Managing SMT Resource Usage through Speculative Instruction Window Weighting

Simultaneous multithreading processors dynamically share processor resources between multiple threads. In general, shared SMT resources may be managed explicitly, for instance, by dynamically setting queue occupation bounds for each thread as in the DCRA and Hill-Climbing policies. Alternatively, resources may be managed implicitly; that is, resource usage is controlled by placing the desired instruction mix in the resources. In this case, the main resource management tool is the instruction fetch policy which must predict the behavior of each thread (branch mispredictions, long-latency loads, etc.) as it fetches instructions.

Speculative Return Address Stack Management Revisited

Branch prediction feeds a speculative execution processor core with instructions. Branch mispredictions are inevitable and have negative effects on performance and energy consumption. With the advent of highly accurate conditional branch predictors, nonconditional branch instructions are gaining importance.

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.

On generating set index functions for randomized caches

Caches hide the growing latency of accesses to the main memory from the processor by storing the most recently used data on-chip. To limit the search time through the caches, they are organized in a direct mapped or set-associative way. Such an organization introduces many conflict misses that hamper performance. This paper studies randomizing set index functions, a technique to place the data in the cache in such a way that conflict misses are avoided. The performance of such a randomized cache strongly depends on the randomization function. This paper discusses a methodology to generate randomization functions that perform well over a broad range of benchmarks. The methodology uses profiling information to predict the conflict miss rate of randomization functions. Then, using this information, a search algorithm finds the best randomization function. Due to implementation issues, it is preferable to use a randomization function that is extremely simple and can be evaluated in little time. For these reasons, we use randomization functions where each randomized address bit is computed as the XOR of a subset of the original address bits. These functions are chosen such that they operate on as few address bits as possible and have few inputs to each XOR. This paper shows that to index a 2(m)-set cache, it suffices to randomize m+2 or m+3 address bits and to limit the number of inputs to each XOR to 2 bits to obtain the full potential of randomization. Furthermore, it is shown that the randomization function that we generate for one set of benchmarks also works well for an entirely different set of benchmarks. Using the described methodology, it is possible to reduce the implementation cost of randomization functions with only an insignificant loss in conflict reduction.

Towards an understanding of the causes and effects of software requirements change: two case studies

Changes to software requirements not only pose a risk to the successful delivery of software applications but also provide opportunity for improved usability and value. Increased understanding of the causes and consequences of change can support requirements management and also make progress towards the goal of change anticipation. This paper presents the results of two case studies that address objectives arising from that ultimate goal. The first case study evaluated the potential of a change source taxonomy containing the elements ‘market’, ‘organisation’, ‘vision’, ‘specification’, and ‘solution’ to provide a meaningful basis for change classification and measurement. The second case study investigated whether the requirements attributes of novelty, complexity, and dependency correlated with requirements volatility. While insufficiency of data in the first case study precluded an investigation of changes arising due to the change source of ‘market’, for the remainder of the change sources, results indicate a significant difference in cost, value to the customer and management considerations. Findings show that higher cost and value changes arose more often from ‘organisation’ and ‘vision’ sources; these changes also generally involved the co-operation of more stakeholder groups and were considered to be less controllable than changes arising from the ‘specification’ or ‘solution’ sources. Results from the second case study indicate that only ‘requirements dependency’ is consistently correlated with volatility and that changes coming from each change source affect different groups of requirements. We conclude that the taxonomy can provide a meaningful means of change classification, but that a single requirement attribute is insufficient for change prediction. A theoretical causal account of requirements change is drawn from the implications of the combined results of the two case studies.

Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a dataset as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output.The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed.

Tracking Human Position and Lower Body Parts Using Kalman and Particle Filters Constrained by Human Biomechanics

In this paper, a novel framework for visual tracking of human body parts is introduced. The approach presented demonstrates the feasibility of recovering human poses with data from a single uncalibrated camera by using a limb-tracking system based on a 2-D articulated model and a double-tracking strategy. Its key contribution is that the 2-D model is only constrained by biomechanical knowledge about human bipedal motion, instead of relying on constraints that are linked to a specific activity or camera view. These characteristics make our approach suitable for real visual surveillance applications. Experiments on a set of indoor and outdoor sequences demonstrate the effectiveness of our method on tracking human lower body parts. Moreover, a detail comparison with current tracking methods is presented.

Electron-impact excitation of Fe II

We report in this paper the computation of accurate total collision strengths and effective collision strengths for electron-impact excitation of FeII, using the parallel R-matrix program PRMAT. Target states corresponding to the 3d(6)4s, 3d(7), 3d(6)4p and 3d(5)4s4s basis configurations were included in the calculations giving rise to a 113 LS state 354 coupled channel problem. Following a detailed systematic study of correlation effects in both the target state and collision wavefunctions, it was found that an additional 21 configuration functions needed to be included in the Configuration Interaction expansion to obtain significantly more accurate target states and collision wavefunctions. This much improved 26-configuration model has been used to calculate converged total effective collision strengths for all sextet to quartet transitions among these levels with total spin S=2, giving a total of 1785 lines. These calculations have laid the foundation for an approach which may be adopted in the study of electron collisions with the low ionization stages of other iron peak elements. The work has been further extended with the commencement of a Breit-Pauli relativistic calculation for one of the smaller models and includes 262 fine-structure levels and over 1800 coupled channels. At the same time the PRMAT parallel R-matrix package is being extended to include relativistic effects which will allow us to attempt the more sophisticated 26-configuration model and produce for the first time the amount and quality of atomic data required to perform a meaningful synthesis of the Fe II spectrum.

Chlorostannate(II) Ionic Liquids: Speciation, Lewis Acidity, and Oxidative Stability

The anionic speciation of chlorostannate(II) ionic liquids, prepared by mixing 1-alkyl-3-methylimidazolium chloride and tin(II) chloride in various molar ratios, chi(SnCl2), was investigated in both solid and liquid states. The room temperature ionic liquids were investigated by Sn-119 NMR spectroscopy, X-ray photoelectron spectroscopy, and viscometry. Crystalline samples were studied using Raman spectroscopy, single-crystal X-ray crystallography, and differential scanning calorimetry. Both liquid and solid systems (crystallized from the melt) contained [SnCl3](-) in equilibrium with Cl- when chi(SnCl2) < 0.50, [SnCl3](-) in equilibrium with [Sn2Cl5](-) when chi(SnCl2) > 0.50, and only [SnCl3](-) when chi(SnCl2) = 0.50. Tin(II) chloride was found to precipitate when chi(SnCl2) > 0.63. No evidence was detected for the existence of [SnCl4](-) across the entire range of chi(SnCl2) although such anions have been reported in the literature for chlorostannate(II) organic salts crystallized from organic solvents. Furthermore, the Lewis acidity of the chlorostannate(II)-based systems, expressed by their Gutmann acceptor number, has been determined as a function of the composition, chi(SnCl2), to reveal Lewis acidity for chi(SnCl2) > 0.50 samples comparable to the analogous systems based on zinc(II). A change of the Lewis basicity of the anion was estimated using H-1 NMR spectroscopy, by comparison of the measured chemical shifts of the C-2 hydrogen in the imidazolium ring. Finally, compositions containing free chloride anions (chi(SnCl2) < 0.50) were found to oxidize slowly in air to form a chlorostannate(IV) ionic liquid containing the [SnCl6](2-) anion.

Breast-feeding and childhood-onset type 1 diabetes:A pooled analysis of individual participant data from 43 observational studies

OBJECTIVE: To investigate if there is a reduced risk of type 1 diabetes in children breastfed or exclusively breastfed by performing a pooled analysis with adjustment for recognized confounders.
RESEARCH DESIGN AND METHODS: Relevant studies were identified from literature searches using MEDLINE, Web of Science, and EMBASE. Authors of relevant studies were asked to provide individual participant data or conduct prespecified analyses. Meta-analysis techniques were used to combine odds ratios (ORs) and investigate heterogeneity between studies.
RESULTS: Data were available from 43 studies including 9,874 patients with type 1 diabetes. Overall, there was a reduction in the risk of diabetes after exclusive breast-feeding for >2 weeks (20 studies; OR = 0.75, 95% CI 0.64-0.88), the association after exclusive breast-feeding for >3 months was weaker (30 studies; OR = 0.87, 95% CI 0.75-1.00), and no association was observed after (nonexclusive) breast-feeding for >2 weeks (28 studies; OR = 0.93, 95% CI 0.81-1.07) or >3 months (29 studies; OR = 0.88, 95% CI 0.78-1.00). These associations were all subject to marked heterogeneity (I(2) = 58, 76, 54, and 68%, respectively). In studies with lower risk of bias, the reduced risk after exclusive breast-feeding for >2 weeks remained (12 studies; OR = 0.86, 95% CI 0.75-0.99), and heterogeneity was reduced (I(2) = 0%). Adjustments for potential confounders altered these estimates very little.
CONCLUSIONS: The pooled analysis suggests weak protective associations between exclusive breast-feeding and type 1 diabetes risk. However, these findings are difficult to interpret because of the marked variation in effect and possible biases (particularly recall bias) inherent in the included studies.

Risk-informed decision-making in the presence of epistemic uncertainty

An important issue in risk analysis is the distinction between epistemic and aleatory uncertainties. In this paper, the use of distinct representation formats for aleatory and epistemic uncertainties is advocated, the latter being modelled by sets of possible values. Modern uncertainty theories based on convex sets of probabilities are known to be instrumental for hybrid representations where aleatory and epistemic components of uncertainty remain distinct. Simple uncertainty representation techniques based on fuzzy intervals and p-boxes are used in practice. This paper outlines a risk analysis methodology from elicitation of knowledge about parameters to decision. It proposes an elicitation methodology where the chosen representation format depends on the nature and the amount of available information. Uncertainty propagation methods then blend Monte Carlo simulation and interval analysis techniques. Nevertheless, results provided by these techniques, often in terms of probability intervals, may be too complex to interpret for a decision-maker and we, therefore, propose to compute a unique indicator of the likelihood of risk, called confidence index. It explicitly accounts for the decisionmaker’s attitude in the face of ambiguity. This step takes place at the end of the risk analysis process, when no further collection of evidence is possible that might reduce the ambiguity due to epistemic uncertainty. This last feature stands in contrast with the Bayesian methodology, where epistemic uncertainties on input parameters are modelled by single subjective probabilities at the beginning of the risk analysis process.