An empirical study of data decomposition for software parallelization

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• 作者：Anne Meade ; ^{anne.meade@gmail.com} ; Deva Kumar Deeptimahanti ^{devabtechit@gmail.com} ; Jim Buckley ^{jim.buckley@ul.ie} ; J.J. Collins ^{j.j.collins@ul.ie}
• 关键词：Data decomposition ; Parallelization ; Empirical studies
• 刊名：Journal of Systems and Software
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：125
• 期：Complete
• 页码：401-416
• 全文大小：1999 K
• 卷排序：125

文摘

Multi-core programming is becoming increasingly important. Data decomposition is a key challenge during parallelization for multi-core CPUs. We conduct a multi-method study to better understand data decomposition. We derive a set of 10 key requirements for tools to support parallelization. The state-of-the-art tooling support does not support these requirements.