Challenging Cloning Related Problems with GPU-Based Algorithms

1. Authors : Thierry Lavoie、Michael Eilers-Smith、Ettore Merlo Publisher: ACM IWSC’10 Presenter: Ye-Zhi Chen Date: 2011/12/21 Challenging Cloning Related Problems with GPU-Based Algorithms

2. Introduction • This paper describes an implementation of the Smith-Watterman algorithm for proper clone filtering

3. Algorithm • To address the clone detection false positives problem by an appropriate filtering technique ; the DP-matching seemed to be an interesting choice

4. Algorithm

5. Algorithm GPU DP-matching ： • Find what cells of the matrix are free ofcomputational dependencies in order to compute their valueson separate cores simultaneously • It is simple to check that every cells on theanti-diagonals become free of any computational dependenciesat the same moment because their value is solely dependenton the cells of the previous anti-diagonals.

6. Algorithm • Let Vk represents the linear buffer computed at step k. Let fk be the following map between the Indexes of V and those of the matrix D : u can be seen as the index of threads , s1 and s2‘s first character are gaps

7. Algorithm

8. The characters which are compared top left Upper left

9. Algorithm Worst case problem: • The worst case of the classical DP-matching algorithm has a quadratic running time. • In the general worst case, the GPU-based implementation also has a running quadratic worst time. • However, since a large number of cores perform the computation at the same time, the hidden quadratic constant can be divided by a large factor

10. Algorithm • On very small instances of DP-matching problems, the CPU might outrun the GPU, mostly because of memory bandwidth limitations • If computation on such very small instances is to be performed on a basis of one string matched against a set of strings, there’s a way of packing the data on the GPU to make the total computation more efficient.

11. Algorithm • Let C be a set of strings and let c0 be an element of C. Lets define C’ as: C ’= C − {c0} The problem is then defined as matching c0 against all ci in C’. • Practical implementations need to pad the strings to be matched.This will enforce the numberof computational steps k to be the same in each sub matrix.The length of the padding p of a ci isdefined as follow: p = len(ci) − max(len(cj)|cj ∈ C) • Each padded ci of C’ is then concatenated to each otherseparated by a special blank character

12. k’s initial value is not 0,the initial value is |C’-1|*(max(len(ci)|ci∈C)+1) the number of computational steps k is reduced to 2*(max(len(ci)|ci ∈ C))-1

14. the indexes γ corresponding to these cells can be evaluated with this equation: γ = x ∗ (max(len(ci)|ci ∈ C) + 1) ∀ x ∈ {0..|C| − 1}

15. EXPERIMENTAL Equipment: Intel Core 2 Duo computer 3.00 GHz with 6MB of cache, 3GB of RAM and a GeForce 8800GT