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Learning to Unroll Loops Optimally S¸tefan Ciobâcă LSV, Ecole Normale Supérieure Cachan and CNRS 94235 Cachan, France ciobaca@lsv.ens-cachan.fr Liviu Ciortuz Department of Computer Science “Al. I. Cuza” University of Iasi 700483 Iasi, Romania ciortuz@<strong>info</strong>.<strong>uaic</strong>.ro Abstract Every few months new types of hardware are released. Compiler writers face the challenging task of keeping the compiler optimizations up-to-date with the latest in hardware technology. In this paper we apply machine learning techniques to predict the best unroll factors for loops, using GCC and the x86 architecture for our experiments. We show that, depending on the machine learning tools used, we get similar or slightly better performance than the native GCC loop optimizer. Our result shows that machine learning techniques can be used to automatically train the heuristic that computes the unroll factor for loops, saving time for compiler manufacturers and providing better performance in the case of scientific computations. 1 Introduction Compiler writers develop heuristics to solve the problems that occur during the optimization phases of the compilation process. Finding a heuristic for such a problem is difficult and time consuming for the compiler manufacturer, especially when the compiler must work just as well on a number of different architectures. Common optimization goals include minimizing code size, maximizing speed and minimizing power consumption. Optimization is highly dependent on the target architecture of the system. What works well on the Itanium might not work so well on the x86. Among the most commonly used optimizations are: • function inlining consists of replacing the call to a function by the body of the function itself. This usually leads to bigger code, but, depending on the archi- 1
tecture of the system and if the function is sufficiently small, performance is generally better. • local instruction scheduling consists of permuting instructions in a basic block (a sequence of instructions with one entry point and one exit point) in order to improve pipelining. • basic block reordering consists of rearranging basic blocks in order to reduce the branch cost and instruction cache misses. • loop unrolling consists of replacing the body of a loop by multiple copies of itself, while reducing the number of times the loop is executed. This increases locality and reduces the number of jumps. The number of times the loop body is copied is called the unroll factor. All of the above optimizations were attacked by using tools such as machine learning or genetic algorithms. In [3], Cavazos and O’Boyle use a genetic algorithm to automatically tune the inlining heuristic of a dynamic compiler. In [2], Cavazos uses machine learning techniques to build flexible instructions schedulers. In [5], Liu et al. use neural networks to reorder basic blocks. In [4], Stephenson and Amarasinghe show how to predict unroll factors using supervised classification. However, very few articles describe experiments on a mainstream architecture/compiler combination, preferring for example the Itanium architecture or even simulators [2] of other architectures. In this paper, we concentrate on using machine learning techniques to predict the best loop unrolling factors. We use the GCC [6] compiler (version 4.1.2), an x86 machine and the SciMark2 scientific benchmark [7]. In section 2, we present the methodology used to gather experimental data and the algorithms used to learn the best unroll factor. Section 3 presents the timing data we obtained from the experiments. In section 4 we check how the data we gathered from the SciMark2 benchmark generalizes to a different benchmark [8] for a different language: Fortran. This was possible within our existing experimental framework because of the modular architecture of GCC (loops are unrolled at the same place, both for C and Fortran). Section 5 concludes the paper and gives possible directions for future research. 2 Methodology and Benchmark The SciMark2 benchmark was originally designed for testing the performance of different implementations of the Java virtual machine. However, the benchmark has been translated to ANSI C, and can be compiled using GCC. We chose the SciMark2 benchmark because it is freely available, fully self-contained, very easy to compile and it contains scientific code, which is amenable to loop unrolling. There are five computational kernels in SciMark2: • Fast Fourier Transform (FFT) performs a one-dimensional forward transform of 4096 complex numbers. 2
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