact sections of code accessing local memory. Runspace provides mechanisms supporting hierarchical allocation, optimization, monitoring and control, and supporting resilient, energy efficient large-scale computing.

Abstract: The present invention, known as runspace, relates to the field of computing system management, data processing and data communications, and specifically to synergistic methods and systems which provide resource-efficient computation, especially for decomposable many-component tasks executable on multiple processing elements, by using a metric space representation of code and data locality to direct allocation and migration of code and data, by performing analysis to mark code areas that provide opportunities for runtime improvement, and by providing a low-power, local, secure memory management system suitable for distributed invocation of compact sections of code accessing local memory. Runspace provides mechanisms supporting hierarchical allocation, optimization, monitoring and control, and supporting resilient, energy efficient large-scale computing.

Abstract: Codeletset methods and/or apparatus may be used to enable resource-efficient computing. Such methods may involve decomposing a program into sets of codelets that may be allocated among multiple computing elements, which may enable parallelism and efficient use of the multiple computing elements. Allocation may be based, for example, on efficiencies with respect to data dependencies and/or communications among codelets.

Abstract: A distributed computing system may incorporate an implementation of an execution model, locality management schemes, scheduling schemes, a type system, and/or management of heterogeneous systems.

Abstract: The present invention, known as runspace, relates to the field of computing system management, data processing and data communications, and specifically to synergistic methods and systems which provide resource-efficient computation, especially for decomposable many-component tasks executable on multiple processing elements, by using a metric space representation of code and data locality to direct allocation and migration of code and data, by performing analysis to mark code areas that provide opportunities for runtime improvement, and by providing a low-power, local, secure memory management system suitable for distributed invocation of compact sections of code accessing local memory. Runspace provides mechanisms supporting hierarchical allocation, optimization, monitoring and control, and supporting resilient, energy efficient large-scale computing.

Abstract: Node-centric checkpointing may be used in a multi-node computing system to provide fault-tolerance. Such checkpointing may involve storage of input and/or output data prior to and/or after execution of a task on a node.