S. VenkataKeerthy, Siddharth Jain, Anilava Kundu, Rohit Aggarwal, Albert Cohen and Ramakrishna Upadrasta
Register allocation is one of the well-studied and important compiler optimization problems. It involves assigning a finite set of registers to an unbounded set of variables. Its decision problem is reducible to graph coloring, which is one of the classical NP-Complete problems. Register allocation as an optimization involves additional sub-tasks, more than graph coloring itself. Several formulations have been proposed that return exact, or heuristic-based solutions.
In this work, we propose a retargetable Reinforcement Learning (RL) approach for solving the REgister ALlocation (REAL) problem on diverse architectures.
We formulate a multi-agent hierarchical reinforcement learning optimization considering program-specific information -
The legality of the register allocations and assignments is preserved by imposing constraints on the action space, or outcome of each agent. As register allocation is a complex combinatorial problem, establishing the ground truth is hard, making RL a natural choice. Also, it facilitates the imposition of correctness constraints.
The reinforcement learning model is end-to-end integrated with the LLVM compiler and the communication between the model and compiler is facilitated by a gRPC module effectively integrated with LLVM.
RL4ReAl achieves competitive results; our results match or out-perform the heavily tuned, production grade allocators of LLVM on standard SPEC CPU benchmark suites.
We evaluate performance on a complex x86 (Intel Xeon SkyLake W2133, 6 cores, 32GB RAM), and a simpler mobile AArch64 (ARM Cortex A72, 2 cores, 8GB RAM) processors. We consider allocations of general purpose, vector, floating point registers for both x86 and AArch64 architectures.
RL4ReAl opens up new opportunities for research on regalloc and on other backend compilation problems.
TBD Soon to be open-sourced; Artifacts will be available in our GitHub page.
This research is partially funded by a Google PhD fellowship, an NSM research grant (MeitY/R&D/HPC/2(1)/2014), and a faculty research grant from AMD.