Parallelizing a Machine Translation Decoder for Multicore Computer
Machine translation (MT), with its broad potential use, has gained increased attention from both researchers and software vendors. To generate high quality translations, however, MT decoders can be highly computation intensive. With significant raw computing power, multi-core microprocessors have the potential to speed up MT software on desktop machines. However, retrofitting existing MT decoders is a nontrivial issue. Race conditions and atomicity issues are among those complications making parallelization difficult. In this article, we show that, to parallelize a state-of-the-art MT decoder, it is much easier to overcome such difficulties by using a process-based parallelization method, called functional task parallelism, than using conventional thread-based methods. We achieve a 7.60 times speed up on an 8-core desktop machine while making significantly less changes to the original sequential code than required by using multiple threads.
Long Chen Wei Huo Haitao Mi Zhaoqing Zhang Xiaobing Feng Zhiyuan Li
Key Laboratory of Computer System and Architecture,Institute of Computing Technology, Chinese Academ Department of Computer Science,Purdue University West Lafayette, US, IN 47907
国际会议
2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)
上海
英文
2267-2272
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）