Understanding Change in the Deutsche Telekom Group: The Problem of Organisational Learning at Top Management Level

Performance and sustainability of a company are often viewed as being strongly influenced by its top management. And yet, despite a rich literature in the field of organisational learning, little is known about how the top management of a company acquires and maintains the knowledge of "how to run the business", especially in fast-changing environments. This study focuses on Deutsche Telekom as it sought to transform itself during a turbulent period from a traditional operator into an international, diverse business player. This work seeks to extend the theory and understanding of organisational learning to this particular group of people at the apex of the organisation and contributes to the debate of top-level organisational learning (TLOL) as a dynamic capability. The study, which is based upon longitudinal, qualitative research, featuring separate cases embedded within a single, large organisation, explores the methods and outcomes of TLOL. Comparative cases in different organisational settings and business contexts, at corporate and at divisional level, were used in this diversified organisation to empirically investigate the deployed initiatives and methods as well as drivers of and barriers to TLOL. The research contributes to and expands upon current theory by providing empirical evidence that the interplay between exogenous factors, strategy, the characteristics of the organisation and of the top management group shapes the methods used for TLOL. It provides evidence of the link between learning method and organisational outcomes. The study finds that in the turbulent environment described, the deployed methods for TLOL in the mature part of the organization differed substantially from the learning methods used in the young, fast-growing divisions. The learning methods used did, however, not produce exclusively positive results. Finally, by developing a new conceptual framework, the study allows researchers and management to systematically examine and shape top-level organisational learning within their own organisations

Function of beta1 and beta7 Integrins in the Hematopoietic System

In the hematopoietic system, integrins are crucial for the colonisation of hematopoietic organs, the development and distribution of progenitor cells, and for the extravasation of leukocytes into inflamed tissues. Using somatic chimeric mice carrying a targeted disruption of the gene for alpha4 integrin, which is known to dimerize with either integrin beta1 or beta7, alpha4 integrins were shown to be crucial for normal hematopoiesis. In these mice only few alpha4-null B cells were detected and T cell precursors could not leave the BM for maturation in the thymus. Furthermore, erythrocyte development was blocked and expansion of myeloid progenitors was reduced in the absence of alpha4 integrins. Mice with a knockout of beta7 or beta1 integrin restricted to the hematopoietic system, however, have no defects in hematopoiesis. To test whether alpha4beta1 and alpha4beta7 integrins have redundant functions beta1 and beta7 double knockout mice were generated by intercrossing mice with a constitutive beta7 knockout and a conditional ablation of beta1. To restrict the deletion to the hematopoietic system, BM chimeric mice were generated. The deletion of the beta1 integrin gene was induced after repopulation by repeated injections of polyIC. Surprisingly, no defects in the hematopoietic development of beta1beta7 mutant BM chimeras were noted. Development of B cells, T cells, platelets, erythrocytes, granulocytes and monocytes was unimpaired in the absence of beta1 and beta7. Normal cellularity of lymphoid organs indicated no obvious defect in leukocyte migration. No expansion of beta1 expressing cells was noted 12 months after the induction of the beta1 integrin gene deletion, suggesting no competitive advantage of beta1+ hematopoietic cells. Finally, normal expansion of hematopoietic cells was observed after acute haemolysis. Unexpectedly, HPCs and erythroblasts were found which express alpha4 integrin on the surface in the absence of beta1 and beta7 integrin. This alpha4 integrin was not able to mediate binding to VCAM-1 and it is therefore unclear, whether it is of functional importance. Using a genetic approach we could show that this alpha4 integrin is not heterodimerizing with beta2 integrin, which is strongly expressed on hematopoietic cells. These data indicate that alpha4beta1 and alpha4beta7 are not required for hematopoiesis and do not have essential overlapping functions. The severe phenotype reported for alpha4 null somatic chimeric mice might be caused by the loss of alpha4 integrin on non-hematopoietic cells, while in our system the ablation of beta1 and beta7 integrin is restricted to the hematopoietic system

Enabling Radiative Transfer on AMR grids in CRASH

We introduce CRASH-AMR, a new version of the cosmological Radiative Transfer (RT) code CRASH, enabled to use refined grids. This new feature allows us to attain higher resolution in our RT simulations and thus to describe more accurately ionisation and temperature patterns in high density regions. We have tested CRASH-AMR by simulating the evolution of an ionised region produced by a single source embedded in gas at constant density, as well as by a more realistic configuration of multiple sources in an inhomogeneous density field. While we find an excellent agreement with the previous version of CRASH when the AMR feature is disabled, showing that no numerical artifact has been introduced in CRASH-AMR, when additional refinement levels are used the code can simulate more accurately the physics of ionised gas in high density regions. This result has been attained at no computational loss, as RT simulations on AMR grids with maximum resolution equivalent to that of a uniform cartesian grid can be run with a gain of up to 60% in computational time.Comment: 19 pages, 17 figures. MNRAS, in pres

Efficient cosmological parameter sampling using sparse grids

We present a novel method to significantly speed up cosmological parameter sampling. The method relies on constructing an interpolation of the CMB-log-likelihood based on sparse grids, which is used as a shortcut for the likelihood-evaluation. We obtain excellent results over a large region in parameter space, comprising about 25 log-likelihoods around the peak, and we reproduce the one-dimensional projections of the likelihood almost perfectly. In speed and accuracy, our technique is competitive to existing approaches to accelerate parameter estimation based on polynomial interpolation or neural networks, while having some advantages over them. In our method, there is no danger of creating unphysical wiggles as it can be the case for polynomial fits of a high degree. Furthermore, we do not require a long training time as for neural networks, but the construction of the interpolation is determined by the time it takes to evaluate the likelihood at the sampling points, which can be parallelised to an arbitrary degree. Our approach is completely general, and it can adaptively exploit the properties of the underlying function. We can thus apply it to any problem where an accurate interpolation of a function is needed.Comment: Submitted to MNRAS, 13 pages, 13 figure

Cluster-based communication and load balancing for simulations on dynamically adaptive grids

short paperThe present paper introduces a new communication and load-balancing scheme based on a clustering of the grid which we use for the efficient parallelization of simulations on dynamically adaptive grids. With a partitioning based on space-filling curves (SFCs), this yields several advantageous properties regarding the memory requirements and load balancing. However, for such an SFC- based partitioning, additional connectivity information has to be stored and updated for dynamically changing grids. In this work, we present our approach to keep this connectivity information run-length encoded (RLE) only for the interfaces shared between partitions. Using special properties of the underlying grid traversal and used communication scheme, we update this connectivity information implicitly for dynamically changing grids and can represent the connectivity information as a sparse communication graph: graph nodes (partitions) represent bulks of connected grid cells and each graph edge (RLE connectivity information) a unique relation between adjacent partitions. This directly leads to an efficient shared-memory parallelization with graph nodes assigned to computing cores and an efficient en bloc data exchange via graph edges. We further refer to such a partitioning approach with RLE meta information as a cluster-based domain decomposition and to each partition as a cluster. With the sparse communication graph in mind, we then extend the connectivity information represented by the graph edges with MPI ranks, yielding an en bloc communication for distributed-memory systems and a hybrid parallelization. For data migration, the stack-based intra-cluster communication allows a very low memory footprint for data migration and the RLE leads to efficient updates of connectivity information. Our benchmark is based on a shallow water simulation on a dynamically adaptive grid. We conducted performance studies for MPI-only and hybrid parallelizations, yielding an efficiency of over 90% on 256 cores. Furthermore, we demonstrate the applicability of cluster-based optimizations on distributed-memory systems.We like to thank the Munich Centre of Advanced Computing for for funding this project by providing computing time on the MAC Cluster. This work was partly supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Centre ”Invasive Computing” (SFB/TR 89)

Evaluation of an efficient etack-RLE clustering concept for dynamically adaptive grids

This is the author accepted manuscript. The final version is available from the Society for Industrial and Applied Mathematics via the DOI in this record.Abstract. One approach to tackle the challenge of efficient implementations for parallel PDE simulations on dynamically changing grids is the usage of space-filling curves (SFC). While SFC algorithms possess advantageous properties such as low memory requirements and close-to-optimal partitioning approaches with linear complexity, they require efficient communication strategies for keeping and utilizing the connectivity information, in particular for dynamically changing grids. Our approach is to use a sparse communication graph to store the connectivity information and to transfer data block-wise. This permits efficient generation of multiple partitions per memory context (denoted by clustering) which - in combination with a run-length encoding (RLE) - directly leads to elegant solutions for shared, distributed and hybrid parallelization and allows cluster-based optimizations. While previous work focused on specific aspects, we present in this paper an overall compact summary of the stack-RLE clustering approach completed by aspects on the vertex-based communication that ease up understanding the approach. The central contribution of this work is the proof of suitability of the stack-RLE clustering approach for an efficient realization of different, relevant building blocks of Scientific Computing methodology and real-life CSE applications: We show 95% strong scalability for small-scale scalability benchmarks on 512 cores and weak scalability of over 90% on 8192 cores for finite-volume solvers and changing grid structure in every time step; optimizations of simulation data backends by writer tasks; comparisons of analytical benchmarks to analyze the adaptivity criteria; and a Tsunami simulation as a representative real-world showcase of a wave propagation for our approach which reduces the overall workload by 95% for parallel fully-adaptive mesh refinement and, based on a comparison with SFC-ordered regular grid cells, reduces the computation time by a factor of 7.6 with improved results and a factor of 62.2 with results of similar accuracy of buoy station dataThis work was partly supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Centre “Invasive Computing” (SFB/TR 89)

SFC-based Communication Metadata Encoding for Adaptive Mesh

This volume of the series “Advances in Parallel Computing” contains the proceedings of the International Conference on Parallel Programming – ParCo 2013 – held from 10 to 13 September 2013 in Garching, Germany. The conference was hosted by the Technische Universität München (Department of Informatics) and the Leibniz Supercomputing Centre.The present paper studies two adaptive mesh refinement (AMR) codes whose grids rely on recursive subdivison in combination with space-filling curves (SFCs). A non-overlapping domain decomposition based upon these SFCs yields several well-known advantageous properties with respect to communication demands, balancing, and partition connectivity. However, the administration of the meta data, i.e. to track which partitions exchange data in which cardinality, is nontrivial due to the SFC’s fractal meandering and the dynamic adaptivity. We introduce an analysed tree grammar for the meta data that restricts it without loss of information hierarchically along the subdivision tree and applies run length encoding. Hence, its meta data memory footprint is very small, and it can be computed and maintained on-the-fly even for permanently changing grids. It facilitates a forkjoin pattern for shared data parallelism. And it facilitates replicated data parallelism tackling latency and bandwidth constraints respectively due to communication in the background and reduces memory requirements by avoiding adjacency information stored per element. We demonstrate this at hands of shared and distributed parallelized domain decompositions.This work was supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Centre “Invasive Computing (SFB/TR 89). It is partially based on work supported by Award No. UK-c0020, made by the King Abdullah University of Science and Technology (KAUST)