Chemistry in anisotropic asymptotic giant branch winds

We have constructed a model for chemistry in the outflow of an asymptotic giant branch (AGB) star, using a spheroidal anisotropy in density, after that used by Jura. The predicted distributions of a selection of representative species are shown, and it is suggested that the abundance distributions observed by interferometry in IRC + 10216 may be the result of directional variation in outflow velocity.

Ruling out a massive asymptotic giant-branch star as the progenitor of supernova 2005cs

We calculate the predicted UBVRIJHK absolute magnitudes for models of supernova progenitors and apply the result to the case of supernova 2005cs. We agree with previous results that the initial mass of the star was low, around 6 to 8 M-circle dot. However, such stars are thought to go through a second dredge-up to become asymptotic giant branch (AGB) stars. We show that had this occurred to the progenitor of 2005cs it would have been observed in JHK pre-explosion images. The progenitor was not detected in these bands and therefore we conclude that it was not an AGB star. Furthermore, if some AGB stars do produce supernovae they will have a clear signature in pre-explosion near-infrared images. Electron-capture supernovae are thought to occur in AGB stars, hence the implication is that 2005cs was not an electron-capture supernova but was the collapse of an iron core.

Behavior-Based branch prediction by dynamically clustering branch instructions

Conditional branches frequently exhibit similar behavior (bias, time-varying behavior,...), a property that can be used to improve branch prediction accuracy. Branch clustering constructs groups or clusters of branches with similar behavior and applies different branch prediction techniques to each branch cluster. We revisit the topic of branch clustering with the aim of generalizing branch clustering. We investigate several methods to measure cluster information, with the most effective the storage of information in the branch target buffer. Also, we investigate alternative methods of using the branch cluster identification in the branch predictor. By these improvements we arrive at a branch clustering technique that obtains higher accuracy than previous approaches presented in the literature for the gshare predictor. Furthermore, we evaluate our branch clustering technique in a wide range of predictors to show the general applicability of the method. Branch clustering improves the accuracy of the local history (PAg) predictor, the path-based perceptron and the PPM-like predictor, one of the 2004 CBP finalists.

Clustered indexing for branch predictors

As a result of resource limitations, state in branch predictors is frequently shared between uncorrelated branches. This interference can significantly limit prediction accuracy. In current predictor designs, the branches sharing prediction information are determined by their branch addresses and thus branch groups are arbitrarily chosen during compilation. This feasibility study explores a more analytic and systematic approach to classify branches into clusters with similar behavioral characteristics. We present several ways to incorporate this cluster information as an additional information source in branch predictors.