Along with the advancement of chip architecture and density of processor design, there are more functional units that can execute in parallel on a chip. In order to make good use of them, it is important to obtain enough and accurate instructions ahead of time. Branch prediction provides a way to know the instruction stream ahead of time. Its prediction accuracy is thus one of the key factors of system performance. In our research, we designed a branch prediction method based upon the loop-relevance classifications of conditional jump instructions. It divides conditional jump instructions into two classifications: loop-exit and non-loop-exit conditional jump instructions. We utilized various prediction methods to perform the branch prediction tasks for these two classes of conditional branch instructions, separately. Inside these methods, dynamic learning from actual branch results is carried out to switch to suitable prediction models such that more prediction accuracy can be obtained. In this thesis research, in order to validate the accuracy of this prediction method vs. other prediction methods, we designed a software performance evaluation environment to do trace-driven simulation of types of branch prediction methods. It consists of an instruction trace extractor and a set of trace-driven simulators. Experiment results shows that our prediction methods performs near the same as other prediction methods on the scalar processing programs that have little or no amount of regularly behaved loops. However, on the scientific or engineering programs that exhibit certain percentage of regularly behaved loops, experiment results shows that our prediction method recognizes their loop behavior patterns and achieves better prediction accuracy.

Thesis (M.A.)--University of Chicago. / Bibliography: p. 107-108.

Pipelining is the major organizational technique by which computers can execute several instructions simultaneously to reach higher single-processor performance. Branches are recognized as a major impediment to achieve the maximum performance of pipelining and superscalar processors due to stalls caused by unresolved branches. Branch prediction is an effective strategy to reduce the branch penalty via predicting, prefetching and executing the speculative instructions before the branch is resolved. A branch target buffer (BTB)[13] can reduce the performance caused by branches via predicting the direction of the branch and caching information about the branch. While prediction is incorrect, the processor requires flushing the speculative instructions, undoing the effects of the improperly initiated speculative execution and resuming on the correct path. These flushing and refilling degrade significantly processor performance. In this thesis we propose a mechanism, Double Level Branch Buffer, which can reduce the branch penalty and performance loss caused from incorrect prediction. We try to cache the information of branch about both taken and not taken direction. The pipeline will degrade the dependence upon branch prediction accuracy by utilizing this mechanism.

by Chan Man Fai. / Bibliography: leaves 45-47 / Thesis (M.B.A.)--Chinese University of Hong Kong, 1982

Thesis (Ph. D.)--Brown University, 2001. / Includes bibliographical references (leaves 146-152).

Göttingen, Universiẗat, Diss., 2003. / Dateien in unterschiedlichen Formaten.

This thesis is in two parts. The first is a study of soluble profinite groups with the maximal condition (max) and mirrors the development of the fundamental properties of polycyclic groups. Two of the main results are a profinite version of Hall's criterion for nilpotence and the existence of nilpotent almost-supplements for the Fitting subgroup. It is shown that the automorphism group of a soluble profinite group with max itself has max and that a soluble profinite group has max if and only if each of its subnormal subgroups of defect at most 2 has max. Quantitative versions of these results are also obtained. The second part derives a new characterization of abstract and profinite branch groups. This is achieved by examining the subnormal subgroup structure of just non-(virtually abelian) groups and a partial classification of this larger class is obtained. Weak branch groups are shown to satisfy no abstract group laws and to contain many abstract free subgroups in the profinite case. The notion of a branch group is weakened to that of a generalized branch group. Associated with each generalized branch group is its structure graph, and the circumstances under which this graph is a tree are characterized.

No description available.

In preparation for the upcoming all-sky data releases of the Gaia mission we compiled a catalog of known hot subdwarf stars and candidates drawn from the literature and yet unpublished databases. The catalog contains 5613 unique sources and provides multi-band photometry from the ultraviolet to the far infrared, ground based proper motions, classifications based on spectroscopy and colors, published atmospheric parameters, radial velocities and light curve variability information. Using several different techniques we removed outliers and misclassified objects. By matching this catalog with astrometric and photometric data from the Gaia mission, we will develop selection criteria to construct a homogeneous, magnitude-limited all-sky catalog of hot subdwarf stars based on Gaia data.

We consider the problem of computing the branchwidth and an optimal branch decomposition of a graph. Branch decompositions and branchwidth were introduced in 1991 by Robertson and Seymour and were used in the proof of Graph Minors Theorem (GMT), a well known conjecture (Wagner's conjecture) in graph theory. The notions of branchwidth and branch decompositions have been proved to be useful for solving many NP-hard problems that have applications in fields such as graph theory, network design, sensor networks and biology. Branch decompositions have been utilized for problems such as the traveling salesman problem by Cook and Seymour, general minor containment and the branchwidth problem by Hicks by means of the relevant branch decomposition-based algorithms. Branch decomposition-based algorithms are fixed parameter tractable algorithms obtained by combining dynamic programming techniques with branch decompositions. The running time and space of these algorithms strongly depend on the width of the utilized branch decomposition. Thus, finding optimal or close to optimal branch decompositions is very important for the efficiency of the branch decomposition-based algorithms. Motivated by the vastness of the fields of application, we aim to increase the efficiency of the branch decomposition-based algorithms by investigating effective techniques to find optimal branch decompositions. We present three integer programming models for the branchwidth problem. Two similar formulations are based on the relationship of branchwidth problem with a special case of the Steiner tree packing problem. The third formulation is based on the notion of laminar separations. We utilize upper and lower bounds obtained by heuristic algorithms, reduction techniques and cutting planes to increase the efficiency of our models. We use all three models for the branchwidth problem on hypergraphs as well. We compare the performance of three models both on graphs and hypergraphs. Furthermore we use the third model for rank-width problem and also offer a heuristic for finding good rank decompositions. We provide computational results for this problem, which can be a basis of comparison for future formulations.