Implementing a Vector Kernel on a Vector Processor and GPU Concepts illustrated by this case study ¦ Programming Vector Processors ¦ Programming GPUs ¦ Performance Estimation MrBayes is a popular...

Implementing a Vector Kernel on a Vector Processor and GPU Concepts illustrated by this case study ¦ Programming Vector Processors ¦ Programming GPUs ¦ Performance Estimation MrBayes is a popular computational biology application for inferring the evolutionary histories among a set of input species based on their prealigned DNA sequence data of length n. MrBayes works by performing an heuristic search over the space of all binary tree topologies for which the inputs are the leaves. In order to evaluate a particular tree, the application must compute an n4 conditional likelihood table (named clP) for each interior node. The table is a function of the conditional likelihood tables of the node’s two descendent nodes (clL and clR, single precision floating point) and the 44 transition probability table (tiPL and tiPR, single precision floating point). One of this application’s kernels is the computation of this conditional likelihood table and is shown as follows: for (k=0; k clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T])* (tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]); clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T])* (tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]); clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T])* (tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]); clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T])* (tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]); clL += 4; clR += 4; }