LEONARDO: A Pan-European Pre-Exascale Supercomputer for HPC and AI applications

A new pre-exascale computer cluster has been designed to foster scientific progress and competitive innovation across European research systems, it is called LEONARDO. This paper describes thegeneral architecture of the system and focuses on the technologies adopted for its GPU-accelerated partition. High density processing elements, fast data movement capabilities and mature software stack collections allow the machine to run intensive workloads in a flexible and scalable way. Scientific applications from traditional High Performance Computing (HPC) as well as emerging Artificial Intelligence (AI) domains can benefit from this large apparatus in terms of time and energy to solution

Composable architecture for rack scale big data computing

The rapid growth of cloud computing, both in terms of the spectrum and volume of cloud workloads, necessitate re-visiting the traditional rack-mountable servers based datacenter design. Next generation datacenters need to offer enhanced support for: (i) fast changing system configuration requirements due to workload constraints, (ii) timely adoption of emerging hardware technologies, and (iii) maximal sharing of systems and subsystems in order to lower costs. Disaggregated datacenters, constructed as a collection of individual resources such as CPU, memory, disks etc., and composed into workload execution units on demand, are an interesting new trend that can address the above challenges. In this paper, we demonstrated the feasibility of composable systems through building a rack scale composable system prototype using PCIe switch. Through empirical approaches, we develop assessment of the opportunities and challenges for leveraging the composable architecture for rack scale cloud datacenters with a focus on big data and NoSQL workloads. In particular, we compare and contrast the programming models that can be used to access the composable resources, and developed the implications for the network and resource provisioning and management for rack scale architecture

Action Classification in Human Robot Interaction Cells in Manufacturing

Action recognition has become a prerequisite approach to fluent Human-Robot Interaction (HRI) due to a high degree of movement flexibility. With the improvements in machine learning algorithms, robots are gradually transitioning into more human-populated areas. However, HRI systems demand the need for robots to possess enough cognition. The action recognition algorithms require massive training datasets, structural information of objects in the environment, and less expensive models in terms of computational complexity. In addition, many such algorithms are trained on datasets derived from daily activities. The algorithms trained on non-industrial datasets may have an unfavorable impact on implementing models and validating actions in an industrial context. This study proposed a lightweight deep learning model for classifying low-level actions in an assembly setting. The model is based on optical flow feature elicitation and mobilenetV2-SSD action classification and is trained and assessed on an actual industrial activities’ dataset. The experimental outcomes show that the presented method is futuristic and does not require extensive preprocessing; therefore, it can be promising in terms of the feasibility of action recognition for mutual performance monitoring in real-world HRI applications. The test result shows 80% accuracy for low-level RGB action classes. The study’s primary objective is to generate experimental results that may be used as a reference for future HRI algorithms based on the InHard dataset

ECHOFS: a scheduler-guided temporary filesystem to leverage node-local NVMS

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The growth in data-intensive scientific applications poses strong demands on the HPC storage subsystem, as data needs to be copied from compute nodes to I/O nodes and vice versa for jobs to run. The emerging trend of adding denser, NVM-based burst buffers to compute nodes, however, offers the possibility of using these resources to build temporary file systems with specific I/O optimizations for a batch job. In this work, we present echofs, a temporary filesystem that coordinates with the job scheduler to preload a job's input files into node-local burst buffers. We present the results measured with NVM emulation, and different FS backends with DAX/FUSE on a local node, to show the benefits of our proposal and such coordination.This work was partially supported by the Spanish Ministry of Science and Innovation under the TIN2015–65316 grant, the Generalitat de Catalunya under contract 2014– SGR–1051, as well as the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement no. 671951 (NEXTGenIO). Source code available at https://github.com/bsc-ssrg/echofs.Peer ReviewedPostprint (author's final draft

LEONARDO: A Pan-European Pre-Exascale Supercomputer for HPC and AI Applications

A new pre-exascale computer cluster has been designed to foster scientific progress and competitive innovation across European research systems, it is called LEONARDO. This paper describes the general architecture of the system and focuses on the technologies adopted for its GPU-accelerated partition. High density processing elements, fast data movement capabilities and mature software stack collections allow the machine to run intensive workloads in a flexible and scalable way. Scientific applications from traditional High Performance Computing (HPC) as well as emerging Artificial Intelligence (AI) domains can benefit from this large apparatus in terms of time and energy to solution.Comment: 16 pages, 5 figures, 7 tables, to be published in Journal of Large Scale Research Facilitie