# Presentation: 882865

## Overview of NERSC Analytics Program

**Overview of NERSC Analytics Program**
**Cecilia Aragon**
**NERSC Analytics Team**
**aragon@hpcrd.lbl.gov**
**NERSC User Group Meeting**
**September 17, 2007**

## NERSC Analytics Team Members

**Wes Bethel, Team Lead**
**Cecilia Aragon**
**Janet Jacobsen**
**Peter Nugent**
**Kurt Stockinger**
**Gunther Weber**
**(~3 FTEs with 1 FTE to be hired)**

- W. Bethel

- C. Aragon

- J. Jacobsen

- P. Nugent

- K. Stockinger

- G. Weber

## What is the Analytics Program at NERSC?

**At NERSC, the Analytics Program is the confluence of several key technologies:**
**Data management **
**Data storage/retrieval/sharing/movement, data indexing/querying, format conversion, sharing.**
**Data analysis, exploration and visualization**
**Feature detection/tracking.**
**Statistical analysis.**
**Subsetting, filtering, partitioning.**
**Comparison: models to models, models to data, etc.**
**Interactive data exploration.**
**Visualization: visual analysis.**
**Workflow management**
**Systematic approach to “data processing pipelines,” especially those that use multiple distributed resources and automate scientific data processing activities.**

## Analytics Components

**Analytics Components**

- Experiment

- Simulation

- Data

- Analysis

- Results

- Filter, feature detection, search, subset, transform, visualize.

- Images, movies, data, decisions, knowledge.

- Raw data files, metadata, location transparency, single project, community repositories.

- Workflow

- High Performance I/O Libraries, Data Models

## What we do for NERSC users

**Mission statement:**
**Facilitate NERSC User knowledge discovery**
**through use, adaptation, extension, creation, application and deployment of a diverse array of technologies spanning the domains of**
**data management**
**data analysis and exploration**
**visualization**
**workflow management**

## What we do for NERSC users

**Generally, no off-the-shelf, general purpose solutions for Analytics exist.**
**The Analytics Program adapts, extends, integrates and sometimes creates technologies to meet user needs.**
**Consulting and collaborative projects with users in: visualization, data management, data exploration, data analysis, workflows.**
**Substantive impact on science comes through in-depth work with stakeholder/users.**
**Contact us at **_**[consult@nersc.](mailto:consult@nersc.gov)**__**[gov](mailto:consult@nersc.gov)**_

## NERSC Analytics Web Site

**http://www.nersc.gov/nusers/analytics/**
**Completely redesigned in March 2007**
**Response to 2006 User Survey (need for more web-based analytics documentation)**

## Resources of the Analytics Program: Personnel, Analytics System

**Team of six**** ****(~3 FTEs with 1 FTE to be hired)**** ****with experience spanning all aspects of analytics, high performance computing, and many science domains.**
**DaVinci: ****SGI Altix**** – 32 processors, 192GB RAM, 40TB attached FC storage**
**Architectural balance favors data intensive operations: large SMP memory, best I/O bandwidth on the floor at NERSC.**
**Procurement process underway**** ****for new analytics machine (response to user predictions of substantial increase in data size over next two years).**

## Supported Science Areas 

**Notes:**
Astrophysics and fusion – each over 20%
Accelerator physics – 12%
Life sciences – 9%
Chemistry, material sciences – each 6%

## Analytics CustomerTechnology Matrix

## Analytics Customers

**Samples of our work:**
**Climate**
**Fusion**
**Spectrum Synthesis**
**Laser Wakefield Particle Acceleration**
**Astrophysics**
**General purpose**

## Analysis of Climate Modeling: Automatic Feature Extractionby Blind Source Separation

*Tropical storm visible in sea level pressure simulations at multiple time steps.*

*Images of extracted features: top ten independent components were extracted from set of all 8x8 subimages.  *

- Extracted features can be used as templates for finding similar features.

- In this case,  the features were variations on rotating
- low-pressure systems.
- This was not assumed
- a priori.

**Notes:**
Usual method of detecting tropical storms in GCM is to look for high wind, low SL pressure, high vorticity, etc.  
What if you don’t know what explicit criteria to use to find features of interest?
The BSS method (also known as blind deconvolution, independent component analysis (ICA)) is a statistical method for detecting unusual signals.
We applied the BSS method to find features in climate models.
“Think of the sub images as PCA-like components of interesting features. In general they are variations on a similar thing, a rotating low-pressure system (as it turns out, though this wasn't known ahead of time).” 

## Fusion: Comparative Analysis

**Science objective: compare experiment (SSPX) and simulation (NIMROD).**
**Problem: data formats are incompatible with each other and with visual and comparative analysis tools.**
**Solution #1: one-step conversion from NIMROD binary output to VisIt format (replaces a procedure consisting of about 10 steps).**
**Solution #2: VisIt reader for SSPX data. Implement basic comparative visual analysis capabilities in VisIt.**

## Spectrum Synthesis

**NERSC Analytics contribution: visualization and analysis to test and confirm theory of new type of Type Ia SN – one having “Super- Chandrasekhar” mass.**
**Top: brightness vs. velocity and amount of deceleration.**
**Middle: velocity vs.mass and unburned carbon using a 1.4 solar mass model.**
**Bottom: moving to a 2 solar mass model includes new observation.**

## PIC Simulation of Laser Wakefield Particle Acceleration

- This image shows a horizontal slice through the electric field; the electrons are colored by the magnitude of the momentum.
*(AVS/Express)*

- This image uses volume rendering to show the plasma density field.
*(VisIt)*

## Accretion-Induced Collapse of White Dwarfs

**Data from 2D radiation-hydrodynamics simulations.**
**Data include 68 scalar and 25 vector fields.**
**Future simulations will be 3D.**

* ***Entropy**

* ***Electron fraction**

* ***Mach number**

## Analytics with Cooperative Funding – SNfactory

**New supernova data analysis and workflow visualization tools (Sunfall and SNwarehouse) have improved usability and situational awareness, and enabled faster and easier access to data for supernova scientists worldwide**
**Advanced image processing (Fourier contour analysis) and machine learning techniques running on NERSC platforms have achieved a ~90% decrease in human workload in nightly supernova search (2.75 FTE)**

**Notes:**
Sunfall  (first figure): "Supernova Factory Assembly Line" software framework has unified multiple pieces of legacy code, developed novel methods ("data forklift") of enabling interoperability between SNfactory servers, collaboration equipment in France, telescopes in Hawaii, and NERSC platforms (PDSF and HPSS).
SNwarehouse (second figure): supernova data analysis and visualization tool has improved usability, enabled faster and easier access to data for supernova scientists worldwide, and improved situational awareness.
Supernova Search: The third figure (with the 6 subimages) shows in the upper row, far right, an example of a supernova subtraction.  Lower row, far right, shows a bad subtraction (C-shaped image).  Hundreds of thousands of such subimages are processed nightly on NERSC platforms.  Novel image processing and machine learning techniques distinguish between good supernova candidates and artifacts produced by bad subtractions, CCD errors, and other noisy data. Search code computes ~20 photometric and geometric features from each subimage, and decides whether or not to follow up the candidate with spectroscopic measurements, but the need for human experts to make the final decision slows down the daily scheduling of follow-up observations.  We have added 2 new geometric features computed with a novel, fast image processing algorithm that captures the shape of the candidate object; these features are able to reduce the number of false candidates sent to human scanners by 38%. We have also added a machine learning technique to automatically classify each candidate image by treating its measured features as a point in a high-dimensional space. The plot (fourth image) illustrates how good supernova candidates (blue points) and false supernova candidates (red points) may be separated.
The final image in the lower right corner is a Hubble space telescope image of a supernova appearing in a nearby galaxy.

## Scientific Data Management

**Storage Resource Manager (****SRM****) for distributed data management:**
**integrated mechanism for transferring files from one location to another,**
**uniform access to heterogeneous storage (disk, tape),**
**fault tolerant.**
**FastBit**** for efficient indexing and querying.**
**HDF5 FastQuery**** combines bitmap indices with HDF5.**

## Improving Remote Display Performance

**Remote Analytics – improve performance of remote display through X11 protocol acceleration/proxies.**
**General purpose solution widely applicable to many different applications**
**Addresses a major user concern of users**
**Conducted performance tests to evaluate various protocol acceleration technologies**
**Project scope and objectives documented on internal NERSC website -- next step is coordination with other groups within NERSC**
**Intent is to deploy technology to accelerate performance of applications with remote display capability**

## NERSC Remote Licensing

**Objective:**
**Allow remote users to take advantage of (expensive) commercially licensed software.**
**Implementation:**
**Consolidate all license serving inside NERSC to a central location.**
**Set up facility whereby remote users can “check out licenses” for use on their desktop machines.**
**Software supported: IDL, AVS, AVS/Express, CEI/Ensight Gold.**

## Questions?

- http://www.nersc.gov/nusers/analytics/
**Wes Bethel, NERSC Analytics Team Lead,** *ewbethel@lbl.gov*
**Cecilia Aragon,** *aragon@hpcrd.lbl.gov*
**Analytics Team,*** consult@nersc.gov*