Register allocation and spilling using the expected distance heuristic. (7th February 2016)

• Record Type: Journal Article
• Authors: Burroughs, Neil
• Is Part Of: Software, practice & experience. Volume 46:Number 11(2016)
• Page Start: 1499
• Page End: 1523
• Publication Date: 2016-02-07
• Subjects: register allocation -- Belady MIN algorithm -- spilling -- minimum‐cut -- graph contraction; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2393
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: The primary goal of the register allocation phase in a compiler is to minimize register spills to memory. Spill decisions by the allocator are often made based on the costs of spilling a virtual register and, therefore, on an assumed placement of spill instructions. However, because most allocators make these decisions incrementally, placement opportunities can change as allocation proceeds, calling into question the basis for the original spill decision. An alternative heuristic to placement costs for spill decisions focuses on where program execution will lead. Spilling the virtual register with the Furthest Next Use is known to lead to the minimum number of loads under certain conditions in straight‐line code. While it has been implemented in register allocation in different forms, none of these implementations fully exploits profiling information. We present a register allocator that can adapt to improved profiling information, using branch probabilities to compute an Expected Distance to Next Use for making spill decisions and block frequency information to optimize post‐allocation spill instruction placement. Spill placement is optimized after allocation using a novel method for minimizing spill instruction costs on the control flow graph. Our evaluation of the allocator compared with LLVM recognizes more than 36% and 50% reductions, on average, in the number of dynamically executed store and load instructions, respectively, when using statically derived profiling information. When using dynamically gathered profiling, these improvements increase to 50% and 60% reductions, on average, for stores and loads, respectively. Copyright © 2016 John Wiley & Sons, Ltd.