Gab2017 science-lab

Apr 21, 2017Download as PPTX, PDF2 likes527 views

This document introduces the Global Azure Bootcamp 2017 Science Lab which uses distributed computing on Azure to run the Seliga algorithm in order to better understand star formation history by limiting the effects of uncertainties in observations and models. Participants will deploy packages running the Seliga algorithm on Azure Batch to contribute to astrophysics research and can compete on global dashboards to see scores. The science lab architecture maximizes Azure resource use by running the Seliga algorithm on an Azure Batch process that users can deploy and scale as desired.

In previous years we have tried to bring you a science lab that is both interesting and represents great
research. This year is no exception, this year we bring you the stars themselves!
The Science Lab takes a look at Star Formation History (SFH) to see how many stars of a specific
metallicity are formed in a galaxy during its lifetime. Traditional approaches are affected by uncertainties
associated with observations or the limited accuracy of models when running computer code, making it
difficult to accurate calculate the age of stars within a galaxy.
To overcome this, we need to correct for these effects to obtain the “true” SFH. The objective of the
Seliga (SEcret LIfe of GAlaxies) algorithm, developed by Sebastian L. Hidalgo from the Instituto de
Astrofísica de Canarias (IAC), is to limit the impact of all these effects so we can compare the predictions
of the models more directly with the observations. This task needs a huge number of tests that can be
performed successfully only by using distributed computing, like the one you will deploy in the Global
Azure Bootcamp Science Lab.
By taking part in the Science Lab, you will be helping to contribute to the body of knowledge in this
important field that allows researchers to understand the very beginnings of the universe itself. We hope
you choose to deploy the packages that will run the Seliga algorithm, and deliver real results to the
researchers.
The Secret Life of Galaxies

To show the power of Azure in the
Global Azure Bootcamp Science Lab
this year we have created a solution
that maximises the use of Azure
resources whilst providing the
research team with large volumes of
computing power.
The Seliga algorithm runs in an
Azure Batch process that YOU
deploy and scale as you want.
For extra fun, there is a dashboard
showing global scores so you can
compete against your friends or
other people around the world.
Batch Server API
User Database Inputs/Outpus
Event Hubs
US
Batch Server API
User Database Inputs/Outpus
Event Hubs
Europe
Batch Server API
User Database Inputs/Outpus
Event Hubs
Asia
BatchAzure
Automation
GetNewBatchJob
Job 1
Job N
Task 1
Task M
Task 1
Task M
OutputsTraffic Manager
Global
Dashboards
Stream Analytics
Global Dashboards
User deployment
Seliga
Application
Insights
Azure cache
Inputs
DocDB
Science Lab Architecture

About
http://bit.ly/gablab2017about
Instructions
http://bit.ly/gablab2017run
Lab Key: YJK-DGW-IDC
Dashboard
http://gablab2017.azurewebsites.net
Global Azure Bootcamp 2017 Science Lab

Step 3
Use Existing Resource Group
Email, FullName, TeamName,
CompanyName: fill with your
personal info. on the global
dashboards (e-mail will
Team Name = ‘NJ-AZURE’
Country Code = ‘US’
Lab Key Code = ‘YJK-DGW-IDC’

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Gab2017 science-lab

1. Welcome to the Iselin, New Jersey

2. Global Azure Bootcamp 2017 Science Lab

3. In previous years we have tried to bring you a science lab that is both interesting and represents great research. This year is no exception, this year we bring you the stars themselves! The Science Lab takes a look at Star Formation History (SFH) to see how many stars of a specific metallicity are formed in a galaxy during its lifetime. Traditional approaches are affected by uncertainties associated with observations or the limited accuracy of models when running computer code, making it difficult to accurate calculate the age of stars within a galaxy. To overcome this, we need to correct for these effects to obtain the “true” SFH. The objective of the Seliga (SEcret LIfe of GAlaxies) algorithm, developed by Sebastian L. Hidalgo from the Instituto de Astrofísica de Canarias (IAC), is to limit the impact of all these effects so we can compare the predictions of the models more directly with the observations. This task needs a huge number of tests that can be performed successfully only by using distributed computing, like the one you will deploy in the Global Azure Bootcamp Science Lab. By taking part in the Science Lab, you will be helping to contribute to the body of knowledge in this important field that allows researchers to understand the very beginnings of the universe itself. We hope you choose to deploy the packages that will run the Seliga algorithm, and deliver real results to the researchers. The Secret Life of Galaxies

4. To show the power of Azure in the Global Azure Bootcamp Science Lab this year we have created a solution that maximises the use of Azure resources whilst providing the research team with large volumes of computing power. The Seliga algorithm runs in an Azure Batch process that YOU deploy and scale as you want. For extra fun, there is a dashboard showing global scores so you can compete against your friends or other people around the world. Batch Server API User Database Inputs/Outpus Event Hubs US Batch Server API User Database Inputs/Outpus Event Hubs Europe Batch Server API User Database Inputs/Outpus Event Hubs Asia BatchAzure Automation GetNewBatchJob Job 1 Job N Task 1 Task M Task 1 Task M OutputsTraffic Manager Global Dashboards Stream Analytics Global Dashboards User deployment Seliga Application Insights Azure cache Inputs DocDB Science Lab Architecture

5. About http://bit.ly/gablab2017about Instructions http://bit.ly/gablab2017run Lab Key: YJK-DGW-IDC Dashboard http://gablab2017.azurewebsites.net Global Azure Bootcamp 2017 Science Lab

6. Step 3 Use Existing Resource Group Email, FullName, TeamName, CompanyName: fill with your personal info. on the global dashboards (e-mail will Team Name = ‘NJ-AZURE’ Country Code = ‘US’ Lab Key Code = ‘YJK-DGW-IDC’

Gab2017 science-lab

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