Parallel DNA Sequence Alignment
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The document describes several parallel approaches for speeding up sequence alignment. It discusses splitting a DNA string into chunks that are distributed to worker nodes. Various techniques are proposed for handling matches that span multiple chunks, including using bigger chunks, on-demand requests for additional data, and having workers find partial matches along chunk edges to be combined by the master. The approaches are analyzed in terms of advantages and disadvantages, and a test plan is outlined to evaluate performance under varying parameters like number of workers and query length.
![Benchmarking consisted in evaluating and comparing running
times of each algorithm as function of the following
parameters
• Number of processors (# workers +1) [2, 4, 8, 16]
• DNA length (Small -5MB-, Medium -149 MB-, Large -292MB-)
• Query length (Small -8byte-, Medium -32byte-, Large -64byte-)
• # best allignments -Approximate search only- (10, 50, 100)
In grey the fixed value for the parameter when not evaluated
TEST PLAN](https://image.slidesharecdn.com/presdnadefinitivaaggiustata-130628072327-phpapp02/85/Parallel-DNA-Sequence-Alignment-49-320.jpg)
Parallel DNA Sequence Alignment
- 1. SEQUENCE ALIGNMENT SPEED-UP A PARALLEL APPROACH University of Salerno Parallel and Concurrent Computing Course 19 February 2013 Giuliana Carullo Luca Pepe Daniele Valenza
- 2. • Introduction • Problem definition • Simple Search • Approximate Search • Parallelization • Cross-Chunk Matching • Bigger chunk • On Demand • Side to Side Sliding Query • Approximate Search • Test plan
- 3. Sequence alignment is a process for comparing two or more DNA or RNA sequences. Sequence alignment is performed in order to find similar or identical regions in the provided sequences, or to check if it is a known sequence stored in a database.
- 4. DNA STRUCTURE DNA bases: A C G T Bounds: (A, T) (C, G)
- 5. DNA ALIGNMENT Affinity measures: • MATCH • MISMATCH • GAP MATCHING TYPE: • SIMPLE • REVERSE AND COMPLEMENT Q: ATGATTACC DNA String R(Q): CCATTAGTA Reverse C (R(Q)): GGTAATCAT Complement
- 6. • Global Alignment: • Local Alignment: • Local Alignment: DNA ALIGNMENT TYPES
- 7. • Introduction • Problem definition • Simple Search • Approximate Search • Parallelization • Cross-Chunk Matching • Bigger chunk • On Demand • Side to Side Sliding Query • Approximate Search • Test plan
- 8. Searching all the perfect matchings of a small query string in a bigger DNA string. INPUT: DNA String, Query String OUTPUT: Number of occurences, Occurences starting positions SIMPLE SEARCH Variables Notation # Workers 𝑛 Query length 𝑙 𝑞 DNA Length 𝑙 𝑑 Relative pos. 𝑂𝑓𝑓𝑖 Absolute pos. 𝑠𝑖
- 9. Searching the «best» n alignments of a small query string in a bigger DNA string INPUT: DNA String, Query String OUTPUT: Best alignments starting positions APPROXIMATE SEARCH Variables Notation # Workers 𝑛 Query length 𝑙 𝑞 DNA Length 𝑙 𝑑 Relative pos. 𝑂𝑓𝑓𝑖 Absolute pos. 𝑠𝑖
- 10. APPROXIMATE SEARCH – SIMILARITY EVALUATION Character similarity function 𝑠𝑖 = 𝑥, 𝑀𝑎𝑡𝑐ℎ 𝑦, 𝑀𝑖𝑠𝑚𝑎𝑡𝑐ℎ 𝑧, 𝐺𝑎𝑝 x > 0; y, z ≤ 0 (In this work gaps are not considered) Objective function to maximize: 𝑆 = 𝑖 𝑙 𝑞 𝑠𝑖
- 11. • Introduction • Problem definition • Simple Search • Approximate Search • Parallelization • Cross-Chunk Matching • Bigger chunk • On Demand • Side to Side Sliding Query • Approximate Search • Test plan
- 12. The common approach to all solutions is based on Map Reduce model: • Master node splits the string into chunks and scatters them to workers node. • The workers perform the computation and results are sent back to the master. • Master combines the single solutions and returns the output. GENERAL IDEA Attention must be paid to the cross-matching strings
- 13. GENERIC SPLIT AND COMPUTATION Complete Matching Partial Matching 𝑇0 𝑇1 𝑇2 𝑇3 𝑇4 𝑇5 𝑇6 𝑇7 Query string DNA string 𝑙 𝑑/n 𝑙 𝑑/n 𝑙 𝑑/n Chunk size Chunk 𝒊 − 𝟏 Chunk 𝒊 Chunk 𝒊 + 𝟏
- 14. GENERIC REDUCE PHASE 𝑖 ∗ (𝑙 𝑑 𝑛) + 𝑜𝑓𝑓𝑖 Worker ID Offset 𝑖 𝑜𝑓𝑓𝑖 𝑗 𝑜𝑓𝑓𝑗 𝑙 𝑞 Query string DNA string Size WORKERS OUTPUT FINAL OUTPUT 𝑗 ∗ (𝑙 𝑑 𝑛) + 𝑜𝑓𝑓𝑗 𝑙 𝑞 Positions 𝑠𝑖 𝑠𝑗 𝑠𝑖 𝑠𝑗
- 15. • Introduction • Problem definition • Simple Search • Approximate Search • Parallelization • Cross-Chunk Matching • Bigger chunk • On Demand • Side to Side Sliding Query • Approximate Search • Test plan
- 16. Bigger chunk: The master sends to every worker a chunk of sizes = 𝑙 𝑑 𝑛 + 𝑙 𝑞 − 1 such that cross chunk matching strings can be found. On Demand: The master sends chunks of sizes = 𝑙 𝑑 𝑛 , whether a worker finds a partial matching at the end of its chunk, it asks the remaining part r ≤ 𝑙 𝑞 − 𝑘 such that cross chunk matching strings can be found. Two possible heuristics: big request and small request Side to Side Sliding Query (3SQ): Every worker receives a chunk of sizes = 𝑙 𝑑 𝑛 and computes its complete matchings and all partial matchings. Partial matchings will be combined by the master in order to find Cross-Chunk Matchings. SOLUTION APPROACHES
- 17. 𝑇0 𝑇1 𝑇2 𝑇3 𝑇4 𝑇5 𝑇6 𝑇7 BIGGER CHUNK APPROACH Complete Matching Chunk 𝒊 − 𝟏 𝑙 𝑑/n Query string DNA string 𝑙 𝑞-1 Chunk size Chunk 𝒊 𝑙 𝑑/n 𝑙 𝑞-1 Chunk 𝒊 + 𝟏 𝑙 𝑑/n 𝑙 𝑞-1 Same Char
- 18. ADVANTAGES: • it does not requires intra-workers communication; • it does not produce duplicated occurrences; • the master has an extremely small sequential work to perform. DISADVANTAGES: • each worker (except the last one) receives 𝑙 𝑞 − 1 extra characters Thus, an extra bandwidth 𝑏 𝑒 usage is produced such as: 𝑏 𝑒 = 𝑙 𝑞 − 1 ⋅ (𝑛 − 1) BIGGER CHUNK APPROACH
- 19. Bigger Chunk analogous to generic approach On Demand: analogous to generic approach Side to Side Sliding Query (3SQ): additional work is performed by master node for combining partial matchings. REDUCE PHASE
- 20. Bigger chunk: The master sends to every worker a chunk of sizes = 𝑙 𝑑 𝑛 + 𝑙 𝑞 − 1 such that cross chunk matching strings can be found. On Demand: The master sends chunks of sizes = 𝑙 𝑑 𝑛 , whether a worker finds a partial matching at the end of its chunk, it asks the remaining part r ≤ 𝑙 𝑞 − 𝑘 such that cross chunk matching strings can be found. Two possible heuristics: big request and small request Side to Side Sliding Query (3SQ): Every worker receives a chunk of sizes = 𝑙 𝑑 𝑛 and computes its complete matchings and all partial matchings. Partial matchings will be combined by the master in order to find Cross-Chunk Matchings. SOLUTION APPROACHES
- 21. 𝑇0 … 𝑇𝑗 𝑇𝑗 + 1 𝑇𝑗 + 2 𝑇𝑗 + 3 ON DEMAND – BIG REQUEST APPROACH 𝑙 𝑑/n v v v v v x v v v v … Chunk 𝒊 Chunk 𝒊 + 𝟏 Complete Matching Partial Matching Query string DNA string Chunk size
- 22. ADVANTAGES: • extra data is requested only when needed • it does not produce duplicated occurrences • a single request is performed for each worker DISADVANTAGES: • extra overhead for the big request • potential useless extra characters ON DEMAND – BIG REQUEST APPROACH
- 23. 𝑇0 … 𝑇𝑗 𝑇𝑗 + 1 𝑇𝑗 + 2 𝑇𝑗 + 3 ON DEMAND – SMALL REQUEST APPROACH 𝑙 𝑑/n v v v v v x v v v v … Chunk 𝒊 Chunk 𝒊 + 𝟏 Complete Matching Partial Matching Query string DNA string Chunk size
- 24. ADVANTAGES: • extra data are requested only when needed • it does not produce duplicated occurrences • better bandwidth usage than big request DISADVANTAGES: • Number of requests grows proportionally to the length of the query ON DEMAND – SMALL REQUEST APPROACH
- 25. Two kind of communication can be adopted: ON DEMAND – CENTRALIZED VS DISTRIBUTED Centralized: request is made to master node Distributed: request is made to adjacent right node k )
- 26. ON DEMAND – CENTRALIZED VS DISTRIBUTED Centralized Distributed ADVANTAGES Master idle time is reduced. No extra accesses to DNA are needed. No linearization point. DISADVANTAGES Linearization point is added. Access to DNA must be performed. Extra data requests may be slowed down.
- 27. Bigger Chunk analogous to generic approach On Demand: analogous to generic approach Side to Side Sliding Query (3SQ): additional work is performed by master node for combining partial matchings. REDUCE PHASE
- 28. Bigger chunk: The master sends to every worker a chunk of sizes = 𝑙 𝑑 𝑛 + 𝑙 𝑞 − 1 such that cross chunk matching strings can be found. On Demand: The master sends chunks of sizes = 𝑙 𝑑 𝑛 , whether a worker finds a partial matching at the end of its chunk, it asks the remaining part r ≤ 𝑙 𝑞 − 𝑘 such that cross chunk matching strings can be found. Two possible heuristics: big request and small request Side to Side Sliding Query (3SQ): Every worker receives a chunk of sizes = 𝑙 𝑑 𝑛 and computes its complete matchings and all partial matchings. Partial matchings will be combined by the master in order to find Cross-Chunk Matchings. SOLUTION APPROACHES
- 29. … 𝑇0 𝑇1 𝑇2 𝑇3 𝑇𝑗 𝑇𝑗+1 𝑇𝑗+2 𝑇𝑗+3 SIDE TO SIDE SLIDING QUERY (3SQ) APPROACH Complete Matching Right-side Partial Matching Query string DNA string 𝑙 𝑑/n 𝑙 𝑑/n 𝑙 𝑑/n Chunk size Chunk 𝒊 − 𝟏 Chunk 𝒊 … Left-side Partial Matching … Chunk 𝒊 + 𝟏 …
- 30. ADVANTAGES: • no extra data is required • it does not produce duplicated occurrences • no extra communication is needed • the master does not need to store the DNA string • it reduces bandwidth consumption to perform cross-chunk strings checking. Indeed workers return bits instead of integers. DISADVANTAGES: • Extra work is required to the master (partial matchings combine) SIDE TO SIDE SLIDING QUERY (3SQ) APPROACH
- 31. Bigger Chunk analogous to generic approach On Demand: analogous to generic approach Side to Side Sliding Query (3SQ): additional work is performed by master node for combining partial matchings. REDUCE PHASE
- 32. 3SQ REDUCE PHASE 𝑖 ∗ (𝑙 𝑑 𝑛)- j 1 1 0 1 𝑙 𝑞 Query match DNA string Size WORKER i Right side array FINAL OUTPUT 𝑖 ∗ (𝑙 𝑑 𝑛)- k 𝑙 𝑞 Positions 𝑠𝑗 𝑠 𝑘 Results array 𝑠 𝑘 1 0 0 1 AND 1 0 0 1 WORKER i+1 Left side array 𝑠𝑖 𝒋 𝒌
- 33. • Introduction • Problem definition • Simple Search • Approximate Search • Parallelization • Cross-Chunk Matching • Bigger chunk • On Demand • Side to Side Sliding Query • Approximate Search • Test plan
- 34. Same as simple search • Splitting phase: same of simple search • Computation phase: • Similarity function is evaluated for every alignment of query string • Likely simple search, Cross-chunk strings must be considered • Every worker returns its 𝑛 best similarity values, with relative positions • Reduce phase: All similarity values are merged in order and the best 𝑛 alignments are returned PARALLELIZATION MODEL
- 35. REDUCE PHASE Off. Similarity X 10 Y 8 Z 3 Off. Similarity A 5 B -3 C -6 W. Id Off. Sim. 1 X 10 1 Y 8 2 U 7 3 A 5 1 Z 3 2 V 2 2 W -1 3 B -3 3 C -6 Pos. Similarity X’ 10 Y’ 8 U’ 7 O R D E R E D M E R G E P O S. T R A N S L A T I O N Off. Similarity U 7 V 2 W -1 FINAL OUTPUT Worker 1 Worker 2 Worker 3
- 36. Bigger chunk: The master sends to every worker a chunk of size s ≤ 𝑙 𝑑 𝑛 + 𝑙 𝑞 − 1 such that cross chunk matching similarities can be evaluated. Side to Side Sliding Query (3SQ): Every worker receives a chunk of size s = 𝑙 𝑑 𝑛 and computes its similarity values and all partial similarities (leftside and rightside). Partial similarities will be summed by the master in order to compute Cross- Chunk String similarity values. CROSS-CHUNK MATCHING
- 37. 3SQ PARTIAL SIMILARITY COMBINE PHASE 4 2 0 1 𝑙 𝑞 Query match DNA string Size WORKER i Right side array OUTPUT W. Id. Off. Sim i 𝑠𝑗 5 i 𝑠 𝑘 3 i … Results array sk 1 0 3 -4 + 5 2 3 -3 WORKER i+1 Left side array si 𝒋 𝒌 𝑠𝑗 = 𝑙 𝑐 − (𝑙 𝑞 − 1)+ j 𝑙 𝑞 Chunk 𝒊 Chunk 𝒊 + 𝟏
- 38. • Introduction • Problem definition • Simple Search • Approximate Search • Parallelization • Cross-Chunk Matching • Bigger chunk • On Demand • Side to Side Sliding Query • Approximate Search • Test plan
- 39. Varying parameters: • Number of Workers • Query Length We plan to evaluate the running times of every presented algorithm. The analysis of these results will validate our proposal, highlighting the algorithm that performs better. OVERVIEW
- 40. SEQUENCE ALIGNMENT SPEED-UP A PARALLEL APPROACH University of Salerno Parallel and Concurrent Computing Course 19 February 2013 Giuliana Carullo Luca Pepe Daniele Valenza DEVELOPMENT AND BENCHMARKING
- 41. • Implementation • Introduction • DNA Splitting • Bandwidth usage • Comunication • Benchmarking • Testing environment • Test plan • Results • Conclusions
- 42. Every proposed algorithm has been implemented using C language and OpenMPI library Advantages: • High performances • Scalability • Portability INTRODUCTION
- 43. A natural approach: load it entirely from file, calculate the size (𝑙 𝑑), split it in 𝑛 chunks and send them to the workers Problems: A DNA genome may be very large (3.0 ×109 bp (base pairs) ) The available memory can’t be enough. Projectual choice: The whole DNA is actually never needed DNA is never entirely loaded in memory, first dna and chunk size are calculated, and then step by step 𝑙 𝑐 characters are read from file and sent to a worker. PROJECTUAL CHOICES: DNA SPLITTING
- 44. • The type of messages exchanged during the simple search computation would normally consist in: • Characters (splitting phase) • Integers (Reduce phase) Bandwidth usage: • 1 byte (Char size) x lc x n - Splitting phase • 4 byte (Integer size) x lc x n (best case, all matchings) – Reduce phase Can we do better? … YES! PROJECTUAL CHOICES: BANDWIDTH USAGE
- 45. In the Simple Search algorithm, a compression can be performed in order to drastically reduce bandwidth consumption. Simple Search Reduce phase Compression: instead of sending actual positions, a bit array of size 𝑙 𝑐 is exploited. Bit array costruction: for each position, if a matching is found starting from it, the bit is set to 1, 0 otherwise. Compression Ratio: 1: 32 (E.g, with 4 integers from 4 positions to 128 positions) PROJECTUAL CHOICES: BANDWIDTH USAGE
- 46. COMUNICATION Master to workers Extra Comunication Workers to Master Messages Data Type Type Messages Data Type Type Messages Data Type Type Bigger Chunk N(ld/n+lq-1) Char Async Sync X N(ld/n) Int Bit Sync Sync On Demand: N(ld/n) Char Async Sync N-1(lq-1) Char Sync Sync N(ld/n) Int Bit Sync Sync 3SQ N(ld/n) Char Async Sync X N(ld/n)+2(l q-1) Int Bit Sync Sync
- 47. • Implementation • Introduction • DNA Splitting • Bandwidth usage • Comunication • Benchmarking • Testing environment • Test plan • Results • Conclusions
- 48. Cluster 8 Nodes - Ethernet 100Mbps connection Node CPU: Intel Xeon Dual Core 2.8 Ghz RAM: 4GB Hard Drive: 2x 30GB SCSI Software OS: Debian 6.0.4 OpenMPI 1.6.1 TESTING ENVIRONMENT Image for illustrative purposes only
- 49. Benchmarking consisted in evaluating and comparing running times of each algorithm as function of the following parameters • Number of processors (# workers +1) [2, 4, 8, 16] • DNA length (Small -5MB-, Medium -149 MB-, Large -292MB-) • Query length (Small -8byte-, Medium -32byte-, Large -64byte-) • # best allignments -Approximate search only- (10, 50, 100) In grey the fixed value for the parameter when not evaluated TEST PLAN
- 50. SIMPLE SEARCH: NUMBER OF WORKERS (1/2) Results: • Good Scalability for every algorithm • 3SQ worse than the others because additional sequential work must be performed.
- 51. SIMPLE SEARCH: NUMBER OF WORKERS (2/2) Results: • Bigger Chunk Bit performs better than int solution. • Increasing processors, bigger chunk performs better than the others because more cross- chunk matchings occur. • No relevant improvements occurred between 8 and 16 processors.
- 52. SIMPLE SEARCH: SPEED UP 0 0,5 1 1,5 2 2,5 3 3,5 4 2 4 8 16 SPEEDUP NUMBER OF PROCESSORS Speed Up Simple Search DNA Size: Big Query Size: Small BC-bit OD-cent BC-int OD-dist 3SQ Results: • Increasing speedup for every algorithm (except BC-int) • The speedup grows proportionally to 𝑛 + 1 • BC-int suffers from network bottleneck due to the size of the messages.
- 53. SIMPLE SEARCH: DNA LENGTH Results: • Good Scalability for every algorithm • 3SQ worse: additional sequential work than others…. • Bigger Chunk Bit performs better than int solution • Execution times grows linearly respect to DNA size
- 54. SIMPLE SEARCH: QUERY LENGTH Results: • 3SQ is highly sensible to query length variations due to partial matching combine phase. • No significative variations for other algorithms since single Query Matching is interrupted on first mismatch found.
- 55. APPROXIMATE SEARCH: NUMBER OF WORKERS Results: • Running times decrease linearly respectively to the number of processors. • 3SQ is only slightly worse than Bigger chunk because the sequential work is almost the same (Ordered Merge)
- 56. APPROXIMATE SEARCH: SPEED UP 0 2 4 6 8 10 12 14 16 2 4 8 16 SPEEDUP NUMBER OF PROCESSORS Speed Up Approximate Search DNA Size: Medium Query Size: Small 3SQ BC-int Results: Speed up globally better than simple search and close to the ideal value.
- 57. APPROXIMATE SEARCH: DNA SIZE Results: Running times grows linearly respectively to the DNA SIZE Motivation The main sequential computation consists in Ordered Merge that has linear complexity.
- 58. APPROXIMATE SEARCH: QUERY SIZE Results: Running times is influenced by Query Size. Motivation The computation of similarity function is affected by query length.
- 59. APPROXIMATE SEARCH: NUMBER OF BEST ALIGNMENTS Results: Running times grows almost linearly. Motivation Each worker returns to the master its Number of best alignments and the ordered merge process is affected by it. 0,00 5,00 10,00 15,00 20,00 25,00 30,00 35,00 10 50 100 RUNNINGTIME(SECONDS) NUMBER OF BEST ALIGNMENT Approximate Search DNA size: Big Processor: 16 Query Size: Small BC-int 3SQ
- 60. • Implementation • Introduction • DNA Splitting • Bandwidth usage • Comunication • Benchmarking • Testing environment • Test plan • Results • Conclusions
- 61. The winner is…. Bigger Chunk On Demand 3SQ
- 62. Further improvements can be applied to the presented algorithms Splitting phase: DNA alphabet consists merely in 4 characters, 2 bit are enough to rappresent the character, instead of 8 bit Bit Mapping: e.g. A=00, T=01, C=10, G=11 Compression Ratio: 1: 4 (E.g, with 1 character from 1 base to 4 bases) IMPROVEMENTS
- 63. 3SQ algorithm: Partial matchings combine phase can be performed in a distributed manner Each node sends its left or right partial matching to left or right sibling, which will combine it with his results and send them to master. In this way sequential work can be reduced IMPROVEMENTS
- 64. Thanks !