Searching Large Scientific Data

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Searching Large Scientific Data

• John Wu
• Scientific Data Management
• Lawrence Berkeley National Laboratory

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Outline

• Highlight of Accomplishments
• Grid Collector (accelerate others work)
• Query-Driven Visualization (enabling new way of
  knowledge discovery)
• Molecular docking (enabling others to accomplish
  great things)
• Outlook
• More complex searches
• Parallelization
• Supporting more data formats
• Integration with large framework

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FastBit In a Nutshell

• FastBit is designed to search multi-dimensional
  append-only data
• Conceptually in table format
• rows ? objects
• columns ? attributes
• FastBit uses vertical (column-oriented)
  organization for the data
• Efficient for searching
• FastBit uses bitmap indices with our compression
  method
• Proven in analysis to be optimal for
  one-dimensional queries
• Faster than other optimal indexes for
  multi-dimensional queries

column
row
Wu, Otoo, Shoshani 2006
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Motivation

• Scientific datasets are getting larger fast
• Most data analysis algorithm can not handle a
  whole dataset
• Therefore, most data analysis tasks are performed
  on a subset of the data
• Some examples of searches
• Find the collision events with the most distinct
  features of Quantum-Qluon-Plasma from a
  high-energy physics experiment
• Find and tracking ignition in a combustion
  simulation
• Identify the puppet-master bedind a distribution
  denial-of-service attack on a computer network

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Highlight 1 Grid Collector

• Searching over billions of objects with hundreds
  of attributes each
• Distributed analysis over the Grid
• Make petabytes of raw data available for world
  wide analyses
• Benefits of the Grid Collector
• Transparent object access, select objects based
  on their attributes
• Improvement of analysis systems throughput
• Best Paper Award (ISC05) Wu, Gu, Lauret,
  Poskanzer, Shoshani, Sim and Zhang 2005

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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 gt 1.5,
  1/10 events, speed up gt 2.
• Bottom line improve the throughtput of data
  analyses!

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Highlight 2 Visualization

• Query-Driven Visualization collaboration
  between SDM and VACET
• Use FastBit indexes to efficiently select the
  most interesting data for visualization
• Above example laser wakefield accelerator
  simulation
• VORPAL produces 2D and 3D simulations of
  particles in laser wakefield
• Finding and tracking particles with large
  momentum is key to design the accelerator
• Brute-force algorithm is quadratic (taking 5
  minutes on 0.5 mil particles), FastBit time is
  linear in the number of results (takes 0.3 s,
  1000 X speedup)

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Bin-Based Parallel Coordinate Display

• Integrate FastBit with H5Part, a HDF5 package for
  particle physics data
• Use FastBit to compute histograms efficiently
• Bin-based parallel coordinate display reduces the
  number of lines displayed on screen, reduces
  visual clutter, reduces response time
• FastBit further speeds up the response time
  further

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FastBit Speeds up Historgraming
Lower is better
104 X

• Time needed to compute desired histograms
• Custom code that directly uses the raw data
  directly
• FastBit can be 1000 X faster than the custom code
  (left)
• FastBit maintains the performance advantage on a
  parallel system

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Highlight 3 Molecular Docking

• Jochen Schlosser schlosser_at_zbh.uni-hamburg.deCe
  nter for Bioinformatics, University of Hamburg
• Application Structure-based virtual screening
  (ACS Fall 2007)

Standard approach match every ligand with every
target protein New approach using FastBit
indexes to avoid brute-force matching
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Use of FastBit for Molecular Docking

• Method
• Specification of the descriptor as triangle
  geometry
• Types of interaction centers
• Triangle side lengths
• Interaction directions
• 80 bulk dimensions
• Receptors
• Receptor descriptors are generated similarly
• Using complementary information where necessary
• Use of pharmacophore constraints on receptor
  triangles
• Reduces number of queries
• Improved query selectivity because the
  pharmacophore tends to be inside the protein
  cavity

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Use of FastBit for Molecular Docking

• Method
• Indexing system
• Properties of the problem
• Billions of descriptors ( 1,000 for each ligand)
• High dimensional query
• Properties of bitmap indexes
• Well suited for those kind of queries
• Can be run stand alone
• Further compression possible
• FastBit uses compression

• Results
• TrixX-BMI is an efficient tool for virtual
  screening with average runtime in sub-second
  range
• screen libraries of ligands 12 times faster than
  FlexX without pharmacophore constraints
• With pharmacophore constraints, speedup 140 250

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Outline

• Highlight of Accomplishments
• Grid Collector
• Query-Driven Visualization
• Molecular docking
• Outlook
• More complex searches
• Parallelization
• Supporting more data formats
• Integration with large framework

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Complex Searches

• So far, FastBit software primarily handles range
  queries of the form pressure gt 105 and
  temperature between 800 and 1000
• Need to support complex types of searches
• GTC data analysis find all particles with
  certain energy level that have passed through a
  region with specified properties on the electric
  field
• Network security find the hosts that have
  contacted all identified drones within an hour of
  the start of an attack
• Protein sequences Identify known proteins with
  specified molecular weight
• Catalog matching matching records of stars and
  galaxies from one survey / simulation to another
  one
• Subqueries searching the results of previous
  searches

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Complex Searches

• Extending the histograming functionality group
  by, top-k, automatic computation of derived
  fields
• Implement join algorithm
• Existing bitmap indexes are efficient for
  filtering out the desired records for common join
  algorithms such as sort-merge join
• Existing bitmap index based join algorithms
  appear promising from back-of-envelope
  calculation
• A algorithm for programs such as neighborhood
  expansion, formulating them as joins may be not
  as efficient as using alternative searching
  algorithms, such as, A

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Parallelization

• For I/O dominated tasks,
• Take advantage of parallel I/O system, PVFS
• Better data layout to effectively utilize the I/O
  hardware
• Active Storage, In-Situ data processing
• For CPU dominated tasks,
• Devise new algorithms, e.g., parallel join
  algorithms, new join indexes
• Algorithms for GPU, Cell processor, and many-core
  architecture

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More Data Formats

• Working with application specialist to integrate
  FastBit with their data library
• H5Part HDF5
• ROOT (?)
• ADIOS
• Restructure FastBit to make it easier to work
  with different data formats
• Virtualize data sources

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Integrated Data Analysis Framework

• Iterator for coarse grain data
• Examples ROOT and Map-Reduce
• Indexing provides a way to implement a smart
  iterator, e.g., Grid Collector for STAR data
  analysis framework (using ROOT)
• Framework for fine grain data
• Tighter integration with programmatic API
• Provide scripting support for productivity layer
  (end user)

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Indexes Facilitate Smart Analysis

• Indexes go here!
• Or
• How to make your system smarter!