This document discusses parallel and distributed information retrieval. It describes how parallel architectures like MIMD can be used to accelerate search over very large document collections by distributing the work across multiple processors. Two main approaches to parallelism are covered: building new parallel algorithms or adapting existing techniques. Common ways to partition data for parallel indexing and search are discussed, including document partitioning and term partitioning. Specific data structures like inverted files, suffix arrays, and signature files are examined in terms of how they can be adapted for parallel and distributed retrieval architectures.

1. Special Topics in Computer ScienceSpecial Topics in Computer Science
Advanced Topics in Information RetrievalAdvanced Topics in Information Retrieval
Lecture 7Lecture 7 (book chapter 9)(book chapter 9)::
Parallel and Distributed IRParallel and Distributed IR
Alexander Gelbukh
www.Gelbukh.com

2. Previous Chapter: ConclusionsPrevious Chapter: Conclusions
 How to accelerate search? Same results as sequential
 Ideas:
 Quick-and-dirty rejection of bad objects, 100% recall
 Fast data structure for search (based on clustering)
 Careful check of all found candidates
 Solution: mapping into fewer-D feature space
 Condition: lower-bounding of the distance
 Assumption: skewed spectrum distribution
 Few coefficients concentrate energy, rest are less important

3. Previous Chapter: Research topicsPrevious Chapter: Research topics
 Object detection (pattern and image recognition)
 Automatic feature selection
 Spatial indexing data structures (more than 1D)
 New types of data.
 What features to select? How to determine them?
 Mixed-type data (e.g., webpages, or images with
sound and description)
 What clustering/IR methods are better suited for
what features? (What features for what methods?)
 Similar methods in data mining, ...

4. The problemThe problem
 Very large document collections
 Google: 4,000,000,000 pages
 Slow response?
 Solution: parallel computing
 Google: 10,000 computers

5. Parallel architecturesParallel architectures
Data stream
Single Multiple
Instructionstream
Single
SISD
classical
SIMD
simple
Multiple
MISD
(rare)
MIMD
many SISD

6. MIMD architectureMIMD architecture
 The most common
 Can be
 tightly coupled
 loosely coupled
 Distributed
 Many computers interacting via network
 PC Clusters
 Similar to MIMD computers, but greater cost of
communication
 very loosely coupled
 More coarse-grained programs

7. Performance improvementPerformance improvement
Time: speedup S
 Ideally, N times (number of processors)
 In practice impossible
 The problem does not decompose into N equal parts
 Communication and control overhead
 < 1 / f, where f is the largest separable fraction of the
problem
Cost
 Per processor: S / N

8. Two approaches to parallelismTwo approaches to parallelism
 Build new algorithms
 E.g., neural nets
 Naturally parallel
 Problem: to define the retrieval task
 Adapt the existing techniques to parallelism
 Allows relying on well-studied approaches
 We will consider this option

9. Ways to use parallelismWays to use parallelism
 Multitasking
 N search engines
 Good for processing many queries
Problems:
 A single query is not speeded up
 Bottleneck: disk access (index)
 Possible solution: replicating (part of) data. RAIDs
 Parallel algorithms
 IR = data. Main question: how to partition the data
 Document / index term matrix
(terms can be LSI dimensions, signature bits, etc)

10. Possible partitioningsPossible partitionings
 Horizontal: document partitioning. Union of results
 Vertical: term partitioning. Basically, intersect results

11. Inverted files: Logical partitioningInverted files: Logical partitioning
 Logical vs. physical document partitioning
 Logical: for each term, use pointers into inverted file data for
each processor, to indicate its portion

12. Inverted files: Logical partitioningInverted files: Logical partitioning
Construction and updatingConstruction and updating
 Also parallel
Construction
 Assign docs to processors
 Order docs such that each processor has an interval
 Process in parallel
 Merge. Each piece is ordered already

13. Inverted files:Inverted files:
Physical document partitioningPhysical document partitioning
 Several separate collections, one per processor
 Separate indices
 Then the lists are merged (they are already ordered)
 Priority queue is used
 The result is not sorted; Insertion is quick
 The maximal element can be found quickly
 First k elements can be found rather quickly
 Details in the book
 Consistent scores are needed
 Global statistics is needed. Can be computed at index
time

14. Logical or physical partitioning?Logical or physical partitioning?
 Logical requires less communication
 Faster
 Physical is more flexible. Simpler implementation
 Simpler conversion of existing systems

15. Inverted files:Inverted files: Term partitioningTerm partitioning
 Each processor processes a part of the inverted file
 The results are intersected (for AND)
 (or as appropriate for Boolean operations, OR and NOT)
 When term distribution in user queries is skewed,
then document partitioning is better
 When uniform, term partitioning is better.
 Twice for long queries, 5 – 10 times for short (Web-like)

16. Suffix arraysSuffix arrays
 Array construction can be parallelized
 merges are parallel
 Document partitioning is applied straightforwardly
 Each processor maintains its own suffix array
 Term partitioning can be applied
 Each processor owns a branch of the tree (lexicographic
interval)
 Bottleneck: all processors need access to the entire text

18. Signature filesSignature files
 Document partitioning: straightforward
 Create query signature, distribute to each processor
 Merge results (using Boolean operations if needed)
 Term partitioning: shorter signatures
 Merging and eliminating false drops is slow
 This method is not recommended

19. SIMD computersSIMD computers
 Single Instruction, Multiple data
 Uncommon
 Good for simple operations
 Bit operations in signature files
 Details in the book
 Ranking is supported in hardware in some computers
 If signature file does not fit into memory, can be
processed in batches
 I/O overhead
 Use multiple queries with the same batch
 This improves throughput, but not response time

20. …… SIMD computersSIMD computers
 Inverted files are difficult to adapt to SIMD
 The inverted file is restructured
 Details in the book

21. Distributed IRDistributed IR
 MIMD with
 Slow communication
 Not all nodes are used for a given query
 Encryption issues
 Document partitioning is usually used
 Term partitioning imposes greater communication
overhead
 Document clustering can be useful (to distribute docs
by processors)
 Index clusters and then search only the best ones
 Another approach: use training queries, then similarity of
the user query to these

22. Research topicsResearch topics
 How to evaluate the speedup
 New algorithms
 Adaptation of existing algorithms
 Merging the results is a bottleneck
 Meta search engines
 Creating large collections with judgements
 Is recall important?

23. ConclusionsConclusions
 Parallel computing can improve
 response time for each query and/or
 throughput: number of queries processed with same speed
 Document partitioning is simple
 good for distributed computing
 Term partitioning is good for some data structures
 Distributed computing is MIMD computing with slow
communication
 SIMD machines are good for Signature files
 Both are out of favor now

24. Thank you!
Till May 17? 18?, 6 pm