Data Driven Innovation Open Summit: HPC@ENI. Marco Bianchi, ENI

Machine Learning 2. May 19th 2018. Data Driven Innovation 2018. Engineering Department, University of Roma Tre

Data & Analytics

Data Driven Innovation Open Summit: HPC@Eni
Marco Bianchi
19 maggio 2018

HPC@Eni: Imaging
2
ACQUISITION DATA
time
SUBSURFACE IMAGE
depth
IMAGING

HPC@Eni: Relative computational effort of imaging algorithms
3
PSPI
TTI FWI
Isotropic FWI
Anisotropic DVA
Visco-elastic FWI
CRS
Isotropic RTM
TTI RTM
KTA1
100
10
1.000
10.000
2000 2005 2010 2015 2020

HPC@Eni: Evolution Path
Pre Petascale Era
Homogeneous Architectures
Industry Standard Components
Linux OS
Pre Petascale Era
Homogeneous Architectures
Industry Standard Components
Linux OS
2000‐2012
Petascale Era
Hybrid Architectures
Industry Standard Components
Linux OS
Petascale Era
Hybrid Architectures
Industry Standard Components
Linux OS
2013‐2019
Pre Exascale Era
Hybrid Architectures
Linux OS
Pre Exascale Era
Hybrid Architectures
Linux OS
2020‐2021
4

HPC@Eni: The hybrid model
Read
Write
Compute
General purpose processor
5

HPC@Eni: The hybrid model
Offload
Read
Write
Compute
General purpose processor Special coprocessor
6

HPC@Eni: hpc4 in numbers
8
‐ 1,595 nodes in 76 48U racks:
• 2x Intel Xeon 8160 2.1GHz (48 cores/node)
• 12x 16GB DIMM DDR4 @ 2,666 MHz
• 2x 480GB SATA G3 SSD
• 2 GPU Nvidia Tesla P100 16GB HBM
‐ Network:
• Infiniband EDR (100 Gbps) 2:1
• Ethernet 1/10 Gbps 4:1
• Management 1 GE
‐ Performance:
• Peak SP: 37.24 PFlop/s
• Peak DP: 18.62 PFlop/s
• HPL: 12.21 PFlop/s
‐ Power consumption: 1.720 kW

HPC@Eni: hpc4 HPL
9
T/V                              N         NB        P           Q                       Time                                           Gflops
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐
WR03L2L2     5947392     1536      71         44                11486.89                                   1.221e+07
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐
||Ax‐b||_oo/(eps*(||A||_oo*||x||_oo+||b||_oo)*N)=        0.0000777 ...... PASSED
Start 2018‐02‐16 07:37:54.974
Stop 2018‐02‐16 10:49:21.862

HPC@Eni: TOP500’s first 10 ranks (November 2017) www.top500.org/lists/2017/11/
hpc4 253,600 12,210 18,621 1,320
10

HPC@Eni: Environmental Footprint of hpc4
www.epa.gov/energy/greenhouse‐gas‐equivalencies‐calculator March 21, 2018
10,000 tons/year
CO2 emission savings
Hybrid Architectures 10.8 PFlops/MW
Non Hybrid Architectures 3.7 PFlops/MW
Energy savings 13,000 MWh/year
11

12
HPC@Eni: Green Data Center
6 computer rooms:
‐ 4 x 800 sqm
‐ 2 x 1,000 sqm
5,200 sqm overall
The facility is designed to feed IT systems for
up to 30 MW of usable power.

13
HPC@Eni: Green Data Center – Free Cooling

14
HPC@Eni: Green Data Center – Operating Efficiency
IT load
83%
Chiller
5%
Fun & Pump
6%
Distribution /
UPS / Trafo
losses
5% Lighting
1%
PUE 1,2

This document discusses using functional programming languages for embedded systems and low-level code. It mentions that Haskell can be used to write NetBSD kernel drivers, ATS2 can write code for 8-bit systems like an Arduino, and the goal of the Metasepi project is to re-write Linux kernel drivers in ATS2 to benefit from dependent and linear types. The document also notes that people at PPL 2015 wearing a specific T-shirt can explain how to install the ATS2 language.

Qiang Yu Resume

Qiang Yu

This document is a resume for Qiang Yu, an embedded/software system architect and consultant based in Bristol, UK. It outlines his objective of seeking consulting opportunities utilizing his experience in product development, architecture design, and problem-solving skills. It then details his work experience, including developing low power wireless applications, network stacks, safety critical systems, firmware, software architectures, data recording systems, and more for clients like Atkins, Expro, and Schlumberger as an independent consultant. It also outlines his experience as a lead software engineer and senior software engineer for companies in access control and rail systems. His skills, education, and qualifications are also summarized.

OpenACC Monthly Highlights: July and August 2018

NVIDIA

HPC Top 5 Stories: August 17th, 2018

NVIDIA

This document highlights the top 5 stories in high performance computing and artificial intelligence from the past week. The stories include: 1) the announcement of the new CUDA Toolkit 10, 2) Docker compatibility with Singularity containers for HPC, 3) the benefits of combining HPC and AI, 4) how containers can be easily created for HPC applications using HPC Container Maker, and 5) how NVIDIA powers the Dell EMC AI stack with ubiquitous GPU acceleration.

Building a Remote Control Robot with Automotive Grade Linux

Leon Anavi

Automotive Grade Linux (AGL) is a leading embedded Linux distribution for the automotive industry and soon it will debut on the 2018 Toyota Camry. Out of the box AGL offers reliable open source solutions for graphics, connectivity, security and software over the air updates. Could other industries benefit from these features? In a quest to discover if AGL is suitable for Internet of Things (IoT) outside the automotive industry, this presentation will reveal a practical experiment of using AGL in robotics. Attendees will learn the exact steps for building a do it yourself (DIY) robot based on Raspberry Pi 3 with off-the-shelf components. The talk will provide guidelines for integration of additional software, sensors and other peripheral hardware device in a headless AGL profile.

Qiang Yu

This document provides a summary of Qiang Yu's experience as an Embedded/Software System Architect and Consultant based in Bristol, UK. It includes details of his role and responsibilities in previous positions at Eastinco Ltd, Gunnebo Entrance Control, and Knorr-Bremse Rail Systems (UK) Ltd where he developed firmware, software, and embedded systems using languages like C, C++, and C# on platforms such as ARM, PIC, and multicore systems. It also lists his skills in areas like architecture, systems, languages, tools, and technologies.

OpenACC Monthly Highlights June 2017

NVIDIA

The document discusses the European approach for achieving exascale performance in high performance computing. It notes the trend towards specialization through the use of accelerators rather than CPU-only systems. The European approach involves developing the RHEA processor, which acts as a common platform to unify different accelerators and handle complex workflows. This approach aims to provide a common open platform and ecosystem based on ARM architecture to cover applications from IoT to supercomputing.

Orchestrate Your AI Workload with Cisco Hyperflex, Powered by NVIDIA GPUs

Renee Yao

Deep learning, a collection of statistical machine learning techniques, is transforming every digital business. As data grows, businesses need to find new ways of capitalizing on the volume of information to drive their competitive advantage. GPUs are becoming mainstream in the datacenter for accelerating containerized AI workloads. Kubernetes is a popular management framework for orchestrating containers at scale. However, managing GPUs in Kubernetes is still nascent, and setting up a Kubernetes cluster with GPUs can be challenging for customers. Join this session to learn more about how to use Kubernetes to orchestrate your AI workloads on Cisco Hyperflex, powered by NVIDIA V100, world’s most powerful GPU.

ERTS 2008 - Using Linux for industrial projects

End-to-End Big Data AI with Analytics Zoo

Jason Dai

The document discusses Analytics Zoo, an open-source software platform for building end-to-end big data AI applications. It provides distributed deep learning frameworks like TensorFlow and PyTorch on Apache Spark. Analytics Zoo allows seamless scaling of AI models from laptop to distributed big data and includes features like automated machine learning, time series forecasting, and serving models in production. It aims to simplify development of end-to-end big data AI solutions.

CINECA for HCP and e-infrastructures infrastructures

Cineca

Sanzio Bassini. Head of the HPC Department of Cineca. Cineca is the technological partner of the Ministry of Education, and takes part in the Italian commitment for the development of e-infrastrcuture in Italy and in Europe for HCP and HCP technologies; scientific data repository and management, cloud computing for industries and Public administration, for the development of computing intensive and data intensive methods for science and engineering Cineca offers a unique offer for: open access of integrated tier0 and tier1 HCP national infrastructure; of education and training activities under the umbrella of PRACE Training advanced center action; integrated help desk and scale up process for HCP users support

Exascale Update from Hyperion Research

In this deck from the HPC User Forum in Detroit, Bob Sorensen from Hyperion Research presents: Exascale Update. As a research firm, Hyperion is tracking the development of Exascale supercomputers worldwide. "The four geographies actively developing Exascale machines are: USA, China, Europe, and Japan. While it is important to emphasize that this is not a race, the first machine to achieve Exascale in terms of sustained LINPACK should be the A21 Aurora system at Argonne in 2021. It will be followed soon after by machines from all the other active projects." Watch the video: https://wp.me/p3RLHQ-j1U Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

ERTS 2008 - Using Linux for industrial projects

BXI: Bull eXascale Interconnect

Edge AI and Vision Alliance

In this deck, Jean-Pierre Panziera from Atos presents: BXI - Bull eXascale Interconnect. "Exascale entails an explosion of performance, of the number of nodes/cores, of data volume and data movement. At such a scale, optimizing the network that is the backbone of the system becomes a major contributor to global performance. The interconnect is going to be a key enabling technology for exascale systems. This is why one of the cornerstones of Bull’s exascale program is the development of our own new-generation interconnect. The Bull eXascale Interconnect or BXI introduces a paradigm shift in terms of performance, scalability, efficiency, reliability and quality of service for extreme workloads." Watch the video: http://wp.me/p3RLHQ-gJa Learn more: https://bull.com/bull-exascale-interconnect/ Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

Software based video, audio, web conferencing - can standard servers deliver?

Anders Løkke

"APIs for Accelerating Vision and Inferencing: Options and Trade-offs," a Pre...

For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-trevett For more information about embedded vision, please visit: http://www.embedded-vision.com Neil Trevett, President of the Khronos Group and Vice President at NVIDIA, presents the "APIs for Accelerating Vision and Inferencing: Options and Trade-offs" tutorial at the May 2018 Embedded Vision Summit. The landscape of SDKs, APIs and file formats for accelerating inferencing and vision applications continues to rapidly evolve. Low-level compute APIs, such as OpenCL, Vulkan and CUDA are being used to accelerate inferencing engines such as OpenVX, CoreML, NNAPI and TensorRT. Inferencing engines are being fed via neural network file formats such as NNEF and ONNX. Some of these APIs, like OpenCV, are vision-specific, while others, like OpenCL, are general-purpose. Some engines, like CoreML and TensorRT, are supplier-specific, while others, such as OpenVX, are open standards that any supplier can adopt. Which ones should you use for your project? In this presentation, Trevett presents the current landscape of APIs, file formats and SDKs for inferencing and vision acceleration, explaining where each one fits in the development flow. Trevett also highlights where these APIs overlap and where they complement each other, and previews some of the latest developments in these APIs.

Civil Infrastructure Platform: Industrial Grade SLTS Kernel and Base-layer De...

Yoshitake Kobayashi

The Civil Infrastructure Platform (CIP) is creating a super long-term supported (SLTS) open source "base layer" for industrial grade software. We have been working on security fixes and some backported features since the moment we decided that Linux kernel v4.4 would be the first SLTS version. In this talk, we will describe the current development status of the SLTS kernel and testing environment. First, we'll explain our kernel development policy. Then, we'll describe the functionality that has been backported. Second, we'll talk about testing before using our base-layer on real products. We have been developing a test framework to collect and share test results. To build it, we don't want to duplicate existing work such as KernelCI, Fuego and others. For that reason, we are trying to collaborate and contribute to such projects.

Fundamentals of Ethernet /IP Technology

softconsystem

This document provides an overview of the agenda and topics for a Rockwell Automation TechED presentation on EtherNet/IP networking technology. The presentation will cover standard industrial network technology including EtherNet/IP, the OSI reference model, and industrial automation network architectures. It will also discuss topics like converged plantwide Ethernet, EtherNet/IP capabilities, industrial network trends, and the physical layer of networking.

Presentazione IBM Power System Evento Venaria 14 ottobre

PRAGMA PROGETTI

The document discusses IBM's POWER8 processor and Linux on Power platform. It provides an overview of the OpenPOWER Consortium which aims to drive innovation through an open development model. Key highlights of POWER8 include 12 cores per socket, improved caches and memory bandwidth. Linux is highlighted as a growing enterprise workload with over 90% of supercomputers using it. Linux on Power is positioned as a strategic platform for new workloads like big data and analytics by combining Linux with the performance of POWER8.

AI Bridging Cloud Infrastructure (ABCI) and its communication performance

South Tyrol Free Software Conference

In this deck from the MVAPICH User Group, Shinichiro Takizawa from AIST presents: AI Bridging Cloud Infrastructure (ABCI) and its communication performance. "AI Bridging Cloud Infrastructure (ABCI) is the world's first large-scale Open AI Computing Infrastructure, constructed and operated by National Institute of Advanced Industrial Science and Technology (AIST), Japan. It delivers 19.9 petaflops of HPL performance and world' fastest training time of 1.17 minutes in ResNet-50 training on ImageNet datasets as of July 2019. ABCI consists of 1,088 compute nodes each of which equipped with two Intel Xeon Gold Scalable Processors, four NVIDIA Tesla V100 GPUs, two InfiniBand EDR HCAs and an NVMe SSD. ABCI offers a sophisticated high performance AI development environment realized by CUDA, Linux containers, on-demand parallel filesystem, MPI, including MVAPICH, etc. In this talk, we focus on ABCI’s network architecture and communication libraries available on ABCI and shows their performance and recent research achievements." Watch the video: https://wp.me/p3RLHQ-kLz Learn more: https://abci.ai/ and http://mug.mvapich.cse.ohio-state.edu/program/ Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

Fundamentals of EtherNet/IP Network Technology

Rockwell Automation

Hpc in the cloud meetup 19 march 2019

Abhishek Gupta

HPC in the cloud provides benefits like faster time to market, global accessibility, and unlocking new capabilities through access to unlimited hardware. However, there are also challenges to address like one size not fitting all needs, the need for expertise in HPC, cloud, and software, and software licensing issues. HPC in the cloud requires more than just remote servers - it involves ensuring a cloud-ready stack, performance management, security and compliance, change management, remote access, appropriate CAE applications, data management, and the right HPC stack. Case studies show how HPC in the cloud can enable breakthroughs like 27x faster living heart simulations and winning innovation awards.

IBM: The Linux Ecosystem

Kangaroot

SFScon21 - Ettore Chimenti - Hardware Roadmap the next frontier of a vending ...

An overview on the ease of development of a very complex system Have you ever stood infront of a Vending machine and wished it could be smarter, remember that you like one sugar with your coffee, that is easier to interact with, easier to pay? In this talk SECO lays the out our Oniro Blueprint, to build a vending machine for the truly digital age, one that knows and remembers your preferences, one that is easy to interact with even if you have no wallet or coins and one that is truly easy for businesses to manage and maintain while boosting the machine use and revenue. Finally will be announced the future roadmap for the new and existing services and reference boards from SECO, including the data orchestration platform Astarte.

Computing Platforms for the XXIc - DSD/SEAA Keynote

Ian Phillips

Wikipedia defines Platform as "A raised level surface on which people or things can stand". A more familiar technical interpretation applies to the hardware and OS configuration applicable to the execution of software; most frequently applicable to highly stable PC or Mainframe architectures. But the world has changed a lot since serious computing power moved into the embedded consumer arena. Now, with runs of many millions for single products, the argument for customisation is much more justifiable; so the traditional view of platforms is struggling against a tide of individuality. Can the ARM architecture bring stability back into this chaos, or is something else needed? Isaac Newton realised the reality of platforms when he talked of standing on the shoulders of giants. A platform is a stable place where engineers and scientists can stand to achieve more than they would otherwise have done. So our XXI Century Platforms are the shape to deliver improved Productivity, Reuse, Quality, TTM, Cost, etc. for the System Products we are now charged to deliver. Its business, stupid!

Journal Seminar: Is Singularity-based Container Technology Ready for Running ...

Kento Aoyama

1. The study evaluates the performance of running MPI applications in Singularity containers on HPC clouds and local clusters. 2. Experiments were conducted on Chameleon Cloud using Intel Xeon Haswell nodes and a local cluster using Intel Knights Landing nodes. 3. Results show that Singularity incurs less than 8% overhead for MPI point-to-point and collective communications and for HPC applications, demonstrating its potential for efficiently running MPI workloads on HPC clouds and systems.

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