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MICRO2022顶会

Speculative Code Compaction: Eliminating Dead Code via Speculative Microcode Transformations

Logan Moody, Wei Qi, Abdolrasoul Sharifi, Layne Berry, Joey Rudek, Jayesh Gaur, Jeff Parkhurst, Sreenivas Subramoney, Kevin Skadron, Ashish Venkat

2022年份
3被引次数
2顶会引用

摘要

The computing landscape has been increasingly characterized by processor architectures with increasing core counts, while a majority of the software applications remain inherently sequential. Although state-of-the-art compilers feature sophisticated optimizations, a significant chunk of wasteful computation persists due to the presence of data-dependent operations and irregular control-flow patterns that are unpredictable at compile-time. This work presents speculative code compaction (SCC), a novel microarchitectural technique that significantly enhances the capabilities of the microcode engine to aggressively and speculatively eliminate dead code from hot code regions resident in the micro-op cache, and further generate a compact stream of micro-ops, based on dynamically predicted machine code invariants. SCC also extends existing micro-op cache designs to co-host multiple versions of unoptimized and speculatively optimized micro-op sequences, providing the fetch engine with significant flexibility to dynamically choose from and stream the appropriate set of micro-ops, as and when deemed profitable.SCC is a minimally-invasive technique that can be implemented at the processor front-end using a simple ALU and a register context table, and is yet able to substantially accelerate the performance of already compile-time optimized and machine-tuned code by an average of 6% (and as much as 30%), with an average of 12% (and as much as 24%) savings in energy consumption, while eliminating the need for profiling and offering increased adaptability to changing datasets and workload patterns.

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