Function merging is an important optimization for reducing code size. This technique eliminates redundant code across functions by merging them into a single function. While initially limited to identical or trivially similar functions, the most recent approach can identify all merging opportunities in arbitrary pairs of functions. However, this approach has a serious limitation which prevents it from reaching its full potential. Because it cannot handle phi-nodes, the state-of-the-art applies register demotion to eliminate them before applying its core algorithm. While a superficially minor workaround, this has a three-fold negative effect: by artificially lengthening the instruction sequences to be aligned, it hinders the identification of mergeable instruction; it prevents a vast number of functions from being profitably merged; it increases compilation overheads, both in terms of compile-time and memory usage.
We present SalSSA, a novel approach that fully supports the SSA form, removing any need for register demotion. By doing so, we notably increase the number of profitably merged functions. We implement SalSSA in LLVM and apply it to the SPEC 2006 and 2017 suites. Experimental results show that our approach delivers on average, 7.9% to 9.7% reduction on the final size of the compiled code. This translates to around 2x more code size reduction over the state-of-the-art. Moreover, as a result of aligning shorter sequences of instructions and reducing the number of wasteful merge operations, our new approach incurs an average compile-time overhead of only 5%, 3x less than the state-of-the-art, while also reducing memory usage by over 2x.
Thu 18 JunDisplayed time zone: Pacific Time (US & Canada) change
13:00 - 14:00 | Code GenerationPLDI Research Papers at PLDI Research Papers live stream Chair(s): Fan Long University of Toronto | ||
13:00 20mTalk | Automatic Generation of Efficient Sparse Tensor Format Conversion Routines PLDI Research Papers Stephen Chou Massachusetts Institute of Technology, USA, Fredrik Kjolstad Stanford University, Saman Amarasinghe Massachusetts Institute of Technology, USA Pre-print | ||
13:20 20mTalk | OOElala: Order-of-Evaluation Based Alias Analysis for Compiler Optimization PLDI Research Papers Ankush Phulia IIT Delhi, India, Vaibhav Bhagee IIT Delhi, India, Sorav Bansal IIT Delhi and CompilerAI Labs | ||
13:40 20mTalk | Effective Function Merging in the SSA Form PLDI Research Papers Rodrigo C. O. Rocha University of Edinburgh, UK, Pavlos Petoumenos University of Manchester, UK, Zheng Wang University of Leeds, UK, Murray Cole University of Edinburgh, UK, Hugh Leather University of Edinburgh, UK |