As microprocessors evolve into heterogeneous architectures with multi-cores of MPUs and GPUs, programming model supports become important for programming such architectures. To address this issue, OpenCL is proposed. Currently, most of OpenCL implementations take LLVM as their infrastructures. This presents an opportunity to demonstrate whether OpenCL can be effectively implemented on other compiler infrastructures. For example, Open64, which is another open source compiler and known to generate efficient codes for microprocessors, can contribute further to performance improvements and enhancing the adoption of heterogeneous computing based on OpenCL. In this paper, we describe the flow to enable an OpenCL compiler based on Open64 infrastructures for ATI GPUs. Our work includes the extension of the front-end parser for OpenCL, the generation of high-level intermediate representations with OpenCL linguistics, performing high-level optimization, and finally applying OpenCL specific optimization for code generations. Preliminary experimental results show that our compiler based on Open64 is able to generate efficient codes for OpenCL programs.
Multithread programming is widely adopted in novel embedded system applications due to its high performance and flexibility. This article addresses compiler optimization for reducing the power consumption of multithread programs. A traditional compiler employs energy management techniques that analyze component usage in control-flow graphs with a focus on single-thread programs. In this environment the leakage power can be controlled by inserting on and off instructions based on component usage information generated by flow equations. However, these methods cannot be directly extended to a multithread environment due to concurrent execution issues.This article presents a multithread power-gating framework composed of multithread power-gating analysis (MTPGA) and predicated power-gating (PPG) energy management mechanisms for reducing the leakage power when executing multithread programs on simultaneous multithreading (SMT) machines. Our multithread programming model is based on hierarchical bulk-synchronous parallel (BSP) models. Based on a multithread component analysis with dataflow equations, our MTPGA framework estimates the energy usage of multithread programs and inserts PPG operations as power controls for energy management. We performed experiments by incorporating our power optimization framework into SUIF compiler tools and by simulating the energy consumption with a post-estimated SMT simulator based on Wattch toolkits. The experimental results show that the total energy consumption of a system with PPG support and our power optimization method is reduced by an average of 10.09% for BSP programs relative to a system without a power-gating mechanism on leakage contribution set to 30%; and the total energy consumption is reduced by an average of 4.27% on leakage contribution set to 10%. The results demonstrate our mechanisms are effective in reducing the leakage energy of BSP multithread programs.
Minimization of power dissipation can be considered at algorithmic, compiler, architectural, logic, and circuit level. Recent research trends for multicore programming models have come to the direction that parallel design patterns can be a solution to develop multicore applications. As parallel design patterns are with regularity, we view this as a great opportunity to exploit power optimizations in the software layer. In this paper, we investigate compilers for low power with parallel design patterns on embedded multicore systems. We evaluate four major parallel design patterns, Pipe and Filter, MapReduce with Iterator, Puppeteer, and Bulk Synchronous Parallel (BSP) Model. Our work attempts to devise power optimization schemes in compilers by exploiting the opportunities of the recurring patterns of embedded multicore programs. The proposed optimization schemes are rate-based optimization for Pipe and Filter pattern , early-exit power optimization for MapReduce with Iterator pattern, power aware mapping algorithm for Puppeteer pattern, and multi-phases power gating scheme for BSP pattern. In our experiments, real world multicore applications are evaluated on a multicore power simulator. Significant power reductions are observed from the experimental results. Therefore, we present a direction for power optimizations that one can further
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