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This divides and conquers a large memory address space by cutting it into little pieces. Which loop transformation can increase the code size? If we are writing an out-of-core solution, the trick is to group memory references together so that they are localized. The good news is that we can easily interchange the loops; each iteration is independent of every other: After interchange, A, B, and C are referenced with the leftmost subscript varying most quickly. Typically loop unrolling is performed as part of the normal compiler optimizations. For example, given the following code: Don't do that now! You can also experiment with compiler options that control loop optimizations. Say that you have a doubly nested loop and that the inner loop trip count is low perhaps 4 or 5 on average. The line holds the values taken from a handful of neighboring memory locations, including the one that caused the cache miss. By interchanging the loops, you update one quantity at a time, across all of the points. Code duplication could be avoided by writing the two parts together as in Duff's device. That would give us outer and inner loop unrolling at the same time: We could even unroll the i loop too, leaving eight copies of the loop innards. A 3:1 ratio of memory references to floating-point operations suggests that we can hope for no more than 1/3 peak floating-point performance from the loop unless we have more than one path to memory. Most codes with software-managed, out-of-core solutions have adjustments; you can tell the program how much memory it has to work with, and it takes care of the rest. When unrolled, it looks like this: You can see the recursion still exists in the I loop, but we have succeeded in finding lots of work to do anyway. Loop unrolling - Wikipedia Look at the assembly language created by the compiler to see what its approach is at the highest level of optimization. In addition, the loop control variables and number of operations inside the unrolled loop structure have to be chosen carefully so that the result is indeed the same as in the original code (assuming this is a later optimization on already working code). Number of parallel matches computed. Check OK to move the S.D after DSUBUI and BNEZ, and find amount to adjust S.D offset 2. : numactl --interleave=all runcpu <etc> To limit dirty cache to 8% of memory, 'sysctl -w vm.dirty_ratio=8' run as root. Of course, operation counting doesnt guarantee that the compiler will generate an efficient representation of a loop.1 But it generally provides enough insight to the loop to direct tuning efforts. PDF ROOM L130 Lecture 8: Dependences and Locality Optimizations