Nested-Loops Tiling for Parallelization and Locality Optimization

Data locality improvement and nested loops parallelization are two complementary and competing approaches for optimizing loop nests that constitute a large portion of computation times in scientific and engineering programs. While there are effective methods for each one of these, prior studies have paid less attention to address these two simultaneously. This paper proposes a unified approach that integrates these two techniques to obtain an appropriate locality conscious loop transformation to partition the loop iteration space into outer parallel tiled loops. The approach is based on the polyhedral model to achieve a multidimensional affine scheduling as a transformation that result the largest groups of tilable loops with maximum coarse grain parallelism, as far as possible. Furthermore, tiles will be scheduled on processor cores to exploit maximum data reuse through scheduling tiles with high volume of data sharing on the same core consecutively or on different cores with shared cache at around the same time

Early Failure Prediction in Software Programs: Dimensionality Reduction Kernel

The aim of this paper is to build an online failure prediction classifier for monitoring the behavior of programs. The classifier predicts the termination state of the program execution paths as failing or passing. This could be achieved by mapping each execution path as a vector into a feature space whose dimensions represent common sub-paths amongst failing and passing execution paths. The main contribution of this paper is to treat the failure prediction problem as a classification task of execution paths in a customized feature space. The main dilemma is the size and the number of space dimensions, affecting the speed of the classifier. The size of the dimensions could be reduced by shortening the length of the common sub-paths, used as the space dimensions. The length of common sub-paths is affected by repeated patterns in program executions. Replacing the consecutively repeated patterns with only a single iteration in execution paths, reduces the size of the common sub-paths. The number of dimensions could be reduced by removing dimensions which have projection onto others. This paper proposes two kernels which measure similarity amongst execution paths in an implicit feature space with reduced dimensionality. Our experiments demonstrate a significant reduction in time overhead of the failure prediction classifier while preserving accuracy

La gestión sostenible y flexible del recurso humano en las organizaciones innovadoras

In response to the changes in economies and technology in recent decades, research in organizational theories have been focused toward innovative and entrepreneurial organizations. A research issue in this evolving research endeavor is adaptation of human resource management and the establishment of a sustainable human resource management. This paper investigates the main characteristics of a sustainable HRM in innovative organizations. The aim is to identify sustainable HRM as a key toward competing in turbulent markets. The problem statement is to find the relationship between psychological capital, HR flexibility and sustainable HRM in innovative organizations. Three main variables of HR flexibility, HR sustainability and psychological capital form the theoretical model of this study; and four hypotheses are developed based on this model. Findings do not reject any of four hypotheses, so it is concluded that psychological capital and HR flexibility has positive and meaningful effect on sustainable HRM; and in addition, psychological capital has positive and meaningful effect on sustainable HRM. Moreover, flexibility has moderate role in relationship between psychological capital and sustainable HRMEn respuesta a los cambios en las economías y la tecnología en las décadas recientes, la investigación en las teorías organizacionales ha estado enfocada en las empresas innovadoras y emprendedoras. Un tema de exploración en estos esfuerzos por una investigación cambiante se trata de la adaptación de la gestión del recurso humano (GRH) y el establecimiento de una gestión sostenible del recurso humano (GRH sostenible). Este artículo investiga las principales características de la GRH en las organizaciones innovadoras. El objetivo es identificar la GRH sostenible como clave para competir en mercados turbulen- tos. El problema planteado radica en encontrar la relación entre el capital psicológico y la flexibilidad en los recursos humanos y la GRH sostenible en organizaciones innovadoras. Tres importantes variables de la flexibilidad de los recursos humanos, la sostenibilidad de los recursos humanos y el capital psicológico forman el modelo teórico de este estudio; y se desarrollan cuatro hipótesis basadas en este modelo. Los hallazgos no rechazan ninguna de las cuatro hipótesis, por lo cual se concluye que el capital psicológico y la flexibilidad en los recursos humanos tienen un efecto positivo y significativo en la GRH sostenible, y adicional- mente, el capital psicológico tiene un efecto positivo y significativo en la GRH sostenible. Además, la flexibilidad desempeña un rol moderado en la relación entre capital psicológico y la GRH sostenibl

Mitigating Backdoors within Deep Neural Networks in Data-limited Configuration

As the capacity of deep neural networks (DNNs) increases, their need for huge amounts of data significantly grows. A common practice is to outsource the training process or collect more data over the Internet, which introduces the risks of a backdoored DNN. A backdoored DNN shows normal behavior on clean data while behaving maliciously once a trigger is injected into a sample at the test time. In such cases, the defender faces multiple difficulties. First, the available clean dataset may not be sufficient for fine-tuning and recovering the backdoored DNN. Second, it is impossible to recover the trigger in many real-world applications without information about it. In this paper, we formulate some characteristics of poisoned neurons. This backdoor suspiciousness score can rank network neurons according to their activation values, weights, and their relationship with other neurons in the same layer. Our experiments indicate the proposed method decreases the chance of attacks being successful by more than 50% with a tiny clean dataset, i.e., ten clean samples for the CIFAR-10 dataset, without significantly deteriorating the model's performance. Moreover, the proposed method runs three times as fast as baselines

Value of Admission HbA1c Level in Non-diabetic Patients With Unstable Angina

Introduction: There have been incompatible evidences about the prognostic value of HbA1c on the adverse outcomes in acute coronary syndrome. Also, these data are so limited in nondiabetic patients with unstable angina.Methods: In this cross-sectional study, HbA1c level of 231 nondiabetic patients admitted with unstable angina, was measured using high performance liquid affinity chromatography (HPLC) at admission. Then transthoracic echocardiography (TTE) was performed for evaluation of ejection fraction (EF) using Simpson method.Results: Our data revealed that HbA1c was significantly higher in patients with EF≤ 50% in comparison with EF>50% group (P value=0.01).Conclusions: HbA1c may be a helpful prognostic marker in nondiabetic patients admitted in emergency department with diagnosis of unstable angina