Improve SI/PI with Ansys SIwave

Dates:

Tuesday, 6 October 2020, at 09.00 – 10.00 am (TH/ID Time zone)
Tuesday, 6 October 2020, at 10.00 – 11.00 am (SG/MY Time zone)

 

To Register,

Go to https://app.mlsend2.com/k0t2p1/

Harnessing the Power of Supercomputers to Advance Marine, Offshore and Renewables

To Register:

About this Event

Continual development in high fidelity modeling and simulation allow scientists and engineers to derive deep insights from the detailed flow field information. This enables better evaluation and prediction of the performance, and devises optimized design for marine vessels and offshore platforms. Augmented by the power of supercomputer, marine and offshore industry is now more receptive towards the idealization of digital twins and in near future, cyber-physical integration.

In this webinar, a few examples on how high fidelity simulation with high performance computing (HPC) are employed for marine, offshore, and renewables sectors. This includes, but not limited to the following:

  • An Intelligent Hull Operation, Processing, & Evaluation (I-HOPE) platform developed by IHPC for high efficient vessel resistance prediction;
  • Design and optimization of marine and offshore structures;
  • Application focus on innovation in offshore renewable energy sector, such as tidal turbine and floating offshore wind turbine performance evaluation, and wind farm layout optimization;
  • Digital twin for ocean basin and towing tank, and reduce order modelling for future preparation of cyber-physical integration

In the examples, highlight will be on the HPC application in the high fidelity investigations, the development of digital twins, as well as the new and innovative modeling and simulation technologies. The talk will conclude with key benefits HPC empowered high fidelity simulation will bring to marine, offshore and renewables sector.

 

Date: 15 October 2020

Time: 10:00am – 11:00am (Singapore Time / UTC+8)

The Zoom Room will be open 10 minutes before the webinar commences. Limited spaces available!

 

Programme:

10:00am – 10:05am: Introduction by NSCC

10:05am – 10:45am: Presentation on Harnessing the Power of Supercomputers to Advance Marine, Offshore and Renewables by Dr. Xing Xiuqing

10:45am – 10:55am: Q&A session

10:55am – 11:00am: Closing by NSCC

 

Speaker’s Profile:

Dr. Xing Xiuqing, Senior Scientist, Institute of High Performance Computing (IHPC), A*STAR, Singapore

Dr. Xing Xiuqing is a Senior Scientist at A*STAR’s Institute of High Performance Computing. She is experienced in computational fluid dynamics (CFD), optimization algorithms, hydrodynamics, aerodynamics, thermodynamics, fluid structure interaction, multiphase flow, and underwater noise. Her research projects include Digital Twinning of Tropical Marine Floating Lab, CFD Analysis on Ballast Water Sedimentation, LNG Boil-off Rate Determination and Management, Fully Automated Geometry Modification and Smart Design, Optimization Process for Ship Hull Form, Enhancing Offshore System Productivity, Integrity and Survivability in Extreme Environments.

Encountering Error when pip install TensorToolbox

I’m using Python-3.8.7  When I do a pip install for TensorFlowbox with Intel Optimized Toolbox, I received errors.

% pip install TensorFlowbox

But it failed with its SpectralToolbox Dependencies.

.....
.....

Building wheels for collected packages: SpectralToolbox, orthpol-light
Building wheel for SpectralToolbox (setup.py) ... error
ERROR: Command errored out with exit status 1:
command: /usr/local/python/intel/2017u3/intelpython3/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-p5qjlor0/spectraltoolbox/setup.py'"'"'; __file__='"'"'/tmp/pip-install-p5qjlor0/spectraltoolbox/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d /tmp/pip-wheel-4k6ars4d
cwd: /tmp/pip-install-p5qjlor0/spectraltoolbox/
Complete output (56 lines):

Somehow the later version of Python3 has issues with SpectralToolbox and TensorToolbox. To compile TensorToolbox, you have to go back to earlier version of Python 3. I chose Python-3.6.9 (https://www.python.org/downloads/release/python-369/).

And it works.

Perquisites:

openmpi-3.1.4
gnu-6.5
m4-1.4.18
gmp-6.1.0
mpfr-3.1.4
mpc-1.0.3
isl-0.18
gsl-2.1

 

Compile

% tar -zxvf Python-3.6.9
% cd Python-3.6.9
% ./configure --prefix=/usr/local/python-3.6.9 --enable-optimizations
% make -j 16
% make install

Installing TensorToolbox

% pip install numpy scipy matplotlib
% pip install mpi4py
% pip install TensorToolbox

For more information, see TensorToolbox-1.0.22 (https://pypi.org/project/TensorToolbox/#description)

 

Fixing can’t load screen14 issues for ANSYS 2020-R1

I was having this strange issue when running ANSYS ICEM

"Fixing can't load screen14 issues, using variable
Signal 11 caught!
Segmentation violation - exiting after doing an emergency save"

The Issue can easily resolved. It is due to errors due to missing fonts libraries

# yum install xorg-x11-fonts-*
Loaded plugins: fastestmirror, langpacks
Loading mirror speeds from cached hostfile
* base: mirror.newmediaexpress.com
* epel: download.nus.edu.sg
* extras: mirror.newmediaexpress.com
* updates: mirror.newmediaexpress.com
Package xorg-x11-fonts-ISO8859-1-75dpi-7.5-9.el7.noarch already installed and latest version
Package xorg-x11-fonts-Type1-7.5-9.el7.noarch already installed and latest version
Resolving Dependencies
--> Running transaction check
---> Package xorg-x11-fonts-100dpi.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-75dpi.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-ISO8859-1-100dpi.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-ISO8859-14-100dpi.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-ISO8859-14-75dpi.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-ISO8859-15-100dpi.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-ISO8859-15-75dpi.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-ISO8859-2-100dpi.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-ISO8859-2-75dpi.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-ISO8859-9-100dpi.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-ISO8859-9-75dpi.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-cyrillic.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-ethiopic.noarch 0:7.5-9.el7 will be installed
---> Package xorg-x11-fonts-misc.noarch 0:7.5-9.el7 will be installed
--> Finished Dependency Resolution

The Issue should be resolved.

MemVerge GA Launch – Breakthrough in Big Memory

Presentations from Intel, Penguin Computing, Banco Intesa Sanpaolo and industry Visual Effects expert

Speaker Timestamps:
(3:32 min) – Intel, VP/GM – Alper Ilkbahar
(9:40 min) – MemVerge, CEO – Dr. Charles Fan
(30:37 min) – Penguin Computing, VP – Dr. Kevin Tubbs
(45:30 min) – Visual Effects Expect – Dr. Hank Driskill
(54:40 min) – Banco Intesa Sanpaolo, Head of Cloud – Nicola Carotti

Using xsos to summarise System Information

Introduction

The goal of xsos is to make it easy to instantaneously gather information about a system together in an easy-to-read-summary, whether that system is the localhost on which xsos is being run or a system for which you have an unpacked sosreport.

xsos will attempt to make it easy, parsing and calculating and formatting data from dozens of files (and commands) to give you a detailed overview about a system.

 

Installation

Manual Install

% git clone https://github.com/ryran/xsos.git
Cloning into 'xsos'...
remote: Enumerating objects: 6, done.
remote: Counting objects: 100% (6/6), done.
remote: Compressing objects: 100% (5/5), done.
remote: Total 946 (delta 1), reused 5 (delta 1), pack-reused 940
Receiving objects: 100% (946/946), 907.12 KiB | 728.00 KiB/s, done.
Resolving deltas: 100% (450/450), done.

Point 1: Get information on OS and Memory

Point 2: Get information on Network, Network Adapters and CPU

Point 3: Get Information on BIOS

Point 4: Get Information on sysctl

Point 5: Get information on kdump

References:

    1. xsos — a tool for sysadmins and support techs
    2. xsos Project Space

Altair acquires Univa and Ellexus

Altair, (Nasdaq: ALTR) a global technology company providing solutions in data analytics, product development, and high-performance computing (HPC), today announced the acquisition of Univa, a leading innovator in enterprise-grade workload management, scheduling, and optimization solutions for HPC and artificial intelligence (AI) on-premises and in the cloud.

For more information, see Altair Acquires Univa

How to increase the number of threads created by the NFS daemon for CENTOS 7

Taken from How to increase the number of threads created by the NFS daemon in RHEL 4, 5, 6 and 7?

In case of a NFS server with a high load, it may be advisable to increase the number of the threads created during the nfsd server start up.

Edit the following line in /etc/nfs.conf

% vim /etc/nfs.conf
#[nfsd]
# debug=0
threads=64
# host=
# port=0
# grace-time=90
# lease-time=90
# udp=y
# tcp=y

Testing whether it works….

% cat /proc/net/rpc/nfsd

According to the RH, “The first number is the total number of NFS server threads started. The second number indicates whether at any time all of the threads were running at once. The remaining numbers are a thread count time histogram.”

th 64 0 2.610 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Find CPU and GPU Performance Headroom using Roofline Analysis

Join Technical Consulting Engineer and HPC programming expert Cedric Andreolli for a session covering:

  • How to perform GPU headroom and GPU caches locality analysis using Advisor Roofline extensions for oneAPI and OpenMP
  • An introduction to a new memory-level Roofline feature that helps pinpoint which specific memory level (L1, L2, L3, or DRAM) is causing the bottleneck
  • A walkthrough of Intel Advisor’s improved user interface

To see video, see https://techdecoded.intel.io/essentials/find-cpu-gpu-performance-headroom-using-roofline-analysis/#gs.fpbz93

NVIDIA to Acquire Arm for $40 Billion, Creating World’s Premier Computing Company for the Age of AI

NVIDIA and SoftBank Group Corp. (SBG) today announced a definitive agreement under which NVIDIA will acquire Arm Limited from SBG and the SoftBank Vision Fund (together, “SoftBank”) in a transaction valued at $40 billion. The transaction is expected to be immediately accretive to NVIDIA’s non-GAAP gross margin and non-GAAP earnings per share.

The combination brings together NVIDIA’s leading AI computing platform with Arm’s vast ecosystem to create the premier computing company for the age of artificial intelligence, accelerating innovation while expanding into large, high-growth markets. SoftBank will remain committed to Arm’s long-term success through its ownership stake in NVIDIA, expected to be under 10 percent.

For more information, see NVIDIA to Acquire Arm for $40 Billion, Creating World’s Premier Computing Company for the Age of AI