An Implementation of Loop Fusion Using Simple-SUIF

ACM Transactions on Programming Languages and Systems, 2005

As shared-memory multiprocessor systems become widely available, there is an increasing need for tools to simplify the task of developing parallel programs. This paper describes one such tool, the automatic parallelization system in the Stanford SUIF compiler. This article represents a culmination of a several-year research effort aimed at making parallelizing compilers significantly more effective. We have developed a system that performs full interprocedural parallelization analyses, including array privatization analysis, array reduction recognition, and a suite of scalar dataflow analyses including symbolic analysis. These analyses collaborate in an integrated fashion to exploit coarse-grain parallel loops, computationally intensive loops that can execute on multiple processors independently with no cross-processor synchronization or communication. The system has successfully parallelized large interprocedural loops over a thousand lines of code completely automatically from sequential applications. This article provides a comprehensive description of the analyses in the SUIF system. We also present extensive empirical results on four benchmark suites, showing the contribution of individual analysis techniques both in executing more of the computation in parallel, and in increasing the granularity of the parallel computations. These results demonstrate the importance of

Proceedings of the 12th international conference on Supercomputing, 1998

This paper presents both an experiment and a system for inserting run-time dependence and privatization testing. The goal of the experiment is to measure empirically the remaining opportunities for exploiting loop-level parallelism that are missed by state-of-theart parallelizing compiler technology. We perform run-time testing of data accessed within all candidate loops not parallelized by the compiler to identify which of these loops could safely execute in parallel for the given program input. This system extends the Lazy Privatizing Doall (LPD) test to simultaneously instrument multiple loops in a nest. Using the results of interprocedural array dataflow analysis, we avoid unnecessary instrumentation of arrays with compile-time provable dependences or loops nested inside outer parallelized loops. We have implemented the system in the Stanford SUIF compiler and have measured programs in three benchmark suites.

1995

Proceedings of the 2nd IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis - CODES+ISSS '04, 2004

The memory bandwidth largely determines the performance and energy cost of embedded systems. At the compiler level, several techniques improve the memory bandwidth at the scope of a basic block, but often fail to exploit all. We propose a technique to optimize the memory bandwidth across the boundaries of a basic block. Our technique incrementally fuses loops to better use the available bandwidth. The resulting performance depends on how the data is assigned to the memories of the memory layer. At the same time, the assignment also strongly influences the energy cost. Therefore, we combine in our approach the fusion and assignment decisions. Designers can use our output to trade-off the energy cost with the system's performance.

Workshop on New Horizons in …, 2006

Abstract. Dynamic compilation is becoming a dominant compilation technique. Runtime compilation has to avoid slow compile times by targeting optimizations to areas where it has a performance impact. For parallelization optimizations this can lead to not exposing ...

ACM Transactions on Asian and Low-Resource Language Information Processing

The multi-core architecture has revolutionized the parallel computing. Despite this, the modern age compilers have a long way to achieve auto-parallelization. Through this paper, we introduce a language that encouraging the auto-parallelization. We are also introducing Front-End for our auto-parallelizing compiler. Later, we examined our compiler employing a different number of core and verify results based on different metrics based on total compilation time, memory utilization, power utilization and CPU utilization. At last, we learned that parallelizing multiple files engage more CPU resources, memory and energy, but it finishes the task at hand in less time. In this paper, we have proposed a loop code generation technique that makes the generation of nested loop IR code faster by dividing the blocks into some extra code blocks using a modular approach. Our TAM compiler technique speedup by 7.506, 5.283 and 2.509 against sequential compilation when we utilized 8, 4 and 2 cores re...