Abstract: A power management system may provide power management recommendations to a computer system including a plurality of computing nodes (which may include processors, etc.), to cause the computing nodes to individually or collectively adjust power states or modes of respective processors to achieve power management of the computer system. The power management system may be provided with a power management framework that continuously utilizes direct and indirect service-level feedbacks to guide power management decisions. The power management system may employ a reinforcement learning algorithm to make power management decisions at a user level, and provide a fast decision overriding mechanism for platform events or service-requested performance boosts.

Abstract: A power management system may provide power management recommendations to a computer system including a plurality of computing nodes (which may include processors, etc.), to cause the computing nodes to individually or collectively adjust power states or modes of respective processors to achieve power management of the computer system. The power management system may be provided with a power management framework that continuously utilizes direct and indirect service-level feedbacks to guide power management decisions. The power management system may employ a reinforcement learning algorithm to make power management decisions at a user level, and provide a fast decision overriding mechanism for platform events or service-requested performance boosts.

Abstract: A method and system of auto parallelization of zero-trip loops that substitutes a nested basic linear induction variable by exploiting a parallelizing compiler is provided. Provided is a use of a max{0,N} variable for loop iterations in case of no information is known about the value of N, for a typical loop iterating from 1 to N, in which N is the loop invariant. For the nested basic induction variables, an induction variable substitution process is applied to the nested loops starting from the innermost loop to the outermost one. Then a removal of the max operator afterwards through a copy propagation pass of the IBM compiler is provided. In doing so, the loop dependency on the induction variable is eliminated and an opportunity for a parallelizing compiler to parallel the outermost loop is provided.

Abstract: A method for providing parallel processing capabilities including: performing scalar and array privatization analysis via a compiler; checking whether an assignment statement is reducible; recognizing reduction patterns through a pattern matching algorithm; classifying a reduction type of each of the reduction patterns; and performing transformations and code generation for each reduction the reduction type of each of the reduction patterns.

Abstract: A method and system of auto parallelization of zero-trip loops that substitutes a nested basic linear induction variable by exploiting a parallelizing compiler is provided. Provided is a use of a max{0,N} variable for loop iterations in case of no information is known about the value of N, for a typical loop iterating from 1 to N, in which N is the loop invariant. For the nested basic induction variables, an induction variable substitution process is applied to the nested loops starting from the innermost loop to the outermost one. Then a removal of the max operator afterwards through a copy propagation pass of the IBM compiler is provided. In doing so, the loop dependency on the induction variable is eliminated and an opportunity for a parallelizing compiler to parallel the outermost loop is provided.

Abstract: A method and system of auto parallelization of zero-trip loops that substitutes a nested basic linear induction variable by exploiting a parallelizing compiler is provided. Provided is a use of a max{0,N} variable for loop iterations in case of no information is known about the value of N, for a typical loop iterating from 1 to N, in which N is the loop invariant. For the nested basic induction variables, an induction variable substitution process is applied to the nested loops starting from the innermost loop to the outermost one. Then a removal of the max operator afterwards through a copy propagation pass of the IBM compiler is provided. In doing so, the loop dependency on the induction variable is eliminated and an opportunity for a parallelizing compiler to parallel the outermost loop is provided.

Abstract: A method for providing parallel processing capabilities including: performing scalar and array privatization analysis via a compiler; checking whether an assignment statement is reducible; recognizing reduction patterns through a pattern matching algorithm; classifying a reduction type of each of the reduction patterns; and performing transformations and code generation for each reduction the reduction type of each of the reduction patterns.

Abstract: A method and system of auto parallelization of zero-trip loops that substitutes a nested basic linear induction variable by exploiting a parallelizing compiler is provided. Provided is a use of a max{0,N} variable for loop iterations in case of no information is known about the value of N, for a typical loop iterating from 1 to N, in which N is the loop invariant. For the nested basic induction variables, an induction variable substitution process is applied to the nested loops starting from the innermost loop to the outermost one. Then a removal of the max operator afterwards through a copy propagation pass of the IBM compiler is provided. In doing so, the loop dependency on the induction variable is eliminated and an opportunity for a parallelizing compiler to parallel the outermost loop is provided.