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Loop interchange is the process of swapping the order of nested loops. It aims to improve data locality and cache utilization by changing the traversal order of multidimensional arrays. It's typically applied when accessing memory in a more cache-friendly manner can improve performance.

Software pipelining is a technique to reorder instructions to better utilize CPU resources by overlapping the execution of multiple iterations of a loop. It is applied in loops with independent iterations to minimize idle CPU cycles and improve instruction-level parallelism.

Induction variable simplification involves finding and optimizing calculations using induction variables, which change by a fixed amount on each loop iteration. It reduces overhead by simplifying or even eliminating induction variables. It's applied in loops to streamline the computation and reduce the number of arithmetic operations.

Loop fusion, also known as loop jamming, combines two adjacent loops that iterate over the same range into a single loop. It is used to decrease loop overhead and improve cache performance by accessing data more efficiently. It is most effective when both loops perform unrelated computations on the same dataset.

Loop invariant code motion optimizes loops by moving instructions that compute the same value in each iteration outside the loop. This reduces the overall workload of the loop. It's applied when there are calculations within a loop that produce the same result every iteration.

Loop tiling, also known as loop blocking, divides loops into smaller blocks or 'tiles.' This technique can improve cache performance by working on smaller chunks of data that fit better into the cache. It is applied to large loops that operate on large data sets which do not fit well into cache.

Loop unrolling is a technique that attempts to improve the performance of a loop by reducing the overhead of branch instructions. By replicating the loop body multiple times, the number of iterations is decreased, thus reducing the number of jumps. It is applied when loop iteration overhead is significant compared to the loop body execution.

Loop fission, or loop distribution, is the process of splitting a loop into multiple separate loops, each handling part of the work. It can improve performance by isolating problematic dependencies within a loop. It is applied when different iterations of the loop are independent and can be separated without changing the outcome.

Loop peeling involves removing one or several iterations from the beginning or end of a loop and handling them separately. This can be useful to simplify the loop conditions or to deal with edge cases. It is applied when a few iterations require different handling than the main body of the loop.