Efficient Code Generation in a Region-based Dynamic Binary Translator

Region-based JIT compilation operates on translation units comprising multiple basic blocks and, possibly cyclic or conditional, control flow between these. It promises to reconcile aggressive code optimisation and low compilation latency in performance-critical dynamic binary translators. Whilst various region selection schemes and isolated code optimisation techniques have been investigated it remains unclear how to best exploit such regions for efficient code generation. Complex interactions with indirect branch tables and translation caches can have adverse effects on performance if not considered carefully. In this paper we present a complete code generation strategy for a region-based dynamic binary translator, which exploits branch type and control flow profiling information to improve code quality for the common case. We demonstrate that using our code generation strategy a competitive region-based dynamic compiler can be built on top of the LLVM JIT compilation framework. For the ARM-V5T target ISA and SPEC CPU 2006 benchmarks we achieve execution rates of, on average, 867 MIPS and up to 1323 MIPS on a standard X86 host machine, outperforming state-of-the-art QEMU-ARM by delivering a speedup of 264%

Techniques and tools for dynamic optimization

Traditional code optimizers have produced significant performance improvements over the past forty years. While promising avenues of research still exist, traditional static and profiling techniques have reached the point of diminishing returns. The main problem is that these approaches have only a limited view of the program and have difficulty taking advantage of the actual run-time behavior of a program. We are addressing this problem through the development of a dynamic optimization system suited for aggressive optimization—using the full power of the most beneficial optimizations. We have designed our optimizer to operate using a software dynamic translation (SDT) execution system. Difficult challenges in this research include reducing SDT overhead and determining what optimizations to apply and where in the code to apply them. Another challenge is having the necessary tools to ensure the reliability of software that is dynamically optimized. In this paper, we describe our efforts in reducing overhead in SDT and efficient techniques for instrumenting the application code. We also describe our approach to determine what and where an optimization should be applied. We discuss other fundamental issues in developing a dynamic optimizer and finally present a basic debugger for SDT systems. 1