A job dispatcher for large and heterogeneous HPC systems running modern applications

High-performance Computing (HPC) systems have become essential instruments in our modern society. As they get closer to exascale performance, HPC systems become larger in size and more heterogeneous in their computing resources. With recent advances in AI, HPC systems are also increasingly being used for applications that employ many short jobs with strict timing requirements. HPC job dispatchers need to therefore adopt techniques to go beyond the capabilities of those developed for small or homogeneous systems, or for traditional compute-intensive applications. In this paper, we present a job dispatcher suitable for today's large and heterogeneous systems running modern applications. Unlike its predecessors, our dispatcher solves the entire dispatching problem using Constraint Programming (CP) with a model size independent of the system size. Experimental results based on a simulation study show that our approach can bring about significant performance gains over the existing CP-based dispatchers in a large or heterogeneous system

Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution

Given a set of images containing objects from the same category, the task of image co-localization is to identify and localize each instance. This paper shows that this problem can be solved by a simple but intriguing idea, that is, a common object detector can be learnt by making its detection confidence scores distributed like those of a strongly supervised detector. More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them. Thus, we devise an entropy-based objective function to enforce the above property when learning the common object detector. Once the detector is learnt, we resort to a segmentation approach to refine the localization. We show that despite its simplicity, our approach outperforms state-of-the-art methods.Comment: Accepted to Proc. European Conf. Computer Vision 201

DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems

As we approach the exascale era, the size and complexity of HPC systems continues to increase, raising concerns about their manageability and sustainability. For this reason, more and more HPC centers are experimenting with fine-grained monitoring coupled with Operational Data Analytics (ODA) to optimize efficiency and effectiveness of system operations. However, while monitoring is a common reality in HPC, there is no well-stated and comprehensive list of requirements, nor matching frameworks, to support holistic and online ODA. This leads to insular ad-hoc solutions, each addressing only specific aspects of the problem. In this paper we propose Wintermute, a novel generic framework to enable online ODA on large-scale HPC installations. Its design is based on the results of a literature survey of common operational requirements. We implement Wintermute on top of the holistic DCDB monitoring system, offering a large variety of configuration options to accommodate the varying requirements of ODA applications. Moreover, Wintermute is based on a set of logical abstractions to ease the configuration of models at a large scale and maximize code re-use. We highlight Wintermute's flexibility through a series of practical case studies, each targeting a different aspect of the management of HPC systems, and then demonstrate the small resource footprint of our implementation.Comment: Accepted for publication at the 29th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2020

Proactive control of cresting in homogeneous oil reservoirs : an experimental study

Cresting in horizontal wells is a well-known reservoir problem usually described as the insurgence of effluent(s) (unwanted water and or gas) through the perforation of the well, which is produced together with oil. Cresting is majorly affected by pressure drop, resulting in uneconomic oil production rates and large volumes of oil could be left behind due to premature shut-in of the well. This study experimentally investigates the use of electromagnetic valve in proactively controlling production of water during cresting from homogeneous thick- and thin-oil rim reservoirs, based on the principle of capillarity (reservoir wettability) and effluents (water and gas) breakthrough time. A time, half the approximated initial effluents breakthrough time, was pre-set for the electromagnetic valve to close. The valve closed almost immediately at the set-time thereby shutting oil production temporarily, causing the water and gas height levels to recede by gravity and capillarity. The efficiency of this technique was compared with an uncontrolled simulation case, in terms of cumulative oil produced and water produced at the same overall production time. Using the cresting control procedure, higher percentages in oil produced and water reduction were observed in the cases controlled proactively. An increment of 3.56% in oil produced and decrement in cumulative water produced of 9.96% were observed for the thick-oil rim reservoir while little increment in oil produced of 0.7% and lower water reduction of 1.03% were observed in the thin-oil rim reservoir. Hence, the effectiveness of the cresting control procedure depends on the oil-column height in the reservoir

Patch-level spatial layout for classification and weakly supervised localization

International audienceWe propose a discriminative patch-level spatial layout model suitable for training with weak supervision. We start from a block-sparse model of patch appearance based on the normalized Fisher vector representation. The appearance model is responsible for i) selecting a discriminative subset of visual words, and ii) identifying distinctive patches assigned to the selected subset. These patches are further filtered by a sparse spatial model operating on a novel representation of pairwise patch layout. We have evaluated the proposed pipeline in image classification and weakly supervised localization experiments on a public traffic sign dataset. The results show significant advantage of the proposed spatial model over state of the art appearance models

Un examen actualizado de la percepción de las barreras para la implementación de la farmacogenómica y la utilidad de los pares fármaco/gen en América Latina y el Caribe

La farmacogenómica (PGx) se considera un campo emergente en los países en desarrollo. La investigación sobre PGx en la región de América Latina y el Caribe (ALC) sigue siendo escasa, con información limitada en algunas poblaciones. Por lo tanto, las extrapolaciones son complicadas, especialmente en poblaciones mixtas. En este trabajo, revisamos y analizamos el conocimiento farmacogenómico entre la comunidad científica y clínica de ALC y examinamos las barreras para la aplicación clínica. Realizamos una búsqueda de publicaciones y ensayos clínicos en este campo en todo el mundo y evaluamos la contribución de ALC. A continuación, realizamos una encuesta regional estructurada que evaluó una lista de 14 barreras potenciales para la aplicación clínica de biomarcadores en función de su importancia. Además, se analizó una lista emparejada de 54 genes/fármacos para determinar una asociación entre los biomarcadores y la respuesta a la medicina genómica. Esta encuesta se comparó con una encuesta anterior realizada en 2014 para evaluar el progreso en la región. Los resultados de la búsqueda indicaron que los países de América Latina y el Caribe han contribuido con el 3,44% del total de publicaciones y el 2,45% de los ensayos clínicos relacionados con PGx en todo el mundo hasta el momento. Un total de 106 profesionales de 17 países respondieron a la encuesta. Se identificaron seis grandes grupos de obstáculos. A pesar de los continuos esfuerzos de la región en la última década, la principal barrera para la implementación de PGx en ALC sigue siendo la misma, la "necesidad de directrices, procesos y protocolos para la aplicación clínica de la farmacogenética/farmacogenómica". Las cuestiones de coste-eficacia se consideran factores críticos en la región. Los puntos relacionados con la reticencia de los clínicos son actualmente menos relevantes. Según los resultados de la encuesta, los pares gen/fármaco mejor clasificados (96%-99%) y percibidos como importantes fueron CYP2D6/tamoxifeno, CYP3A5/tacrolimus, CYP2D6/opioides, DPYD/fluoropirimidinas, TMPT/tiopurinas, CYP2D6/antidepresivos tricíclicos, CYP2C19/antidepresivos tricíclicos, NUDT15/tiopurinas, CYP2B6/efavirenz y CYP2C19/clopidogrel. En conclusión, aunque la contribución global de los países de ALC sigue siendo baja en el campo del PGx, se ha observado una mejora relevante en la región. La percepción de la utilidad de las pruebas PGx en la comunidad biomédica ha cambiado drásticamente, aumentando la concienciación entre los médicos, lo que sugiere un futuro prometedor en las aplicaciones clínicas de PGx en ALC.Pharmacogenomics (PGx) is considered an emergent field in developing countries. Research on PGx in the Latin American and the Caribbean (LAC) region remains scarce, with limited information in some populations. Thus, extrapolations are complicated, especially in mixed populations. In this paper, we reviewed and analyzed pharmacogenomic knowledge among the LAC scientific and clinical community and examined barriers to clinical application. We performed a search for publications and clinical trials in the field worldwide and evaluated the contribution of LAC. Next, we conducted a regional structured survey that evaluated a list of 14 potential barriers to the clinical implementation of biomarkers based on their importance. In addition, a paired list of 54 genes/drugs was analyzed to determine an association between biomarkers and response to genomic medicine. This survey was compared to a previous survey performed in 2014 to assess progress in the region. The search results indicated that Latin American and Caribbean countries have contributed 3.44% of the total publications and 2.45% of the PGx-related clinical trials worldwide thus far. A total of 106 professionals from 17 countries answered the survey. Six major groups of barriers were identified. Despite the region’s continuous efforts in the last decade, the primary barrier to PGx implementation in LAC remains the same, the “need for guidelines, processes, and protocols for the clinical application of pharmacogenetics/pharmacogenomics”. Cost-effectiveness issues are considered critical factors in the region. Items related to the reluctance of clinicians are currently less relevant. Based on the survey results, the highest ranked (96%–99%) gene/drug pairs perceived as important were CYP2D6/tamoxifen, CYP3A5/tacrolimus, CYP2D6/opioids, DPYD/fluoropyrimidines, TMPT/thiopurines, CYP2D6/tricyclic antidepressants, CYP2C19/tricyclic antidepressants, NUDT15/thiopurines, CYP2B6/efavirenz, and CYP2C19/clopidogrel. In conclusion, although the global contribution of LAC countries remains low in the PGx field, a relevant improvement has been observed in the region. The perception of the usefulness of PGx tests in biomedical community has drastically changed, raising awareness among physicians, which suggests a promising future in the clinical applications of PGx in LAC

Author Correction: Elucidating causative gene variants in hereditary Parkinson’s disease in the Global Parkinson’s Genetics Program (GP2)

Correction to: npj Parkinson’s Disease, published online 27 June 2023 In this article the Global Parkinson’s Genetics Program (GP2) members names and affiliations were missing in the main author list of the Original article which are listed in the below