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An agree predictor is a two-level adaptive predictor with globally shared history buffer and pattern history table, and an additional local saturating counter. The outputs of the local and the global predictors are XORed with each other to give the final prediction.
The global predictor is a single-level, 4096-entry branch history table. Each entry is a 2-bit saturating counter; the value of this counter determines whether the current branch is taken or not taken. The choice predictor records the history of the local and global predictors to determine which predictor is the best for a particular branch.
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.
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.
When computing a t-test, it is important to keep in mind the degrees of freedom, which will depend on the level of the predictor (e.g., level 1 predictor or level 2 predictor). [5] For a level 1 predictor, the degrees of freedom are based on the number of level 1 predictors, the number of groups and the number of individual observations. For a ...
Thus, instead of using a conditional branch to select an instruction or a sequence of instructions to execute based on the predicate that controls whether the branch occurs, the instructions to be executed are associated with that predicate, so that they will be executed, or not executed, based on whether that predicate is true or false. [1]
The story of Hispanic men is a two-part story, especially in the areas of education and income, according to Mark Hugo Lopez, director of race and ethnicity research at the Pew Research Center ...
Historically, branch prediction took statistics, and used the result to optimize code. A programmer would compile a test version of a program, and run it with test data. The test code counted how the branches were actually taken. The statistics from the test code were then used by the compiler to optimize the branches of released code.