High Performance Parallel Algorithms for the Tucker Decomposition of Sparse Tensors

International audience—We investigate an efficient parallelization of a class of algorithms for the well-known Tucker decomposition of general N-dimensional sparse tensors. The targeted algorithms are iterative and use the alternating least squares method. At each iteration, for each dimension of an N-dimensional input tensor, the following operations are performed: (i) the tensor is multiplied with (N − 1) matrices (TTMc step); (ii) the product is then converted to a matrix; and (iii) a few leading left singular vectors of the resulting matrix are computed (TRSVD step) to update one of the matrices for the next TTMc step. We propose an efficient parallelization of these algorithms for the current parallel platforms with multicore nodes. We discuss a set of preprocessing steps which takes all computational decisions out of the main iteration of the algorithm and provides an intuitive shared-memory parallelism for the TTM and TRSVD steps. We propose a coarse and a fine-grain parallel algorithm in a distributed memory environment, investigate data dependencies, and identify efficient communication schemes. We demonstrate how the computation of singular vectors in the TRSVD step can be carried out efficiently following the TTMc step. Finally, we develop a hybrid MPI-OpenMP implementation of the overall algorithm and report scalability results on up to 4096 cores on 256 nodes of an IBM BlueGene/Q supercomputer

Profunda femoris artery pseudoaneurysm after surgery and trauma

Pseudoaneurysms of the profunda femoris artery have been reported following different types of trauma and from orthopedic procedures performed in the proximal femur. Two cases of profunda femoris artery pseudoaneurysm with two rare causes are presented. The first one is a core decompression of femoral head for osteonecrosis and the second one is a proximal femur fracture nailing. Awareness and careful follow-up are the key issues for the early diagnosis

Fast BEM solution for scattering problems using Quantized Tensor Train format

International audienceIt is common to accelerate the boundary element method by compression methods (FMM, H-Matrix / ACA) that enable a more accurate solution or a solution in higher frequency. In this work, we present a compression method based on a transformation of the linear system into Tensor-Train format by the quantization technique. The method is applied to a scattering problem by a canonical object with a regular mesh and improves the performance obtained by the previous methods

Rupture of the meniscofibular ligament

The meniscofibular ligament is an anatomically defined ligament of the knee in humans. However, there are no data regarding the prognosis following injury to this ligament. Our case was a 42-year-old man who presented at our clinic with pain of the lateral side of his left knee. MRI of his left knee revealed the rupture of the meniscofibular ligament. The mechanism of injury was consistent with anatomical and mechanical studies of the meniscofibular ligament. The patient was treated conservatively for 1 year, but his pain did not resolve completely. A case series of patients with the same injury is required to establish an effective treatment for this rare injury

The Effects of Pupil Control Ideology of Teachers on their Conflict Management Strategies

The aim of this study was to determine teachers' perspectives on conflict management strategies and further to determine the effects of pupil control ideologies on their conflict management strategies. 120 primary and secondary school teachers were administered a Likert type questionnaire. The data collected were analyzed through multiple regression analyses and the teachers’ perspectives on conflict management strategies were determined. Moreover, the effects of pupil control ideology of teachers on their conflict management strategies were revealed. The results of this study suggested that teachers preferred integration reconciliation strategy in conflict resolution the most, and domination strategy the least. It was observed that, among the conflict management strategies, teachers’ pupil control ideologies predicted domination strategy positively and integration-reconciliation negatively. Certain suggestions were made based on the findings of the study

Les Stratégies de Partitionnement et de Communication pour Factorisation des Matrices Non-négatives Creuses

Non-negative matrix factorization (NMF), the problem of finding two non-negative low-rank factors whose product approximates an input matrix, is a useful tool for many data mining and scientific applications such as topic modeling in text mining and blind source separation in microscopy.In this paper, we focus on scaling algorithms for NMF to very large sparse datasets and massively parallel machines by employing effective algorithms, communication patterns, and partitioning schemes that leverage the sparsity of the input matrix. In the case of machine learning workflow, the computations after SpMM must deal with dense matrices, as Sparse-Dense matrix multiplication will result in a dense matrix. Hence, the partitioning strategy considering only SpMM will result in a huge imbalance in the overall workflow especially on computations after SpMM and in this specific case of NMF on non-negative least squares computations. Towards this, we consider two previous works developed for related problems, one that uses a fine-grained partitioning strategy using a point-to-point communication pattern and on that uses a checkerboard partitioning strategy using a collective-based communication pattern.We show that a combination of the previous approaches balances the demands of the various computations within NMF algorithms and achieves high efficiency and scalability. From the experiments, we could see that our proposed algorithm communicates atleast 4x less than the collective and achieves upto 100x speed up over the baseline FAUN on real world datasets. Our algorithm was experimented in two different super computing platforms and we could scale up to 32000 processors on Bluegene/Q.La factorisation de matrice non-négative (NMF), le problème de trouver deux facteurs de rang faible non négatifs dont le produit se rapproche d'une matrice d'entrée, est un outil utile pour de nombreuses applications scientifiques et d'exploration de données telles que la modélisation de textes et la séparation de signaux en microscopie.Dans cet article, nous etudions les algorithmes passant à l'échelle pour NMF à de très grands ensembles de données creuses et des machines massivement parallèles en utilisant des algorithmes efficaces, des modèles de communication et des schémas de partitionnement qui exploitent la structure creuse de la matrice.Dans le cadre de cet algorithme, les calculs après SpMM doivent traiter des matrices denses, car la multiplication SpMM produira une matrice dense.Par conséquent, la stratégie de partitionnement ne prenant en compte que SpMM entraînera un déséquilibre énorme dans l'algorithme global, en particulier sur les calculs après SpMM et dans ce cas spécifique de NMF sur les calculs de moindres carrés non négatifs.À cet égard, nous considérons deux travaux antérieurs développés pour des problèmes connexes, l'un utilisant une stratégie de partitionnement de granularité ffine utilisant un modèle de communication ``point-to-point'' et utilisant une stratégie de partitionnement en damier utilisant un modèle de communication collectif.Nous montrons qu'une combinaison des approches précédentes permet d'équilibrer les exigences des divers calculs au sein des algorithmes NMF et permet d'obtenir une efficacité et une évolutivité élevées. À partir des expériences, nous avons constaté que notre algorithme proposé communique au moins4x moins que le collectif et atteint jusqu'à 100 fois la vitesse de base sur les jeux de données réels. Notre algorithme a été expérimenté sur deux plates-formes superinformatiques différentes et nous avons pu passer à 32 000 processeurs sur Bluegene / Q

Amyloid Goiter Associated with Amyloidosis Secondary to Rheumatoid Arthritis

Amyloidosis refers to a variety of conditions in which amyloid proteins are abnormally deposited in organs and/or tissues. The most common forms of systemic amyloidosis are primary amyloidosis (PA) of light chains and secondary amyloidosis (SA) caused by chronic inflammatory diseases such as rheumatoid arthritis (RA). Although involvement of the thyroid gland by amyloid is a relatively common phenomenon, clinically significant enlargement of the thyroid owing to amyloid deposition is a rare occurrence. In SA, the deposition of amyloid associated (AA) protein is associated with atrophy of thyroid follicles. The clinical picture of these patients is characterized by rapid, painless thyroid gland enlargement which may be associated with dysphagia, dyspnea, or hoarseness. Thyroid function is not impaired in most cases. Although amyloid goitre secondary to systemic amyloidosis due to chronic inflammatory diseases is relatively common, specifically related to RA is much more uncommon one and it is reported less in the literature. In this report, A 52-old-year female patient with amyloid goiter associated with amyloidosis secondary to rheumatoid arthritis is presented