Exploring the Vision Processing Unit as Co-Processor for Inference

The success of the exascale supercomputer is largely debated to remain dependent on novel breakthroughs in technology that effectively reduce the power consumption and thermal dissipation requirements. In this work, we consider the integration of co-processors in high-performance computing (HPC) to enable low-power, seamless computation offloading of certain operations. In particular, we explore the so-called Vision Processing Unit (VPU), a highly-parallel vector processor with a power envelope of less than 1W. We evaluate this chip during inference using a pre-trained GoogLeNet convolutional network model and a large image dataset from the ImageNet ILSVRC challenge. Preliminary results indicate that a multi-VPU configuration provides similar performance compared to reference CPU and GPU implementations, while reducing the thermal-design power (TDP) up to 8x in comparison.The experimental results were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC Centre for High-Performance Com- puting (PDC-HPC). The work was funded by the European Commission through the SAGE project (Grant agreement no. 671500 / http://www.sagestorage.eu).Postprint (author's final draft

MetH: A family of high-resolution and variable-shape image challenges

High-resolution and variable-shape images have not yet been properly addressed by the AI community. The approach of down-sampling data often used with convolutional neural networks is sub-optimal for many tasks, and has too many drawbacks to be considered a sustainable alternative. In sight of the increasing importance of problems that can benefit from exploiting high-resolution (HR) and variable-shape, and with the goal of promoting research in that direction, we introduce a new family of datasets (MetH). The four proposed problems include two image classification, one image regression and one super resolution task. Each of these datasets contains thousands of art pieces captured by HR and variable-shape images, labeled by experts at the Metropolitan Museum of Art. We perform an analysis, which shows how the proposed tasks go well beyond current public alternatives in both pixel size and aspect ratio variance. At the same time, the performance obtained by popular architectures on these tasks shows that there is ample room for improvement. To wrap up the relevance of the contribution we review the fields, both in AI and high-performance computing, that could benefit from the proposed challenges.This work is partially supported by the Intel-BSC Exascale Lab agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, and by the Generalitat de Catalunya (contracts 2017-SGR-1414).Preprin

10181 Abstracts Collection -- Program Development for Extreme-Scale Computing

From May 2nd to May 7th, 2010, the Dagstuhl Seminar 10181 ``Program Development for Extreme-Scale Computing \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. Links to extended abstracts or full papers are provided, if available

Crowd simulation and visualization

Large-scale simulation and visualization are essential topics in areas as different as sociology, physics, urbanism, training, entertainment among others. This kind of systems requires a vast computational power and memory resources commonly available in High Performance Computing HPC platforms. Currently, the most potent clusters have heterogeneous architectures with hundreds of thousands and even millions of cores. The industry trends inferred that exascale clusters would have thousands of millions. The technical challenges for simulation and visualization process in the exascale era are intertwined with difficulties in other areas of research, including storage, communication, programming models and hardware. For this reason, it is necessary prototyping, testing, and deployment a variety of approaches to address the technical challenges identified and evaluate the advantages and disadvantages of each proposed solution. The focus of this research is interactive large-scale crowd simulation and visualization. To exploit to the maximum the capacity of the current HPC infrastructure and be prepared to take advantage of the next generation. The project develops a new approach to scale crowd simulation and visualization on heterogeneous computing cluster using a task-based technique. Its main characteristic is hardware agnostic. It abstracts the difficulties that imply the use of heterogeneous architectures like memory management, scheduling, communications, and synchronization — facilitating development, maintenance, and scalability. With the goal of flexibility and take advantage of computing resources as best as possible, the project explores different configurations to connect the simulation with the visualization engine. This kind of system has an essential use in emergencies. Therefore, urban scenes were implemented as realistic as possible; in this way, users will be ready to face real events. Path planning for large-scale crowds is a challenge to solve, due to the inherent dynamism in the scenes and vast search space. A new path-finding algorithm was developed. It has a hierarchical approach which offers different advantages: it divides the search space reducing the problem complexity, it can obtain a partial path instead of wait for the complete one, which allows a character to start moving and compute the rest asynchronously. It can reprocess only a part if necessary with different levels of abstraction. A case study is presented for a crowd simulation in urban scenarios. Geolocated data are used, they were produced by mobile devices to predict individual and crowd behavior and detect abnormal situations in the presence of specific events. It was also address the challenge of combining all these individual’s location with a 3D rendering of the urban environment. The data processing and simulation approach are computationally expensive and time-critical, it relies thus on a hybrid Cloud-HPC architecture to produce an efficient solution. Within the project, new models of behavior based on data analytics were developed. It was developed the infrastructure to be able to consult various data sources such as social networks, government agencies or transport companies such as Uber. Every time there is more geolocation data available and better computation resources which allow performing analysis of greater depth, this lays the foundations to improve the simulation models of current crowds. The use of simulations and their visualization allows to observe and organize the crowds in real time. The analysis before, during and after daily mass events can reduce the risks and associated logistics costs.La simulación y visualización a gran escala son temas esenciales en áreas tan diferentes como la sociología, la física, el urbanismo, la capacitación, el entretenimiento, entre otros. Este tipo de sistemas requiere una gran capacidad de cómputo y recursos de memoria comúnmente disponibles en las plataformas de computo de alto rendimiento. Actualmente, los equipos más potentes tienen arquitecturas heterogéneas con cientos de miles e incluso millones de núcleos. Las tendencias de la industria infieren que los equipos en la era exascale tendran miles de millones. Los desafíos técnicos en el proceso de simulación y visualización en la era exascale se entrelazan con dificultades en otras áreas de investigación, incluidos almacenamiento, comunicación, modelos de programación y hardware. Por esta razón, es necesario crear prototipos, probar y desplegar una variedad de enfoques para abordar los desafíos técnicos identificados y evaluar las ventajas y desventajas de cada solución propuesta. El foco de esta investigación es la visualización y simulación interactiva de multitudes a gran escala. Aprovechar al máximo la capacidad de la infraestructura actual y estar preparado para aprovechar la próxima generación. El proyecto desarrolla un nuevo enfoque para escalar la simulación y visualización de multitudes en un clúster de computo heterogéneo utilizando una técnica basada en tareas. Su principal característica es que es hardware agnóstico. Abstrae las dificultades que implican el uso de arquitecturas heterogéneas como la administración de memoria, las comunicaciones y la sincronización, lo que facilita el desarrollo, el mantenimiento y la escalabilidad. Con el objetivo de flexibilizar y aprovechar los recursos informáticos lo mejor posible, el proyecto explora diferentes configuraciones para conectar la simulación con el motor de visualización. Este tipo de sistemas tienen un uso esencial en emergencias. Por lo tanto, se implementaron escenas urbanas lo más realistas posible, de esta manera los usuarios estarán listos para enfrentar eventos reales. La planificación de caminos para multitudes a gran escala es un desafío a resolver, debido al dinamismo inherente en las escenas y el vasto espacio de búsqueda. Se desarrolló un nuevo algoritmo de búsqueda de caminos. Tiene un enfoque jerárquico que ofrece diferentes ventajas: divide el espacio de búsqueda reduciendo la complejidad del problema, puede obtener una ruta parcial en lugar de esperar a la completa, lo que permite que un personaje comience a moverse y calcule el resto de forma asíncrona, puede reprocesar solo una parte si es necesario con diferentes niveles de abstracción. Se presenta un caso de estudio para una simulación de multitud en escenarios urbanos. Se utilizan datos geolocalizados producidos por dispositivos móviles para predecir el comportamiento individual y público y detectar situaciones anormales en presencia de eventos específicos. También se aborda el desafío de combinar la ubicación de todos estos individuos con una representación 3D del entorno urbano. Dentro del proyecto, se desarrollaron nuevos modelos de comportamiento basados ¿¿en el análisis de datos. Se creo la infraestructura para poder consultar varias fuentes de datos como redes sociales, agencias gubernamentales o empresas de transporte como Uber. Cada vez hay más datos de geolocalización disponibles y mejores recursos de cómputo que permiten realizar un análisis de mayor profundidad, esto sienta las bases para mejorar los modelos de simulación de las multitudes actuales. El uso de simulaciones y su visualización permite observar y organizar las multitudes en tiempo real. El análisis antes, durante y después de eventos multitudinarios diarios puede reducir los riesgos y los costos logísticos asociadosPostprint (published version

Towards resilient EU HPC systems: A blueprint

This document aims to spearhead a Europe-wide discussion on HPC system resilience and to help the European HPC community define best practices for resilience. We analyse a wide range of state-of-the-art resilience mechanisms and recommend the most effective approaches to employ in large-scale HPC systems. Our guidelines will be useful in the allocation of available resources, as well as guiding researchers and research funding towards the enhancement of resilience approaches with the highest priority and utility. Although our work is focused on the needs of next generation HPC systems in Europe, the principles and evaluations are applicable globally.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the projects ECOSCALE (grant agreement No 671632), EPI (grant agreement No 826647), EuroEXA (grant agreement No 754337), Eurolab4HPC (grant agreement No 800962), EVOLVE (grant agreement No 825061), EXA2PRO (grant agreement No 801015), ExaNest (grant agreement No 671553), ExaNoDe (grant agreement No 671578), EXDCI-2 (grant agreement No 800957), LEGaTO (grant agreement No 780681), MB2020 (grant agreement No 779877), RECIPE (grant agreement No 801137) and SDK4ED (grant agreement No 780572). The work was also supported by the European Commission’s Seventh Framework Programme under the projects CLERECO (grant agreement No 611404), the NCSA-Inria-ANL-BSC-JSCRiken-UTK Joint-Laboratory for Extreme Scale Computing – JLESC (https://jlesc.github.io/), OMPI-X project (No ECP-2.3.1.17) and the Spanish Government through Severo Ochoa programme (SEV-2015-0493). This work was sponsored in part by the U.S. Department of Energy's Office of Advanced Scientific Computing Research, program managers Robinson Pino and Lucy Nowell. This manuscript has been authored by UT-Battelle, LLC under Contract No DE-AC05-00OR22725 with the U.S. Department of Energy.Preprin

Engineering at San Jose State University, Winter 2014

https://scholarworks.sjsu.edu/engr_news/1012/thumbnail.jp

Evaluation of low-power architectures in a scientific computing environment

HPC (High Performance Computing) represents, together with theory and experiments, the third pillar of science. Through HPC, scientists can simulate phenomena otherwise impossible to study. The need of performing larger and more accurate simulations requires to HPC to improve every day. HPC is constantly looking for new computational platforms that can improve cost and power efficiency. The Mont-Blanc project is a EU funded research project that targets to study new hardware and software solutions that can improve efficiency of HPC systems. The vision of the project is to leverage the fast growing market of mobile devices to develop the next generation supercomputers. In this work we contribute to the objectives of the Mont-Blanc project by evaluating performance of production scientific applications on innovative low power architectures. In order to do so, we describe our experiences porting and evaluating sate of the art scientific applications on the Mont-Blanc prototype, the first HPC system built with commodity low power embedded technology. We then extend our study to compare off-the-shelves ARMv8 platforms. We finally discuss the most impacting issues encountered during the development of the Mont-Blanc prototype system