Dynamic translation of structured Loads/Stores and register mapping for architectures with SIMD extensions.
Sheng-Yu Fu, Ding-Yong Hong, Yu-Ping Liu, Jan-Jan Wu, Wei-Chung Hsu

Conference

Venue

LCTES 2017

Abstract

More and more modern processors have been supporting non-contiguous SIMD data accesses. However, translating such instructions has been overlooked in the Dynamic Binary Translation (DBT) area. For example, in the popular QEMU dynamic binary translator, guest memory instructions with strides are emulated by a sequence of scalar instructions, leaving a significant room for performance improvement when the host machines have SIMD instructions available. Structured loads/stores, such as VLDn/VSTn in ARM NEON, are one type of strided SIMD data access instructions. They are widely used in signal processing, multimedia, mathematical and 2D matrix transposition applications. Efficient translation of such structured loads/stores is a critical issue when migrating ARM executables to other ISAs. However, it is quite challenging since not only the translation of structured loads/stores is not trivial, but also the difference between guest and host register configurations must be taken into consideration. In this work, we present the design and implementation of translating structured loads/stores in DBT, including target code generation as well as efficient SIMD register mapping. Our proposed register mapping mechanisms are not limited to handling structured loads/stores, they can be extended to deal with normal SIMD instructions. On a set of OpenCV benchmarks, our QEMU-based system has achieved a maximum speedup of 5.41x, with an average improvement of 2.93x. On a set of BLAS benchmarks, our system has also obtained a maximum speedup of 2.19x and an average improvement of 1.63x.

Author Links

1. 傅勝余
PhD student
3. 劉聿平
Master student
5. 徐慰中
Advisor

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Sheng-Yu Fu, Ding-Yong Hong, Yu-Ping Liu, Jan-Jan Wu, Wei-Chung Hsu:
Dynamic translation of structured Loads/Stores and register mapping for architectures with SIMD extensions. LCTES 2017
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