1. Scientific Data Management (SDM)
Center For Enabling Technologies (CET)
Lead Institution: LBNL
Coordinating PI: Arie Shoshani

2. Scientific Data Management Center
Participating Institutions
Center PI:
Arie Shoshani LBNL
DOE Laboratories co-PIs:
Bill Gropp, Rob Ross* ANL
Arie Shoshani, Doron Rotem LBNL
Terence Critchlow*, Chandrika Kamath LLNL
Nagiza Samatova* ORNL
Universities co-PIs :
Mladen Vouk North Carolina State
Alok Choudhary Northwestern
Bertram Ludaescher, Ilkay Altinas UC Davis + SDSC
Steve Parker U of Utah
* Area Leaders

3. A Typical SDM Scenario Task A: Generate Time-Steps Flow Tier Task B:
Move TS
Task C:
Analyze TS
Task D:
Visualize TS +
Control Flow
Layer
Applications &
Software Tools
Layer
Data
Mover
Post
Processing
Parallel R
Terascale
Browser
Simulation
Program
Work Tier
I/O System
Layer
HDF5
Libraries
Parallel
NetCDF
PVFS
Sabul
SRM
Storage & Network
Resouces
Layer

4. Technology Details by Layer
Scientific
Scientific
WorkFlow
WorkFlow
Web
Web
Process
Process
Management
Management
Wrapping
Wrapping
Automation
Automation
Tools
Tools
Tools
Tools
(SPA)
(SPA)
Layer
Layer
Data
Data
ASPECT:
ASPECT:
Parallel R
Data
Data
Efficient
Efficient
Efficient
Efficient
Mining &
Mining &
integration
integration
Statistical
Analysis
Analysis
indexing
indexing
Parallel
Parallel
Analysis
Analysis
Framework
Framework
tools
tools
(Bitmap
(Bitmap
Visualization
Visualization
Analysis
(DMA)
(DMA)
(PCA, ICA)
(PCA, ICA)
Index)
Index) ( (
pVTK
pVTK ) )
Layer
Layer
Storage
Storage
Storage
Storage
Parallel
Parallel
ROMIO
ROMIO
Parallel
Parallel
Efficient
Efficient
Resource
Resource
NetCDF
NetCDF
MPI
MPI - - IO IO
Virtual
Virtual
Access
Access
Manager
Manager
Software
Software
System
System
File
File
(SEA)
(SEA)
(To HPSS)
(To HPSS)
Layer
Layer
System
System
Layer
Layer
Hardware, OS, and MSS (HPSS)
Hardware, OS, and MSS (HPSS)

5. Example Data Flow in TSI
Logistical Network
Courtesy: John Blondin

6. Using the Scientific Workflow Tool (Kepler) Emphasizing Dataflow (SDSC, NCSU, LLNL)
Automate data generation, transfer and visualization of a
large-scale simulation at ORNL

7. FastBit
A compressed bitmap indexing technology for efficient searching of read-only data
Don’t say anything about URL

8. FastBit Overview FastBit is designed to search multi-dimensional data
Conceptually in table format
rows  objects
columns  attributes
FastBit uses vertical (column-oriented) organization for the data
Efficient for analysis of read-only data
FastBit uses compressed bitmap indices to speed up searches
Proven in analysis to be optimal for single-attribute queries
Superior to other optimal indices because they are also efficient for multi-attribute queries
column
row
Vertical organization examples: Kdb (1998), C-store (2005)

9. Basic Bitmap Index Data values b0 b1 b2 b3 b4 b5 1 1 1 1 1 1 1 5 3 2 4
Compact: one bit per distinct value per object
Easy to build: faster than common B-trees
Efficient to query: only bitwise logical operations
A < 2  b0 OR b1
2<A<5  b3 OR b4
Efficient for multi-dimensional queries
Use bitwise operations to combine the partial results b0 b1 b2 b3 b4 b5 1 1 1 1 1 1 1 5 3 2 4 =0 =1 =2 =3 =4 =5

10. Grid Collector Features
Key features of the Grid Collector:
Providing transparent object access
Selecting objects based on their attribute values
Improving analysis system’s throughput
Enabling interactive distributed data analysis
Here is what we get when they work together.
Refer to the picture first: (logical request, bitmap index, hrm, files back into cache, event iterator deliver the event to the analysis code)
Then say, this provides: (the point on the top)

11. Grid Collector Speeds up Analyses
Test machine: 2.8 GHz Xeon, 27 MB/s read speed
When searching for rare events, say, selecting one event out of 1000, using GC is 20 to 50 times faster
Using GC to read 1/2 of events, speedup > 1.5, 1/10 events, speed up > 2.

12. FastBit-Based Multi-Attribute Region Finding is Theoretically Optimal
Time required to identify regions in 3D Supernova simulation (LBNL)
Flame Front discovery
(range conditions for multiple measures)
in a combustion simulation (Sandia)
On 3D data with over 110 million points,
region finding takes less than 2 seconds

13. from Files to Object Management
Objects On-Demand:
from Files to Object Management
A Scientific Application Partership (SAP)
Lead Institution: BNL
Coordinating PI: Jerome Lauret

14. Participating Institutions
BNL : Jerome Lauret
LBNL : John Wu
SLAC: Andy Hanushevsky
Technologies
FastBit
SRM (DRM, HRM)
xrootd

15. Client sees all servers as xrootd data servers
Xrootd:Single Level Switch A
open file X
Redirectors
Cache file
location
2nd open X
go to C
Who has file X? B
go to C
open file X
I have C
Client
Redirector
(Head Node)
Data Servers
Cluster
Client sees all servers as xrootd data servers

16. Client sees all servers as xrootd data servers
Xrootd:Single Level Switch A
DRM
open file X
Redirectors
Cache file
location
archive
2nd open X
go to C
Who has file X? B
DRM
HRM
go to C
MSS
open file X
I have C
DRM
Client
Redirector
(Head Node)
Data Servers
Cluster
Client sees all servers as xrootd data servers

17. Objects on-demand xrootd Here is what we get when they work together.
Refer to the picture first: (logical request, bitmap index, hrm, files back into cache, event iterator deliver the event to the analysis code)
Then say, this provides: (the point on the top)

18. Storage Resource Management (SRM)
Center For Enabling Technologies (CET)
Lead Institution: LBNL
Coordinating PI: Alex Sim

19. Participating Institutions
BNL : Jerome Lauret
FNAL : Don Petravick, Timur Perelmutov
TJNAF : Andy Kowalski
LBNL : Alex Sim, Arie Shoshani
UCSD : Abhishek Singh Rana
U. of Wisc: Miron Livny

20. Proposed work
Development of new functional features as part of the SRM collaboration (coordinated by LBNL)
Authorization
Monitoring
Performance estimation
Development of new versions of SRMs by participating institutions
Disk systems and HPSS (LBNL)
dCache (FNAL)
Jasmine (TJNAF)

21. New Aspects
Development of monitoring components for bandwidth and networking availability (U. Wisc, FNAL)
Better control of SRM behavior
Performance estimation
Integration of Lambda station interface into the SRM middleware (FNAL)
Development of an authorization framework (UCSD)
To enforce access privileges
used by SRMs for policy declaration by VO and Sites

22. SRM Collaboration
Continued support of SRMs in experiments and projects
ATLAS (BNL, FNAL)
CLAS (TJNAF)
CMS (FNAL)
CPES (LBNL)
ESG (LBNL)
Lattice QCD (TJNAF, FNAL)
Phenix (BNL)
STAR (BNL, LBNL)
Coordination with other centers and institutes (including LCG, RAL, EGEE).
Goal: joint specification of SRM through regular meetings, joint documents, and GGF participation