International audienceMost modern processors are heavily parallelized and use predictors to guess the outcome of conditional branches, in order to avoid costly stalls in their pipelines. We propose predictor-friendly versions of two classical algorithms: exponentiation by squaring and binary search in a sorted array. These variants result in less mispredictions on average, at the cost of an increased number of operations. These theoretical results are supported by experimentations that show that our algorithms perform significantly better than the standard ones, for primitive data types. 1998 ACM Subject Classification F.2.2 Nonnumerical Algorithms and Problem
This article presents a new and highly accurate method for branch prediction. The key idea is to use...
We explore the fundamental problem of sorting through the lens of learning-augmented algorithms, whe...
In this paper, we introduce a new branch predictor that predicts the outcomes of branches by predict...
Most modern processors are heavily parallelized and use predictors to guess the outcome of condition...
International audienceMost modern processors are heavily parallelized and use predictors to guess th...
A commonly used type of search tree is the alphabetic binary tree, which uses (without loss of gener...
Abstract. The problem of predicting the outcome of a conditional branch instruction is a prerequisit...
A burgeoning paradigm in algorithm design is the field of algorithms with predictions, in which algo...
In this paper, we introduce a new branch predictor that predicts the outcomes of branches by predict...
Recent implementations of local approximate Gaussian process models have pushed computational bounda...
Modern superscalar processors rely on branch predictors to sustain a high instruction fetch throughp...
We study confidence-rated prediction in a binary classification setting, where the goal is to design...
Traditional branch predictors exploit correlations between pattern history and branch outcome to pre...
Classical heuristic search algorithms find the solution cost of a problem while finding the path fro...
In this paper, we introduce a new branch predictor that predicts the outcome of branches by predicti...
This article presents a new and highly accurate method for branch prediction. The key idea is to use...
We explore the fundamental problem of sorting through the lens of learning-augmented algorithms, whe...
In this paper, we introduce a new branch predictor that predicts the outcomes of branches by predict...
Most modern processors are heavily parallelized and use predictors to guess the outcome of condition...
International audienceMost modern processors are heavily parallelized and use predictors to guess th...
A commonly used type of search tree is the alphabetic binary tree, which uses (without loss of gener...
Abstract. The problem of predicting the outcome of a conditional branch instruction is a prerequisit...
A burgeoning paradigm in algorithm design is the field of algorithms with predictions, in which algo...
In this paper, we introduce a new branch predictor that predicts the outcomes of branches by predict...
Recent implementations of local approximate Gaussian process models have pushed computational bounda...
Modern superscalar processors rely on branch predictors to sustain a high instruction fetch throughp...
We study confidence-rated prediction in a binary classification setting, where the goal is to design...
Traditional branch predictors exploit correlations between pattern history and branch outcome to pre...
Classical heuristic search algorithms find the solution cost of a problem while finding the path fro...
In this paper, we introduce a new branch predictor that predicts the outcome of branches by predicti...
This article presents a new and highly accurate method for branch prediction. The key idea is to use...
We explore the fundamental problem of sorting through the lens of learning-augmented algorithms, whe...
In this paper, we introduce a new branch predictor that predicts the outcomes of branches by predict...