Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved

Towards an Active Foveated Approach to Computer Vision

In this paper, a series of experimental methods are presented explaining a new approach towards active foveated Computer Vision (CV). This is a collaborative effort between researchers at CONICET Mendoza Technological Scientific Center from Argentina, Argonne National Laboratory (ANL), and Loyola University Chicago from the US. The aim is to advance new CV approaches more in line with those found in biological agents in order to bring novel solutions to the main problems faced by current CV applications. Basically this work enhance Self-supervised (SS) learning, incorporating foveated vision plus saccadic behavior in order to improve training and computational efficiency without reducing performance significantly. This paper includes a compendium of methods’ explanations, and since this is a work that is currently in progress, only preliminary results are provided. We also make our code fully available

Towards high-end scalability on biologically-inspired computational models

The interdisciplinary field of neuroscience has made significant progress in recent decades, providing the scientific community in general with a new level of understanding on how the brain works beyond the store-and-fire model found in traditional neural networks. Meanwhile, Machine Learning (ML) based on established models has seen a surge of interest in the High Performance Computing (HPC) community, especially through the use of high-end accelerators, such as Graphical Processing Units(GPUs), including HPC clusters of same. In our work, we are motivated to exploit these high-performance computing developments and understand the scaling challenges for new–biologically inspired–learning models on leadership-class HPC resources. These emerging models feature sparse and random connectivity profiles that map to more loosely-coupled parallel architectures with a large number of CPU cores per node. Contrasted with traditional ML codes, these methods exploit loosely-coupled sparse data structures as opposed to tightly-coupled dense matrix computations, which benefit from SIMD-style parallelism found on GPUs. In this paper we introduce a hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) parallelization scheme to accelerate and scale our computational model based on the dynamics of cortical tissue. We ran computational tests on a leadership class visualization and analysis cluster at Argonne National Laboratory. We include a study of strong and weak scaling, where we obtained parallel efficiency measures with a minimum above 87% and a maximum above 97% for simulations of our biologically inspired neural network on up to 64 computing nodes running 8 threads each. This study shows promise of the MPI+OpenMP hybrid approach to support flexible and biologically-inspired computational experimental scenarios. In addition, we present the viability in the application of these strategies in high-end leadership computers in the future.Fil: Dematties, Dario Jesus. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; ArgentinaFil: Thiruvathukal, George K.. University of Chicago; Estados UnidosFil: Rizzi, Silvio. Argonne National Laboratory; Estados UnidosFil: Wainselboim, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; ArgentinaFil: Zanutto, Bonifacio Silvano. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentin

Preface IEEE LDAV 2023

Join us for the 13th IEEE Symposium on Large Data Analysis and Visualization (IEEE LDAV) on Monday, October 23rd 2023 collocated with IEEE VIS 2023 in Melbourne, Victoria, Australia.<br/

Self-administration of omalizumab: why not? A literature review and expert opinion

Introduction: Omalizumab is used to treat severe uncontrolled allergic asthma and chronic spontaneous urticaria (CSU), and is approved for self-administration in prefilled syringes. It is thus important to understand the advantages, critical issues, and indications for home administration.Areas covered: The present review summarizes the available evidence on home administration of omalizumab in asthma and CSU to illustrate the advantages derived from self-administration of patients in this setting.Expert opinion: The available data suggest that patients can safely administer biologics at home with suitable training, and that home administration is time saving and cost-effective. The majority of patients with severe asthma or CSU treated with omalizumab are likely to be suitable candidates for self-administration, which can be proposed to anyone that the clinician deems suitable. In addition to clinicians, pharmacists can also play a key role in managing patients who are prescribed home administration. A practical flow chart is proposed on selection of patients and their management during home administration. Self-administration of biologics can be considered as a valid alternative to traditional injections in a clinical setting, and the evidence has shown that no major issues need to be overcome in terms of safety or efficacy

Complete gene expression profiling of Saccharopolyspora erythraea using GeneChip DNA microarrays

The Saccharopolyspora erythraea genome sequence, recently published, presents considerable divergence from those of streptomycetes in gene organization and function, confirming the remarkable potential of S. erythraea for producing many other secondary metabolites in addition to erythromycin. In order to investigate, at whole transcriptome level, how S. erythraea genes are modulated, a DNA microarray was specifically designed and constructed on the S. erythraea strain NRRL 2338 genome sequence, and the expression profiles of 6494 ORFs were monitored during growth in complex liquid medium

Diseño de protocolo para control de generación distribuido de flujo multimedia

Un aspecto crítico en aplicaciones de tiempo real es el elevado pro-cesamiento para la generación del flujo de datos multimedia de tipo máquina a humano. Dicho procesamiento puede dividirse tanto espacial como temporal-mente para distribuir recursos de procesamiento, memoria o red. Este trabajo describe el diseño de un protocolo de capa de aplicación para el control de la generación de un flujo de datos multimedia en un esquema de mejor esfuerzo, donde dicha generación debe distribuirse entre múltiples nodos para incremen-tar el rendimiento. Proponemos para su implementación la utilización de UDP en capa de transporte, IPv6 con multicast en capa de red y Gigabit Ethernet con control de flujo en capa de enlace de datos. Es un sistema compuesto por un Nodo Administrador encargado del control de generación del flujo multimedia, múltiples Nodos Procesadores encargados de la generación propiamente dicha y uno o más Nodos Integradores encargados de reunir el procesamiento distribui-do y visualizarlo. De esta forma, el presente protocolo se diseña permitiendo que el flujo multimedia a procesar se divida en tareas también denominadas segmentos de procesamiento (Spatial Data Segment, SDS) con identificadores únicos espaciales y temporales. De esta forma el Nodo Administrador puede asignar cada una de las tareas mediante este protocolo, para procesarse, por uno o más Nodos Procesadores y luego enviarse, para integrarse y visualizarse, por determinado Nodo Integrador. El protocolo fue implementado exitosamente en un sistema con nodos tanto homogéneos como heterogéneos, logrando la visua-lización foto realista de volúmenes médicos.Extended Abstract (Poster Track).Sociedad Argentina de Informática e Investigación Operativa (SADIO

Humoral immune response after anti-SARS-CoV-2 vaccine “booster” dose in patients with monoclonal gammopathy of undetermined significance (MGUS)

Not Applicable