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The general rule for a two-level adaptive predictor with an n-bit history is that it can predict any repetitive sequence with any period if all n-bit sub-sequences are different. [8] The advantage of the two-level adaptive predictor is that it can quickly learn to predict an arbitrary repetitive pattern.
Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream. PPM algorithms can also be used to cluster data into predicted groupings in cluster analysis.
The general rule for a two-level adaptive predictor with an n-bit history is that it can predict any repetitive sequence with any period if all n-bit sub-sequences are different.[8] The advantage of the two-level adaptive predictor is that it can quickly learn to predict an arbitrary repetitive pattern.
An adaptive algorithm is an algorithm that changes its behavior at the time it is run, [1] based on information available and on a priori defined reward mechanism (or criterion). Such information could be the story of recently received data, information on the available computational resources, or other run-time acquired (or a priori known ...
Elimination of unnecessary branch instructions can make the execution of necessary branches, such as those that make up loops, faster by lessening the load on branch prediction mechanisms. Elimination of the cost of a branch misprediction which can be high on deeply pipelined architectures.
Introsort: begin with quicksort and switch to heapsort when the recursion depth exceeds a certain level; Timsort: adaptative algorithm derived from merge sort and insertion sort. Used in Python 2.3 and up, and Java SE 7. Insertion sorts Insertion sort: determine where the current item belongs in the list of sorted ones, and insert it there ...
The algorithm maintains two history tables, Local and Global, and the table used to predict the outcome of a branch is determined by a Choice predictor. The local predictor is a two-level table which records the history of individual branches. It consists of a 1,024-entry by 10-bit branch history table. A two-level table was used as the ...
This approach is employed in a variety of areas, including branch prediction in pipelined processors, value prediction for exploiting value locality, prefetching memory and files, and optimistic concurrency control in database systems. [1] [2] [3] Speculative multithreading is a special case of speculative execution.