In Proceedings of the 18th International Conference on Languages, Compilers, Tools, and Theory for Embedded Systems (LCTES)
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.
Speedups on a single core, compared to non-vectorized baseline.
This work is supported by the German Research Foundation (DFG), as part of the Research Training Group 1773 “Heterogeneous Image Systems”, and as part of the Transregional Collaborative Research Center “Invasive Computing” (SFB/TR 89).