As described earlier, conditional execution can replace a branch and an operation with a single conditionally executed assignment. In the matrix multiplication code, we encountered a non-unit stride and were able to eliminate it with a quick interchange of the loops. The question is, then: how can we restructure memory access patterns for the best performance? In nearly all high performance applications, loops are where the majority of the execution time is spent. If you are faced with a loop nest, one simple approach is to unroll the inner loop. c. [40 pts] Assume a single-issue pipeline. To ensure your loop is optimized use unsigned type for loop counter instead of signed type. When the compiler performs automatic parallel optimization, it prefers to run the outermost loop in parallel to minimize overhead and unroll the innermost loop to make best use of a superscalar or vector processor. Once youve exhausted the options of keeping the code looking clean, and if you still need more performance, resort to hand-modifying to the code. This modification can make an important difference in performance. This usually occurs naturally as a side effect of partitioning, say, a matrix factorization into groups of columns. The loop or loops in the center are called the inner loops. Lets illustrate with an example. The surrounding loops are called outer loops. You just pretend the rest of the loop nest doesnt exist and approach it in the nor- mal way. times an d averaged the results. For instance, suppose you had the following loop: Because NITER is hardwired to 3, you can safely unroll to a depth of 3 without worrying about a preconditioning loop. While there are several types of loops, . We look at a number of different loop optimization techniques, including: Someday, it may be possible for a compiler to perform all these loop optimizations automatically. The iterations could be executed in any order, and the loop innards were small. 863 count = UP. Optimizing C code with loop unrolling/code motion. The best pattern is the most straightforward: increasing and unit sequential. We also acknowledge previous National Science Foundation support under grant numbers 1246120, 1525057, and 1413739. In this research we are interested in the minimal loop unrolling factor which allows a periodic register allocation for software pipelined loops (without inserting spill or move operations). Others perform better with them interchanged. [4], Loop unrolling is also part of certain formal verification techniques, in particular bounded model checking.[5]. Blocking references the way we did in the previous section also corrals memory references together so you can treat them as memory pages. Knowing when to ship them off to disk entails being closely involved with what the program is doing. In this section we are going to discuss a few categories of loops that are generally not prime candidates for unrolling, and give you some ideas of what you can do about them. The transformation can be undertaken manually by the programmer or by an optimizing compiler. In this example, approximately 202 instructions would be required with a "conventional" loop (50 iterations), whereas the above dynamic code would require only about 89 instructions (or a saving of approximately 56%). A programmer who has just finished reading a linear algebra textbook would probably write matrix multiply as it appears in the example below: The problem with this loop is that the A(I,K) will be non-unit stride. Say that you have a doubly nested loop and that the inner loop trip count is low perhaps 4 or 5 on average. On a superscalar processor with conditional execution, this unrolled loop executes quite nicely. BFS queue, DFS stack, Dijkstra's algorithm min-priority queue). Loop interchange is a technique for rearranging a loop nest so that the right stuff is at the center. Of course, you cant eliminate memory references; programs have to get to their data one way or another. We make this happen by combining inner and outer loop unrolling: Use your imagination so we can show why this helps. An Aggressive Approach to Loop Unrolling . Recall how a data cache works.5 Your program makes a memory reference; if the data is in the cache, it gets returned immediately. Loop unrolling helps performance because it fattens up a loop with more calculations per iteration. One way is using the HLS pragma as follows: You will need to use the same change as in the previous question. If i = n, you're done. Using an unroll factor of 4 out- performs a factor of 8 and 16 for small input sizes, whereas when a factor of 16 is used we can see that performance im- proves as the input size increases . */, /* Note that this number is a 'constant constant' reflecting the code below. Why is an unrolling amount of three or four iterations generally sufficient for simple vector loops on a RISC processor? On platforms without vectors, graceful degradation will yield code competitive with manually-unrolled loops, where the unroll factor is the number of lanes in the selected vector. The first goal with loops is to express them as simply and clearly as possible (i.e., eliminates the clutter). I have this function. Unrolling also reduces the overall number of branches significantly and gives the processor more instructions between branches (i.e., it increases the size of the basic blocks). Say that you have a doubly nested loop and that the inner loop trip count is low perhaps 4 or 5 on average. The results sho w t hat a . Compile the main routine and BAZFAZ separately; adjust NTIMES so that the untuned run takes about one minute; and use the compilers default optimization level. The difference is in the way the processor handles updates of main memory from cache. Above all, optimization work should be directed at the bottlenecks identified by the CUDA profiler. In [Section 2.3] we showed you how to eliminate certain types of branches, but of course, we couldnt get rid of them all. How do I achieve the theoretical maximum of 4 FLOPs per cycle? Stepping through the array with unit stride traces out the shape of a backwards N, repeated over and over, moving to the right. Illustration:Program 2 is more efficient than program 1 because in program 1 there is a need to check the value of i and increment the value of i every time round the loop. Instruction Level Parallelism and Dependencies 4. The number of copies inside loop body is called the loop unrolling factor. Execute the program for a range of values for N. Graph the execution time divided by N3 for values of N ranging from 5050 to 500500. Second, when the calling routine and the subroutine are compiled separately, its impossible for the compiler to intermix instructions. See comments for why data dependency is the main bottleneck in this example. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above. A loop that is unrolled into a series of function calls behaves much like the original loop, before unrolling. Often you find some mix of variables with unit and non-unit strides, in which case interchanging the loops moves the damage around, but doesnt make it go away. Basic Pipeline Scheduling 3. In most cases, the store is to a line that is already in the in the cache. There are some complicated array index expressions, but these will probably be simplified by the compiler and executed in the same cycle as the memory and floating-point operations. To produce the optimal benefit, no variables should be specified in the unrolled code that require pointer arithmetic. 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). There are several reasons. Picture how the loop will traverse them. Processors on the market today can generally issue some combination of one to four operations per clock cycle. The loop below contains one floating-point addition and two memory operations a load and a store. Loop unrolling increases the programs speed by eliminating loop control instruction and loop test instructions. Bf matcher takes the descriptor of one feature in first set and is matched with all other features in second set and the closest one is returned. Given the following vector sum, how can we rearrange the loop? The general rule when dealing with procedures is to first try to eliminate them in the remove clutter phase, and when this has been done, check to see if unrolling gives an additional performance improvement. 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. The line holds the values taken from a handful of neighboring memory locations, including the one that caused the cache miss. A determining factor for the unroll is to be able to calculate the trip count at compile time. It must be placed immediately before a for, while or do loop or a #pragma GCC ivdep, and applies only to the loop that follows. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. As you contemplate making manual changes, look carefully at which of these optimizations can be done by the compiler. However, there are times when you want to apply loop unrolling not just to the inner loop, but to outer loops as well or perhaps only to the outer loops. You should also keep the original (simple) version of the code for testing on new architectures. Manual loop unrolling hinders other compiler optimization; manually unrolled loops are more difficult for the compiler to analyze and the resulting code can actually be slower. To learn more, see our tips on writing great answers. First, they often contain a fair number of instructions already. [1], The goal of loop unwinding is to increase a program's speed by reducing or eliminating instructions that control the loop, such as pointer arithmetic and "end of loop" tests on each iteration;[2] reducing branch penalties; as well as hiding latencies, including the delay in reading data from memory. They work very well for loop nests like the one we have been looking at. Further, recursion really only fits with DFS, but BFS is quite a central/important idea too. 861 // As we'll create fixup loop, do the type of unrolling only if. Sometimes the compiler is clever enough to generate the faster versions of the loops, and other times we have to do some rewriting of the loops ourselves to help the compiler. Can Martian regolith be easily melted with microwaves? You can also experiment with compiler options that control loop optimizations. Unrolls this loop by the specified unroll factor or its trip count, whichever is lower. Array A is referenced in several strips side by side, from top to bottom, while B is referenced in several strips side by side, from left to right (see [Figure 3], bottom). Hi all, When I synthesize the following code , with loop unrolling, HLS tool takes too long to synthesize and I am getting " Performing if-conversion on hyperblock from (.gphoto/cnn.cpp:64:45) to (.gphoto/cnn.cpp:68:2) in function 'conv'. Again, operation counting is a simple way to estimate how well the requirements of a loop will map onto the capabilities of the machine. Connect and share knowledge within a single location that is structured and easy to search. Introduction 2. A-143, 9th Floor, Sovereign Corporate Tower, We use cookies to ensure you have the best browsing experience on our website. However, synthesis stops with following error: ERROR: [XFORM 203-504] Stop unrolling loop 'Loop-1' in function 'func_m' because it may cause large runtime and excessive memory usage due to increase in code size. It is important to make sure the adjustment is set correctly. Well show you such a method in [Section 2.4.9]. The degree to which unrolling is beneficial, known as the unroll factor, depends on the available execution resources of the microarchitecture and the execution latency of paired AESE/AESMC operations. We basically remove or reduce iterations. If statements in loop are not dependent on each other, they can be executed in parallel. This is exactly what we accomplished by unrolling both the inner and outer loops, as in the following example. The worst-case patterns are those that jump through memory, especially a large amount of memory, and particularly those that do so without apparent rhyme or reason (viewed from the outside). Outer Loop Unrolling to Expose Computations. Afterwards, only 20% of the jumps and conditional branches need to be taken, and represents, over many iterations, a potentially significant decrease in the loop administration overhead. On some compilers it is also better to make loop counter decrement and make termination condition as . It is, of course, perfectly possible to generate the above code "inline" using a single assembler macro statement, specifying just four or five operands (or alternatively, make it into a library subroutine, accessed by a simple call, passing a list of parameters), making the optimization readily accessible. So what happens in partial unrolls? The code below omits the loop initializations: Note that the size of one element of the arrays (a double) is 8 bytes.