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The purpose of the article is to define the role and importance of values in managing a contemporary enterprise, while taking into account the perspective of intercultural management. Values are a very important element affecting employees’ motivation and commitment in achieving common goals, especially in a culturally diverse work environment. The first part of the article explains the essence of management through values. The next part describes the role of values in management and benefits stemming from managing by values. Next intercultural management and its dimensions are discussed, whereas the last part describes the challenges related to values in intercultural management., Celem artykułu jest określenie roli i znaczenia wartości w zarządzaniu współczesnym przedsiębiorstwem, z uwzględnieniem perspektywy zarządzania międzykulturowego. Wartości są bardzo istotnym elementem, który wpływa na motywację i zaangażowanie pracowników w realizację wspólnych celów, zwłaszcza w zróżnicowanym kulturowo środowisku pracy. W pierwszej części artykułu wyjaśniono istotę zarządzania przez wartości. W kolejnej części opisano rolę wartości w zarządzaniu przedsiębiorstwem i korzyści, jakie wynikają z zarządzania przez wartości. Następnie zdefiniowano zarządzanie międzykulturowe i przedstawiono jego wymiary. W ostatniej części artykułu opisano wyzwania związane z wartościami w zarządzaniu międzykulturowym.

Periods of recession and economic crisis pose great challenges to companies committed to quality of work and by extension to their human resources (HR) departments. The need for adjustments can be a great opportunity for the implementation of socially responsible labour practices for the wellbeing of employees and in line with sustainable development and sustainable development goals. This study analyses the effects of the interaction between different socially responsible human resource management labour practices on three variables of employee wellbeing: job satisfaction, job stress and trust in management. Prior to the application of various statistical models, a correlation analysis of labour practices is carried out. Two types of statistical models are used depending on the type of variable to be explained. The ordinal logit has been applied to variables ‘trust in management’ and ‘level of job stress,’ which are not continuous. And the linear regression model for the variable ‘job satisfaction,’ which is continuous. Based on the analysis of data from 1346 workers from the 2010 Quality of Life at Work Survey in Catalonia, the results show the effectiveness of the combination of applied practices related to personal quality of life aspects, such as work‐life balance, non‐discrimination or environmental workplace conditions, as a determining factor for generally increasing the wellbeing of employees in times of crisis. However, practices aimed at achieving greater employee involvement, are more effective when applied individually. The importance of job stability to increase employee wellbeing in times of crisis is to be highlighted.

Incorporating machine learning into automatic performance analysis and tuning tools is a promising path to tackle the increasing heterogeneity of current HPC applications. However, this introduces the need for generating balanced datasets of parallel applications’ executions and for dealing with natural imbalances for optimizing performance parameters. This work proposes a holistic approach that integrates a methodology for building balanced datasets of OpenMP code-region patterns and a way to use such datasets for tuning performance parameters. The methodology uses hardware performance counters to characterize the execution of a given region and correlation analysis to determine whether it covers an unique part of the pattern input space. Nevertheless, a balanced dataset of region patterns may become naturally imbalanced when used for training a model for tuning any specific performance parameter. For this reason, we have explored several methods for dealing with naturally imbalanced datasets for finding the appropriated way of using them for tuning purposes. Experimentation shows that the proposed methodology can be used to build balanced datasets and that such datasets, plus a combination of Random Forest and binary classification, can be used to train a model able to accurately tune the number of threads of OpenMP parallel regions.

Incorporating machine learning into automatic performance analysis and tuning tools is a promising path to tackle the increasing heterogeneity of current HPC applications. However, this introduces the need for generating balanced and representative datasets of parallel applications’ executions. This work proposes a methodology for building datasets of OpenMP parallel code regions patterns. It allows for determining whether a given code region covers a unique part of the pattern input space not covered by the patterns already included in the dataset. The proposed methodology uses hardware performance counters to represent the execution of the region, which is referred to as the region signature for a given number of cores. Then, a complete representation of the region is built by joining the signatures for every different thread configuration in the system. Next, correlation analysis is performed between this representation and the representation of all the patterns already in the training set. Finally, if this correlation is below a given threshold, the region is considered to cover a unique part of the pattern input space and is subsequently added to the dataset. For validating this methodology, an example dataset, obtained from well known benchmarks, has been used to train a carefully designed neural network model to demonstrate that it is able to classify different patterns of OpenMP parallel regions.

This article focuses on sustainable development and human resource management (HRM). It analyzes the effects of the economic crisis on corporate social responsibility (CSR) in the management of employees and its determinants, and also depicts a map of job quality for employees in Spain. Results show that the crisis has not changed the overall pattern or map; neither the characteristics of the firm, the employee, or the job that determine the map. Results also point to the fact that employees at the lower end of CSR in HRM practices have been the most affected by hard cost‐containment measures applied by firms, such as dismissals. Around one‐third of employees have low job quality in nearly all the dimensions analyzed; while two‐thirds have higher job quality, but with variability among dimensions. The analysis is conducted for the years 2006–2010, hence comprising pre‐crisis and amid the crisis years. Cross‐section repeated samples of employees are used, from the Quality of Working Life Survey, which allow us to study the evolution of HRM and CSR. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment

450,000 European citizens are diagnosed every year with colorectal cancer (CRC) and more than 230,000 succumb to the disease annually. For this reason, significant resources are dedicated to the identification of more effective therapies for this disease. However, classical assessment techniques for these treatments are slow and costly. Consequently, systems biology researchers at the Royal College of Surgeons in Ireland (RCSI) are developing computational agent-based models simulating tumour growth and treatment responses with the objective of speeding up the therapeutic development process while, at the same time, producing a tool for adapting treatments to patient-specific characteristics. However, the model complexity and the high number of agents to be simulated require a thorough optimisation of the process in order to execute realistic simulations of tumour growth on currently available platforms. We propose to apply the most advanced HPC techniques to achieve the efficient and realistic simulation of a virtual tissue model that mimics tumour growth or regression in space and time. These techniques combine extensions of the previously developed agent-based simulation software platform (FLAME) with autotuning capabilities and optimisation strategies for the current tumour model. Development of such a platform could advance the development of novel therapeutic approaches for the treatment of CRC which can also be applied other solid tumours. This work has been partially supported by MICINN-Spain under contract TIN2011- 28689-C02-01 and TIN2014-53234-C2-1-R and GenCat-DIUiE(GRR) 2014-SGR-576. This research was also funded by the European Community’s Framework Programme Seven (FP7) Programme under contract No. 278981 680 AngioPredict and supported by the DJEI/DES/SFI/HEA Irish Centre for High- End Computing (ICHEC).

There are several parallel applications that are implemented using a Master/Worker parallel/distributed programming paradigm. Applications using this predefined programming structure can be easily implemented using message passing programming libraries (MPI). Moreover, the multicore features present nowadays on CPU architecture can be exploited at the node level by applying thread parallelism (OpenMP). In order to exploit the benefits of both two levels of parallelism, the Master/Worker applications can be implemented as hybrid applications. However, reaching the expected performance indexes is not trivial because there are several parameters (number of nodes, number of threads per node, thread affinity and data distribution among all nodes) that must be tuned for each particular application or even during its execution to reach a successful performance. On the other hand, the application workload may change drastically during successive executions, so those parameters need to be modified according to this behavior. Additionally, cache memory architecture in multicore systems directly influences the performance and this behaviour must be deeply analysed. In this paper we present a proposal to model the performance of hybrid Master/Worker applications on multicore systems considering the issues outlined above. In particular, this model determines dynamically at runtime the configuration of the appropriate number of workers and threads of the hybrid application to achieve the best possible performance.

Defining performance models associated with the application structure has been proven a useful strategy for implementing dynamic tuning tools. However, for extending this strategy to more complex applications (those composed by different structures) it must integrate a policy for the distribution of the resources among the different application components. Consequently, we propose to take advantage of the knowledge of these models and combine them with a resource management policy for obtaining a global model. In this sense, this work constitutes the ongoing effort in the development of performance models for dynamic tuning.