Ctsolakis at 13:05, 22 October 2019

2019-10-22T13:05:29Z

← Older revision		Revision as of 08:05, 22 October 2019
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			= Presentation Files =
			https://odu.box.com/s/6az46pa7zr9jhf1s05w6gqrs8lm4u5o5
	= Cara Leckey =		= Cara Leckey =

Ctsolakis: /* Cara Leckey */

2019-10-21T20:24:25Z

Cara Leckey

← Older revision		Revision as of 15:24, 21 October 2019
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	* Software at NASA has long lifespan, often is 20+ years		* Software at NASA has long lifespan, often is 20+ years
	* HPCI focuses on building the skills for effient and maintinable software		* HPCI focuses on building the skills for effient and maintinable software
	* Computatubal nodestrictiove evaluation, : simulations for defetc detection ** aircraaft and material focuded *ultrasound simulations		* Computatubal nodestrictiove evaluation, : simulations for defetc detection ''' aircraaft and material focuded ''' ultrasound simulations
	* EDL : entry descent and landing project for mars and other planets		* EDL : entry descent and landing project for mars and other planets
	* MLMCPy replacing putre model carlo gives them about 10x improvement		* MLMCPy replacing putre model carlo gives them about 10x improvement

	= Amber=		= Amber=

Ctsolakis: Created page with "= Cara Leckey = * High performance incubator program (HPCI) * Nasa has projects involving both earth sciences and sun-/other-planet-based sciences * check provided video, con..."

2019-10-21T20:23:53Z

Created page with "= Cara Leckey = * High performance incubator program (HPCI) * Nasa has projects involving both earth sciences and sun-/other-planet-based sciences * check provided video, con..."

New page

= Cara Leckey =

* High performance incubator program (HPCI)
* Nasa has projects involving both earth sciences and sun-/other-planet-based sciences
* check provided video, contains reserach at all levels, like design- implementaion and validation
* Exploring science from Moon to Mars
* Software at NASA has long lifespan, often is 20+ years
* HPCI focuses on building the skills for effient and maintinable software
* Computatubal nodestrictiove evaluation, : simulations for defetc detection ** aircraaft and material focuded *ultrasound simulations
* EDL : entry descent and landing project for mars and other planets
* MLMCPy replacing putre model carlo gives them about 10x improvement

= Amber=
* Buikd arround CEBAF ( Continiuoud electron beam accelator)
* unique in terms of functionality ( uses electrons for probing)
* Operational since 1995, 1630 active users !
* 1/3 of students in nuclear physics come through JLAB
* spinoff techonologies on medical field ** imaging ** cryogenics
* shipy cluster (2016 was on top 500) ** 17k cores 12,5k cores '''mainly for QCD (quantum chromodynamics) ''' not analytically solvable need numberical solution !
* Jlab needs not only offsite but also onsite powerfull computing facilities to handle strewming data
* use simualted data sources to complement experimental like NASA does!

= David =
* chromo is chromodynamics has to do with the “colours” for the quarks
* you nwcwr see quarks alone, the force between in them is extrimly high
* QED/QCD
* Big question is how are quarks and gluons distributed inside the nucleon
* Jlab new developement ny times how big is the proton
* Electron ion has the difference that we do not collide an electonr with a stiic target but with aa proton beam allowing thus higher speeds
* Like in medical imaging we try to rebuild image fromsparce and incomplete data
* expand to other areas , like visualizarion for understading , verification and debugging
* essential we are solving an inverse problem like in the black hole imaging case

= Eric =
* about 10 main CFD codes, that was the speakers background
* CFD vision 2030 document ** advocational document ** practices for nationalwide CFD analyses ** used everywhere where funding matters ** PI was Boeing
* turbulent flows and separation is the main focus
* cruse speed, drag , lift easy already
* separaion and turbulent is the different, landing and takeoff for example
* or how Boeing can be still competitive
* High lift is the most difficult , close to 2 billion for certification of a new boeing aricraft
* ie during landing , landng gear and flaps open
* Big impact
* more than 10k simulation before anything is cut at Boeing
* Capacity job was the norm in order to build databases of small scalae solution
* But capability is need in order to advance
* RANS no enough swithcinh to hybrid RANS/LES
* How to do mesh adaptation on 1000 cores?
* How to hangle geometry ?
* Retropropulsion simulations, ** about two-store vehicle , how to land it on mars ? ** biggest until know was mini-couper size ** landing withing 5 min from landing speed to touchdown
* typical 6 billion element, 200k time spes , 200TB per run
* see number for convetional ** 3 months run on 5000cores 6 months in queue
* now on summit : ** 4 days on 552 GPUS equi to 100000SKL cores ** 30-40 TB per day

= Ed =
* material vision 2040 in the same realm like CFD 2020
* NASA/CR-2018-219771
* wide spectrum of scaling nano-micro to cm
* assertenty verification
* understant micro-structures
* save mass and material without loosing interity structure
* pipeline includes from microstuturess to localized FEA models and contruction integrity

= Pete =
* ATPESC (best tra
* complicating heteronegeious memory layout
* everything is dynamic
* AML (memory libary)
* Argo library *AML ''' on chip memory, easy way to use it as a cache ''' but we can do better than that ''' dive in and write code in RTS to handle it ''' readthedocs page ! argo-aml.readthedocks.ion
* change shape of data structure during transfer through DMA
* futute architectures will be heteregoneous we need to eb able to move data arround
* Ken batcher , supercomputer is a a device for turning compute-bound problesm in to I/O bound problem
* Edge computing is the opposte
* move some of the computtions to edge to reduce data output
* used by hydrology, national secutiry
* new companies comming up with new specialized hardware
* Google edge GPU great but only for one specific thing Tensorflow
* Sage prject , spread accross USA, wild fire prevention
* do machine leanring at the edge to singl if smoke is seen
* never send raw data
* NSF NEON, capture and integrate data @ Miltiple scales
* XLOOP workshop
* How about Japan with many arm based nodes ** extexted arm for vectors ** very power efficent

= Nikolopoulos Dimitris =

* use optimism to improve your algorithms
* how to do now optimistics computing going beyond what computer hardware allows you to do
* How tp=o improve and optimize all the time for all kinds of edge computing
* Can we deliver high-performance cpomputing depedable computing under unsertainly
* TAPAS joined work with IBM. Train-less Neural Network Architecture Exploration tool
* maybe you do not have the time or resouaches to do time consuming training
* What if instead of training we teach how to learn ????
* Learn how networks learn instead of training a network. Find a way to encode them quickly.
* TAPAS goes very quickly over more than 1000 networks/se with low end GPU
* Already deployed in contraines and microservece
* Threes steps ** Data charecterization aginast difficulty (quick) ( how a base probe network corresponds to the dataset ?) ** Build knowledge base and check if anything seen in the past has performed well in these diffuculty ** Search the db.
* Ecnode type and connectivity of nodes as well as the resource requirements
* DEFCON-CPU ** CPUS can run up to 25% faster but finding the right amount of fiddling is diffucult once you go above you have timing issues ** applied on real hardware on ARM processors

# identity workload-specific behavior, timing-error-prone code sequences
# Mitigate those by building a voltage and frequency governorns

* Firsth they found sequncies that cause timing errors and then they identifies these sequncies in real-world problems
* HERMES ** same idea but for memory ** relax hardware limits and find the errors *DRAM fereshes evey few ns to keep data alive, but you also refresh on write so can we minimize refresehes ?
* build on top of linux NUMA module to Reliability data regions
* can save a lof of memory
* summary ** use compiler like optimization back to machine leanrning

Internal:CNF HPC Workshop 2019 - Revision history

Ctsolakis at 13:05, 22 October 2019

Ctsolakis: /* Cara Leckey */

Ctsolakis: Created page with "= Cara Leckey = * High performance incubator program (HPCI) * Nasa has projects involving both earth sciences and sun-/other-planet-based sciences * check provided video, con..."