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| Optimization of thread partitioning parameters in speculative multithreading based on artificial immune algorithm |
| Yu-xiang Li, Yin-liang Zhao, Bin Liu, Shuo Ji |
| Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China |
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Abstract Thread partition plays an important role in speculative multithreading (SpMT) for automatic parallelization of irregular programs. Using unified values of partition parameters to partition different applications leads to the fact that every application cannot own its optimal partition scheme. In this paper, five parameters affecting thread partition are extracted from heuristic rules. They are the dependence threshold (DT), lower limit of thread size (TSL), upper limit of thread size (TSU), lower limit of spawning distance (SDL), and upper limit of spawning distance (SDU). Their ranges are determined in accordance with heuristic rules, and their step-sizes are set empirically. Under the condition of setting speedup as an objective function, all combinations of five threshold values form the solution space, and our aim is to search for the best combination to obtain the best thread granularity, thread dependence, and spawning distance, so that every application has its best partition scheme. The issue can be attributed to a single objective optimization problem. We use the artificial immune algorithm (AIA) to search for the optimal solution. On Prophet, which is a generic SpMT processor to evaluate the performance of multithreaded programs, Olden benchmarks are used to implement the process. Experiments show that we can obtain the optimal parameter values for every benchmark, and Olden benchmarks partitioned with the optimized parameter values deliver a performance improvement of 3.00% on a 4-core platform compared with a machine learning based approach, and 8.92% compared with a heuristics-based approach.
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Received: 13 May 2014
Published: 04 March 2015
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基于人工免疫算法的推测多线程线程划分参数的优化
目的：用人工免疫算法优化线程划分过程的主要影响因素，使不同应用获得最优划分方案。
创新点：将智能算法应用到推测多线程技术，实现该技术在线程划分过程中的优化。
方法：首先，根据启发式规则提取影响线程划分的五个参数，分别是DT，TSL，TSU，SDL，SDU。五个参数根据启发式规则确定取值范围，步长变化是随机的。将加速比设定为目标，五个参数变化形成解空间，优化目标是在解空间中寻找最优解（图6），即找出各个应用最优的划分策略。利用人工免疫算法搜索解空间，找到最优解（表4）。
结论：针对Olden测试集中每个测试函数获得最优划分参数值（图10-20），测试集中的函数在四核平台上的测试性能较之机器学习方法线程划分算法提高3.00%，较之启发式规则线程划分方法性提高8.92%。
关键词：
推测多线程,
线程划分,
人工免疫算法
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