Virtual cluster scheduling through the scheduling graph

This paper presents an instruction scheduling and cluster assignment approach for clustered processors. The proposed technique makes use of a novel representation named the scheduling graph which describes all possible schedules. A powerful deduction process is applied to this graph, reducing at each step the set of possible schedules. In contrast to traditional list scheduling techniques, the proposed scheme tries to establish relations among instructions rather than assigning each instruction to a particular cycle. The main advantage is that wrong or poor schedules can be anticipated and discarded earlier. In addition, cluster assignment of instructions is performed using another novel concept called virtual clusters, which define sets of instructions that must execute in the same cluster. These clusters are managed during the deduction process to identify incompatibilities among instructions. The mapping of virtual to physical clusters is postponed until the scheduling of the instructions has finalized. The advantages this novel approach features include: (1) accurate scheduling information when assigning, and, (2) accurate information of the cluster assignment constraints imposed by scheduling decisions. We have implemented and evaluated the proposed scheme with superblocks extracted from Speclnt95 and MediaBench. The results show that this approach produces better schedules than the previous state-of-the-art. Speed-ups are up to 15%, with average speed-ups ranging from 2.5% (2-Clusters) to 9.5% (4-Clusters).Peer ReviewedPostprint (published version

Scheduling Markovian PERT networks with maximum-NPV objective.

We examine project scheduling with net-present-value objective and exponential activity durations, using a continuous-time Markov decision chain. Based on a judicious partitioning of the state space, we achieve a significant performance improvement compared to the existing algorithms.Project scheduling; Net present value; Stochastic activity durations; Exponential distribution;

Forward and Backward Bisimulations for Chemical Reaction Networks

We present two quantitative behavioral equivalences over species of a chemical reaction network (CRN) with semantics based on ordinary differential equations. Forward CRN bisimulation identifies a partition where each equivalence class represents the exact sum of the concentrations of the species belonging to that class. Backward CRN bisimulation relates species that have the identical solutions at all time points when starting from the same initial conditions. Both notions can be checked using only CRN syntactical information, i.e., by inspection of the set of reactions. We provide a unified algorithm that computes the coarsest refinement up to our bisimulations in polynomial time. Further, we give algorithms to compute quotient CRNs induced by a bisimulation. As an application, we find significant reductions in a number of models of biological processes from the literature. In two cases we allow the analysis of benchmark models which would be otherwise intractable due to their memory requirements.Comment: Extended version of the CONCUR 2015 pape

Learning Dynamic Feature Selection for Fast Sequential Prediction

We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart of many NLP components. This is accomplished by partitioning the features into a sequence of templates which are ordered such that high confidence can often be reached using only a small fraction of all features. Parameter estimation is arranged to maximize accuracy and early confidence in this sequence. Our approach is simpler and better suited to NLP than other related cascade methods. We present experiments in left-to-right part-of-speech tagging, named entity recognition, and transition-based dependency parsing. On the typical benchmarking datasets we can preserve POS tagging accuracy above 97% and parsing LAS above 88.5% both with over a five-fold reduction in run-time, and NER F1 above 88 with more than 2x increase in speed.Comment: Appears in The 53rd Annual Meeting of the Association for Computational Linguistics, Beijing, China, July 201

Actionable Recourse in Linear Classification

Machine learning models are increasingly used to automate decisions that affect humans - deciding who should receive a loan, a job interview, or a social service. In such applications, a person should have the ability to change the decision of a model. When a person is denied a loan by a credit score, for example, they should be able to alter its input variables in a way that guarantees approval. Otherwise, they will be denied the loan as long as the model is deployed. More importantly, they will lack the ability to influence a decision that affects their livelihood. In this paper, we frame these issues in terms of recourse, which we define as the ability of a person to change the decision of a model by altering actionable input variables (e.g., income vs. age or marital status). We present integer programming tools to ensure recourse in linear classification problems without interfering in model development. We demonstrate how our tools can inform stakeholders through experiments on credit scoring problems. Our results show that recourse can be significantly affected by standard practices in model development, and motivate the need to evaluate recourse in practice.Comment: Extended version. ACM Conference on Fairness, Accountability and Transparency [FAT2019

Parallel Tiled Code Generation with Loop Permutation within Tiles

An approach of generation of tiled code with an arbitrary order of loops within tiles is presented. It is based on the transitive closure of the program dependence graph and derived via a combination of the Polyhedral and Iteration Space Slicing frameworks. The approach is explained by means of a working example. Details of an implementation of the approach in the TRACO compiler are outlined. Increasing tiled program performance due to loop permutation within tiles is illustrated on real-life programs from the NAS Parallel Benchmark suite. An analysis of speed-up and scalability of parallel tiled code with loop permutation is presented

Consequences of Traffic Casualties in Relation to Traffic-Engineering Factors - An Analysis in Short-term and Long-term Perspectives

The over-arching objectives of this thesis were to formulate a method to describe the consequences of traffic casualties, and to explain the influence of different traffic engineering factors on these consequences for society and individuals over time. A more detailed objective was to examine whether available short-term indicators can be used to predict consequences in a long-term perspective. Other objectives were to create a better understanding of the multidimensional nature of traffic safety problems, to find out how to allocate resources and traffic safety measures, and to identify changes in traffic safety problems over time. The study was based on one year of traffic casualties, 1991/92, in the admittance areas of five hospitals, in Karlshamn, Karlskrona, Lidköping, Lund and Umeå. The hospital data set held 2,915 casualties, while the police reported 1,722 killed and injured. Data were collected with an incidence approach, and in a public health perspective, i.e. including pedestrians injured in single accidents. On several occasions up to three years and five months after the accident, the injured answered postal questionnaires about the health situation and their care received. All analyses involved both the police and the hospital data sets. The traffic safety problems were expressed in following indicators: killed and injured, hospital care, ISS (Injury Severity Score), length of hospital stay, and visits to a doctor or physiotherapist/nurse, length of sick leave and health loss (Rosser Index), and described in terms of average severities, total consequences, and distributions of total consequences. The effects of six selected traffic-engineering factors on these indicators of consequences were thoroughly analysed in short-term and long-term perspectives. Moreover, the potential of four traffic safety measures were examined. Among the available short–term indicators, the ISS and length of hospital stay within one month after the accident performed the best, although not very well in predicting the long-term consequences of traffic injuries

Inertia and Change in the Early Years: Employment Relations in Young, High Technology Firms

[Excerpt] This paper considers processes of organizational imprinting in a sample of 100 young, high technology companies. It examines the effects of a pair of initial conditions: the founders\u27 models of the employment relation and their business strategies. Our analyses indicate that these two features were well aligned when the firms were founded. However, the alignment has deteriorated over time, due to changes in the distribution of employment models. In particular, the \u27star\u27 model and \u27commitment\u27 model are less stable than the \u27engineering\u27 model and the \u27factory\u27 model. Despite their instability, these two blueprints for the employment relation have strong effects in shaping the early evolution of these firms. In particular, firms that embark with these models have significantly higher rates of replacing the founder chief executive with a non-founder as well as higher rates of completing an initial public stock offering. Some implications of these findings for future studies of imprinting and inertia in organizations are discussed