Specialized Smith-Waterman Instructions for Parallel Sequence Alignment
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Solution Overview
Problem
The Smith-Waterman algorithm's matrix-filling phase is computationally inefficient on parallel processors due to sequential execution of instructions and excessive data movement, leading to prolonged processing times for large DNA and protein sequences.
Innovation Solution
A processor with a specialized instruction decoder and execution unit that reduces the number of instructions for computing sub-alignment scores and optimizes data storage by using a single Smith-Waterman instruction or a six-instruction sequence to concurrently compute multiple sub-alignment scores, and employs interleaved data storage to minimize data movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional parallel processing approaches are used to accelerate the matrix-filling phase, then computational throughput is improved, but the number of instructions and data movement remains excessively high
Solution Approach 1:
The patent combines multiple operations (addition, subtraction, maximum calculation) into a single specialized Smith-Waterman instruction. This merging of operations reduces the total instruction count from ten sequential instructions to a single instruction that performs all computations simultaneously, directly resolving the contradiction between improving throughput and reducing instruction complexity
Solution Approach 2:
The specialized execution unit implements a universal Smith-Waterman instruction that handles multiple sub-alignment score computations in parallel. This multi-functional instruction can compute scores for multiple sequence positions simultaneously, improving computational throughput while maintaining low instruction complexity through standardized operation handling
2Productivity
If conventional parallel processing approaches are used to accelerate the matrix-filling phase, then computational throughput is improved, but data movement overhead increases
Solution Approach 1:
The patent segments the scoring matrix computation into independent anti-diagonal segments that can be processed in parallel. By organizing computations along anti-diagonals rather than rows or columns, the patent minimizes data movement between memory and processing units, as each anti-diagonal segment requires access to a localized subset of input data, thereby reducing data movement overhead while maintaining high computational throughput
Solution Approach 2:
The patent performs preliminary organization of input sequences and substitution matrices before the main computation phase. Data is pre-processed and arranged in an optimal format that reduces the need for data movement during the actual Smith-Waterman computations, allowing the execution unit to operate with minimal data transfer overhead
3Measurement precision
If ten sequential instructions are used to compute each sub-alignment score, then computational accuracy is maintained, but processing time increases
Solution Approach 1:
The patent merges five addition/subtraction operations and five maximum operations into a single specialized instruction that computes all sub-alignment scores simultaneously. This merging maintains computational accuracy by implementing the same mathematical operations (H(j,k) = max{0, H(j-1,k-1)+S(j,k), E(j-1,k), F(j,k-1)}) while reducing processing time from ten sequential instruction cycles to a single cycle
Solution Approach 2:
The patent replaces the mechanical sequential execution of ten instructions with a specialized hardware execution unit that performs all computations in parallel. This substitution of the mechanical instruction-execution model with dedicated hardware circuitry maintains the mathematical precision of the original algorithm while dramatically reducing processing time through simultaneous operation execution
Data Source
AI summary
Various techniques for accelerating Smith-Waterman sequence alignments are provided. For example, threads in a group of threads are employed to use an interleaved cell layout to store relevant data in registers while computing sub-alignment data for one or more local alignment problems. In another example, specialized instructions that reduce the number of cycles required to compute each sub-alignment score are utilized. In another example, threads are employed to compute sub-alignment data for a subset of columns of one or more local alignment problems while other threads begin computing sub-alignment data based on partial result data received from the preceding threads. After computing a maximum sub-alignment score, a thread stores the maximum sub-alignment score and the corresponding position in global memory.