Karl Taht's Research Paper Blog

Authors: Grant Ayers et al. Venue: ISCA 2019 Previous works have highlighted a significant frontend bottleneck is warehouse-scale computers (WSC). A variety of solutions have been proposed, both on real hardware and architectural papers, to mitigate the issue. This paper performs deep analysis across 90%+ of Google's entire fleet to perform a fine-grain analysis of when, where, and how frontend bottlenecks occur. AsmDB comprises of post-processed last branch record (LBR) data to form control flow probabilities and precise information about what instructions triggered I-cache misses. The analysis shows several core reasons for I-cache misses: large jump distances (either via function call or indirect branch) and cold code being brought into the cache (either via cache blocks or prefetching). The remainder of the paper transitions to being much more compiler focused. It describes a software prefetching algorithm which utilizes AsmDB information to inject prefetches at ideal poin...

Authors: Dehao Chen, David Xinliang Li, Tipp Moseley Venue:    CGO 2016 This paper presents AutoFDO, a system used which profiles warehouse-scale applications, and applies feedback to the compilation for the next release. AutoFDO works by profiling and storing profiles in an aggregate database, annotating the profiles via an intermediate representation, and finally providing feedback. On average, this technique boosts performance by around 10% and works well even with stale releases. For profiles to be useful, they must be in an intermediate representation. This is built from binary-level profile, which uses LBR to map instruction frequencies. By using these frequencies, program counters, and the source, a source profile can be generated. The source profile can be used to build an annotated call-graph with edge frequencies, with some inaccuracy. These annotated call-graphs can then be used as feedback to the compiler. Because of the size of the applications and scale of d...