Loop optimization

Since instructions inside loops can be executed repeatedly, it is frequently not possible to give a bound on the number of instruction executions that will be impacted by a loop optimization. This presents challenges when reasoning about the correctness and benefits of a loop optimization, specifically the representations of the computation being optimized and the optimization(s) being performed.[1]

Loop optimization can be viewed as the application of a sequence of specific loop transformations (listed below or in Compiler transformations for high-performance computing[2]) to the source code or intermediate representation, with each transformation having an associated test for legality. A transformation (or sequence of transformations) generally must preserve the temporal sequence of all dependencies if it is to preserve the result of the program (i.e., be a legal transformation). Evaluating the benefit of a transformation or sequence of transformations can be quite difficult within this approach, as the application of one beneficial transformation may require the prior use of one or more other transformations that, by themselves, would result in reduced performance.

Common loop transformations include:

• Fission or distribution – loop fission attempts to break a loop into multiple loops over the same index range, but each new loop takes only part of the original loop's body. This can improve locality of reference, both of the data being accessed in the loop and the code in the loop's body.
• Fusion or combining – this combines the bodies of two adjacent loops that would iterate the same number of times (whether or not that number is known at compile time), as long as they make no reference to each other's data.
• Interchange or permutation – these optimizations exchange inner loops with outer loops. When the loop variables index into an array, such a transformation can improve locality of reference, depending on the array's layout.
• Inversion – this technique changes a standard while loop into a do/while (a.k.a. repeat/until ) loop wrapped in an if conditional, reducing the number of jumps by two for cases where the loop is executed. Doing so duplicates the condition check (increasing the size of the code) but is more efficient because jumps usually cause a pipeline stall. Additionally, if the initial condition is known at compile-time and is known to be side-effect-free, the initial if-guard can be skipped.
• Loop-invariant code motion – this can vastly improve efficiency by moving a computation from inside the loop to outside of it, computing a value just once before the loop begins, if the resultant quantity of the calculation will be the same for every loop iteration (i.e., a loop-invariant quantity). This is particularly important with address-calculation expressions generated by loops over arrays. For correct implementation, this technique must be used with inversion, because not all code is safe to be moved outside the loop.
• Parallelization – this is a special case of automatic parallelization focusing on loops, restructuring them to run efficiently on multiprocessor systems. It can be done automatically by compilers (automatic parallelization) or manually (inserting parallel directives like OpenMP).
• Reversal – a subtle optimization that reverses the order in which values are assigned to the index variable. This can help eliminate dependencies and thus enable other optimizations. Certain architectures utilize looping constructs at assembly level that count in a single direction only (e.g., decrement-jump-if-not-zero [DJNZ][3]).
• Scheduling – this divides a loop into multiple parts that may be run concurrently on multiple processors.
• Skewing – this technique is applied to a nested loop iterating over a multidimensional array, where each iteration of the inner loop depends on previous iterations, and rearranges its array accesses so that the only dependencies are between iterations of the outer loop.
• Software pipelining – a type of out-of-order execution of loop iterations to hide the latencies of processor function units.
• Splitting or peeling – this attempts to simplify a loop or eliminate dependencies by breaking it into multiple loops which have the same bodies but iterate over different portions of the index range. A special case is loop peeling, which can simplify a loop with a problematic first iteration by performing that iteration separately before entering the loop.
• Tiling or blocking – reorganizes a loop to iterate over blocks of data sized to fit in the cache.
• Vectorization – attempts to run as many of the loop iterations as possible at the same time on a SIMD system.
• Unrolling – duplicates the body of the loop multiple times, in order to decrease the number of times the loop condition is tested and the number of jumps, which may degrade performance by impairing the instruction pipeline. Completely unrolling a loop eliminates all overhead (except multiple instruction fetches and increased program load time), but requires that the number of iterations be known at compile time (except in the case of Just-in-time compilation). Care must also be taken to ensure that multiple re-calculation of indexed variables is not a greater overhead than advancing pointers within the original loop.
• Unswitching – moves a conditional from inside a loop to outside of it by duplicating the loop's body, and placing a version of it inside each of the if and else clauses of the conditional.
• Sectioning or strip-mining – introduced for vector processors, loop-sectioning is a loop-transformation technique for enabling SIMD (single instruction, multiple data)-encodings of loops and improving memory performance. This involves each vector operation being done for a size less-than or equal-to the maximum vector length on a given vector machine.[4][5]

• Loop nest optimization
• Polytope model
• Scalable parallelism
• Scalable locality