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These are the minimum requirements for creating a loop that iterates a set number of times. An induction variable that increases or decreases each iteration.A condition variable (boolean variable for the branch argument).Now that we are able to find a loop, it must have the following elements to be vectorizable: As such, we define the loop as all blocks along the path from B to A, which we find using DFS. This is essentially the control flow edge that transitions from the end of the loop back to the beginning. We first define a back edge as an edge from A to B where A and B are basic blocks and B dominates A.
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Transfer: Current block unioned with in-edge Loops Merge: Set intersection (or empty set if first block in program) We need a dominator analysis to find loops, and so we implemented this dataflow analysis as follows:Įdge: Each out edge of a block consists of all blocks that dominate it, including itself
#Python vectorize for loop serial
To vectorize a loop, we must first detect loops, check whether they have dependencies that prevent vectorization, vectorize array operations, and deal with extraneous serial instructions. In addition, this choice proved to be a valuable learning opportunity. We did not use his C++ dynamic library, and instead wrote a new dynamic library in Rust with a wider range of function calls as we were more familiar with the Rust specification. We use his array implementation and the vector operations that he provides which operate on vectors of length four. We build on Philip Bedoukian's work that brings vector instruction support into the Bril interpreter. This allows for i to be incremented by four each iteration instead of by one.įor this project, we designed and implemented automatic loop vectorization by converting serial operations on array elements to their vector counterparts. The second loop executes for a fourth the number of iterations of the first loop while behaving identically by using vload, vstore, and vadd that operate on four array elements at a time. We can leverage this feature to optimize loops that operate iteratively on arrays by changing operations that act on single array elements into vector operations that act on multiple array values in one instruction.Ĭonsider the following loop of a vector-vector add: # Assume a, b, and c represent array base addresses in memory suchĬompare this with the following loop: four: int = const 4 Modern processors have support for SIMD instructions, which allow for efficient vector operations. The input valArray can be of any MATLAB data type, including a string, cell array, or struct.CS 6120: Loop-Level Automatic Vectorization CS 6120 The loop executes for a maximum of n times, where n is the number of columns of valArray, given by numel(valArray, 1, :). For example, on the first iteration, index = valArray(:,1).
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Increments index by the value step on each iteration, or decrements when step is negative.Ĭreates a column vector index from subsequent columns of array valArray on each iteration. Increments the index variable from initval to endval by 1, and repeats execution of program statements until index is greater than endval. Values has one of the following forms − Sr.No. A for loop is a repetition control structure that allows you to efficiently write a loop that needs to execute a specific number of times.