Abstract: Machine-learned ranking algorithms, e.g. for ranking search results, often use a sequence of decision trees involving decision nodes based on threshold values of features. Modules, systems and methods of optimizing such algorithms involve analyzing threshold feature values to determine threshold intervals for each feature and grouping decision trees according to the feature used in a root decision node. Then coalescing the decision trees within each group to form a coalesced group tree for each group and finally coalescing the coalesced group trees to form a coalesced tree that implements the algorithm.

Abstract: A system for serving advertisements determines the frequency of occurrence for each parameter of a plurality of parameters associated with a plurality of search queries. The plurality of parameters are associated with one or more advertisements. The system stores at least some parameters to a first storage based on the frequency of occurrence of the parameters. The system stores the other parameters to a second storage that has a higher latency than the first storage. When serving advertisements, the system ranks advertisements for delivery based on the parameters stored in the first storage device and the second storage.

Abstract: According to one example embodiment, there is disclosed herein uses partial recurrence relaxation for parallelizing DOACROSS loops on multi-core computer architectures. By one example definition, a DOACROSS may be a loop that allows successive iterations executing by overlapping; that is, all iterations must impose a partial execution order. According to one embodiment, the inventive subject matter may be used to transform the dependence structure of a given loop with recurrences for maximal degree of thread-level parallelism (TLP), where the threads can be mapped on to either different logical processors (in a hyperthreaded processor) or can be mapped onto different physical cores (or processors) in a multi-core processor.

Abstract: An advanced system and method for optimizing selection of online advertisements is provided. Decision trees with expressions to evaluate feature values for advertisements may be received, and a decision tree similarity matrix of decision tree similarity values between pairs of decision trees may be generated that represent the number of common features between two decision trees. The edges of the decision tree similarity matrix may be sorted in non-increasing order by edge value, and the decision trees of each edge retrieved from the sorted order may be placed in an optimized sequence order for evaluation. In response to a request to serve advertisements, advertisements may be scored by evaluating the decision trees of advertisements in the optimized sequence order. The advertisements may then be ranked in descending order by score, and advertisement with the highest scores may be sent for display.

Abstract: A method and a system are provided for enabling high performance ad selection. In one example, the system receives an ad. A relevance of the ad needs to be determined. The relevance is a function of one or more computational intensive functions. A computational intensive function is a function that requires more than trivial processing. The system identifies one or more arguments of the computational intensive functions that are within a fixed range. The system generates a tableau based on the one or more arguments that are within a fixed range. The tableau is configured to benefit run-time performance of an ad selection process whenever the computer uses the pre-generated tableau during run-time instead of calculating one or more computational intensive functions.

Abstract: Methods and systems are provided that may be used to selectively decode results in messages received from child nodes for a particular search query.

Abstract: A computer readable medium stores a program for optimization for a search, and has sets of instructions for receiving a first decision tree. The first decision tree includes several nodes, and each node is for comparing a feature value to a threshold value. The instructions are for weighting the nodes within the first decision tree, determining the weighted frequency of a first feature within the first decision tree, and determining the weighted frequency of a second feature within the first decision tree. The instructions order the features based on the determined weighted frequencies, and store the ordering such that values of features having higher weighted frequencies are retrieved more often than values of features having lower weighted frequencies within the first decision tree.

Abstract: Machine-learned ranking algorithms, e.g. for ranking search results, often use a sequence of decision trees involving decision nodes based on threshold values of features. Modules, systems and methods of optimizing such algorithms involve analyzing threshold feature values to determine threshold intervals for each feature and grouping decision trees according to the feature used in a root decision node. Then coalescing the decision trees within each group to form a coalesced group tree for each group and finally coalescing the coalesced group trees to form a coalesced tree that implements the algorithm.

Abstract: The subject matter disclosed herein relates to prefetching data for use in ranking of electronic documents via a document ranking component.

Abstract: The subject matter disclosed herein relates to alter an expression of executable instructions via a compiler component for use in ranking of electronic documents.

Abstract: A method for improving parallel processing of computer programs. DOACROSS loops and similar code are identified and parallelized using a post-wait control structure. The post-wait control structure may be implemented to include any one of a single counter to enforce an order of execution, an array to track code completion that is indexed by a modulus of a positive integer number, and/or a set of arrays to track a last code completed by a thread and a current code being executed by a thread.

Abstract: According to one example embodiment, there is disclosed herein uses partial recurrence relaxation for parallelizing DOACROSS loops on multi-core computer architectures. By one example definition, a DOACROSS may be a loop that allows successive iterations executing by overlapping; that is, all iterations must impose a partial execution order. According to one embodiment, the inventive subject matter may be used to transform the dependence structure of a given loop with recurrences for maximal degree of thread-level parallelism (TLP), where the threads can be mapped on to either different logical processors (in a hyperthreaded processor) or can be mapped onto different physical cores (or processors) in a multi-core processor.

Abstract: A method for improving parallel processing of computer programs. DOACROSS loops and similar code are identified and parallelized using a post-wait control structure. The post-wait control structure may be implemented to include any one of a single counter to enforce an order of execution, an array to track code completion that is indexed by a modulus of a positive integer number, and/or a set of arrays to track a last code completed by a thread and a current code being executed by a thread.