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Intel’s Array Building Blocks: A retargetable, dynamic compiler and embedded language

Chris J. Newburn, Byoungro So, Zhenying Liu, Michael McCool, Anwar Ghuloum, Stefanus Du Toit, Zhi Gang Wang, Zhao Hui Du, Yongjian Chen, Gansha Wu, Peng Guo, Zhanglin Liu and Dan Zhang
Performance and Productivity Libraries, Software and Services Group, Intel Corporation
9th Annual IEEE/ACM International Symposium on Code Generation and Optimization (CGO), 2011

@inproceedings{newburn2011intel,

   title={Intel’s Array Building Blocks: A retargetable, dynamic compiler and embedded language},

   author={Newburn, C.J. and So, B. and Liu, Z. and McCool, M. and Ghuloum, A. and Toit, S.D. and Wang, Z.G. and Du, Z.H. and Chen, Y. and Wu, G. and others},

   booktitle={Code Generation and Optimization (CGO), 2011 9th Annual IEEE/ACM International Symposium on},

   pages={224–235},

   year={2011},

   organization={IEEE}

}

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Our ability to create systems with large amount of hardware parallelism is exceeding the average software developer’s ability to effectively program them. This is a problem that plagues our industry. Since the vast majority of the world’s software developers are not parallel programming experts, making it easy to write, port, and debug applications with sufficient core and vector parallelism is essential to enabling the use of multi- and many-core processor architectures. However, hardware architectures and vector ISAs are also shifting and diversifying quickly, making it difficult for a single binary to run well on all possible targets. Because of this, retargetability and dynamic compilation are of growing relevance. This paper introduces Intel Array Building Blocks (ArBB), which is a retargetable dynamic compilation framework. This system focuses on making it easier to write and port programs so that they can harvest data and thread parallelism on both multi-core and heterogeneous many-core architectures, while staying within standard C++. ArBB interoperates with other programming models to help meet the demands we hear from customers for a solution with both greater programmer productivity and good performance. This work makes contributions in language features, compiler architecture, code transformations and optimizations. It presents performance data from the current beta release of ArBB and quantitatively shows the impact of some key analyses, enabling transformations and optimizations for a variety of benchmarks that are of interest to our customers.
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