Conference contribution
(Conference Contribution)


Auto-vectorization for Image Processing DSLs


Publication Details
Author(s): Reiche O, Kobylko C, Hannig F, Teich J
Editor(s): ACM
Publication year: 2017
Conference Proceedings Title: Proceedings of the 18th International Conference on Languages, Compilers, Tools, and Theory for Embedded Systems (LCTES)
Pages range: 21 - 30

Event details
Event: 18th International Conference on Languages, Compilers, Tools, and Theory for Embedded Systems (LCTES)
Event location: Barcelona
Start date of the event: 21/06/2017
End date of the event: 22/06/2017
Language: English

Abstract
The parallelization of programs and distributing their workloads to multiple threads can be a challenging task. In addition to multi-threading, harnessing vector units in CPUs proves highly desirable. However, employing vector units to speed up programs can be quite tedious. Either a program developer solely relies on the auto-vectorization capabilities of the compiler or he manually applies vector intrinsics, which is extremely error-prone, difficult to maintain, and not portable at all. Based on whole-function vectorization, a method to replace control flow with data flow, we propose auto-vectorization techniques for image processing DSLs in the context of source-to-source compilation. The approach does not require the input to be available in SSA form. Moreover, we formulate constraints under which the vectorization analysis and code transformations may be greatly simplified in the context of image processing DSLs. As part of our methodology, we present control flow to data flow transformation as a source-to-source translation. Moreover, we propose a method to efficiently analyze algorithms with mixed bit-width data types to determine the optimal SIMD width, independently of the target instruction set. The techniques are integrated into an open source DSL framework. Subsequently, the vectorization capabilities are compared to a variety of existing state-of-the-art C/C++ compilers. A geometric mean speedup of up to 3.14 is observed for benchmarks taken from ISPC and image processing, compared to non-vectorized executions.


How to cite
APA: Reiche, O., Kobylko, C., Hannig, F., & Teich, J. (2017). Auto-vectorization for Image Processing DSLs. In ACM (Eds.), Proceedings of the 18th International Conference on Languages, Compilers, Tools, and Theory for Embedded Systems (LCTES) (pp. 21 - 30). Barcelona, ES.

MLA: Reiche, Oliver, et al. "Auto-vectorization for Image Processing DSLs." Proceedings of the 18th International Conference on Languages, Compilers, Tools, and Theory for Embedded Systems (LCTES), Barcelona Ed. ACM, 2017. 21 - 30.

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