Scalable and distributed constrained low rank approximations

Low rank approximation is the problem of finding two low rank factors W and H such that the rank(WH) << rank(A) and A ≈ WH. These low rank factors W and H can be constrained for meaningful physical interpretation and referred as Constrained Low Rank Approximation (CLRA). Like most of the constrained optimization problem, performing CLRA can be computationally expensive than its unconstrained counterpart. A widely used CLRA is the Non-negative Matrix Factorization (NMF) which enforces non-negativity constraints in each of its low rank factors W and H. In this thesis, I focus on scalable/distributed CLRA algorithms for constraints such as boundedness and non-negativity for large real world matrices that includes text, High Definition (HD) video, social networks and recommender systems. First, I begin with the Bounded Matrix Low Rank Approximation (BMA) which imposes a lower and an upper bound on every element of the lower rank matrix. BMA is more challenging than NMF as it imposes bounds on the product WH rather than on each of the low rank factors W and H. For very large input matrices, we extend our BMA algorithm to Block BMA that can scale to a large number of processors. In applications, such as HD video, where the input matrix to be factored is extremely large, distributed computation is inevitable and the network communication becomes a major performance bottleneck. Towards this end, we propose a novel distributed Communication Avoiding NMF (CANMF) algorithm that communicates only the right low rank factor to its neighboring machine. Finally, a general distributed HPC- NMF framework that uses HPC techniques in communication intensive NMF operations and suitable for broader class of NMF algorithms.Ph.D

A genome-wide analysis reveals that the Drosophila transcription factor Lola promotes axon growth in part by suppressing expression of the actin nucleation factor Spire

<p>Abstract</p> <p>Background</p> <p>The phylogenetically conserved transcription factor Lola is essential for many aspects of axon growth and guidance, synapse formation and neural circuit development in <it>Drosophila</it>. To date it has been difficult, however, to obtain an overall view of Lola functions and mechanisms.</p> <p>Results</p> <p>We use expression microarrays to identify the <it>lola</it>-dependent transcriptome in the <it>Drosophila </it>embryo. We find that <it>lola </it>regulates the expression of a large selection of genes that are known to affect each of several <it>lola</it>-dependent developmental processes. Among other loci, we find <it>lola </it>to be a negative regulator of <it>spire</it>, an actin nucleation factor that has been studied for its essential role in oogenesis. We show that <it>spire </it>is expressed in the nervous system and is required for a known <it>lola</it>-dependent axon guidance decision, growth of ISNb motor axons. We further show that reducing <it>spire </it>gene dosage suppresses this aspect of the <it>lola </it>phenotype, verifying that derepression of <it>spire </it>is an important contributor to the axon stalling phenotype of embryonic motor axons in <it>lola </it>mutants.</p> <p>Conclusions</p> <p>These data shed new light on the molecular mechanisms of many <it>lola</it>-dependent processes, and also identify several developmental processes not previously linked to <it>lola </it>that are apt to be regulated by this transcription factor. These data further demonstrate that excessive expression of the actin nucleation factor Spire is as deleterious for axon growth <it>in vivo </it>as is the loss of Spire, thus highlighting the need for a balance in the elementary steps of actin dynamics to achieve effective neuronal morphogenesis.</p

IMPACT OF CONTINUOUS PATIENT COUNSELLING ON KNOWLEDGE, ATTITUDE, AND PRACTICES AND MEDICATION ADHERENCE OF DIABETIC PATIENTS ATTENDING OUTPATIENT PHARMACY SERVICES

ABSTRACTObjective: The morbidity and morbidity associated with diabetes can be drastically reduced by the knowledge about diabetes mellitus and appropriateattitude toward the disease. A study was conducted to assess the level of knowledge, attitude, and practices (KAP) and medication adherence patternsof diabetic patients and effect of pharmacist‑led patient education on KAP and medication adherence patterns in these patients.Methods: 400 diabetic patients of either sex, aged above 18 years were divided randomly into two groups of 200 each as control and the interventiongroups. At the baseline, patients in both the groups were assessed for KAP using KAP Questionnaire and medication adherence using MoriskyAdherence Questionnaire. Patients in the intervention group were counseled both verbally and by distribution of a patient education leaflets at baselineand at three consecutive follow‑ups (1st, 2nd, and 3 months), and patients in the control group were counseled both verbally and by distribution ofpatient education leaflets at the baseline and then on the follow‑up after 3 months. Both the groups were assessed repeatedly for KAP and medicationadherence using same questionnaires after each counseling sessions. The mean scores of KAP and medication adherence, and the fasting blood sugarlevels (FBS) at the baseline and on the follow‑up for control and the intervention groups were analyzed statistically using independent sample t‑testand Mann–Whitney U‑test.rdResults: Of 200 patients in each group, 178 females and 22 males in the intervention group (mean age 57.80±9.878 years) and 179 females and21 males in the control group (mean age 57.57±9.438 years). A statistically significant improvement in the mean KAP and adherence scores wasobserved from the baseline to the final follow‑up in both groups (p≤0.001). The increase in the KAP and medication adherence scores from baselineto the follow‑up in the intervention group was found to be significantly higher than the control group. There was a reduction in the mean FBS frombaseline to the follow‑up in both the groups but a statistically significant higher reduction in the mean FBS was found in the intervention group frombaseline to the final follow‑up when compared to the control group (p &lt; 0.001).Conclusion: A better KAP of diabetic patients about their disease can improve the medication adherence behavior which in turn can improve clinicaloutcomes. The patient education should be a continuous process, and patients should be assessed at every subsequent visit for medication adherenceto achieve better health outcome.Keywords: Diabetes, Adherence, Knowledge, attitude and practices, Patient education

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

Design Principles for Sparse Matrix Multiplication on the GPU

We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on the GPU. Our algorithms expect the sparse input in the popular compressed-sparse-row (CSR) format and thus do not require expensive format conversion. While previous SpMM work concentrates on thread-level parallelism, we additionally focus on latency hiding with instruction-level parallelism and load-balancing. We show, both theoretically and experimentally, that the proposed SpMM is a better fit for the GPU than previous approaches. We identify a key memory access pattern that allows efficient access into both input and output matrices that is crucial to getting excellent performance on SpMM. By combining these two ingredients---(i) merge-based load-balancing and (ii) row-major coalesced memory access---we demonstrate a 4.1x peak speedup and a 31.7% geomean speedup over state-of-the-art SpMM implementations on real-world datasets.Comment: 16 pages, 7 figures, International European Conference on Parallel and Distributed Computing (Euro-Par) 201

A Dynamic Data Driven Application System for Vehicle Tracking

AbstractTracking the movement of vehicles in urban environments using fixed position sensors, mobile sensors, and crowd-sourced data is a challenging but important problem in applications such as law enforcement and defense. A dynamic data driven application system (DDDAS) is described to track a vehicle's movements by repeatedly identifying the vehicle under investigation from live image and video data, predicting probable future locations, and repositioning sensors or retargeting requests for information in order to reacquire the vehicle. An overview of the envisioned system is described that includes image processing algorithms to detect and recapture the vehicle from live image data, a computational framework to predict probable vehicle locations at future points in time, and a power aware data distribution management system to disseminate data and requests for information over ad hoc wireless communication networks. A testbed under development in the midtown area of Atlanta, Georgia in the United States is briefly described