Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Profile Guided Optimization without Profiles: A Machine Learning
  Approach   Profile guided optimization is an effective technique for improving the
optimization ability of compilers based on dynamic behavior, but collecting
profile data is expensive, cumbersome, and requires regular updating to remain
fresh. We present a novel statistical approach to inferring branch
probabilities that improves the performance of programs that are compiled
without profile guided optimizations. We perform offline training using
information that is collected from a large corpus of binaries that have branch
probabilities information. The learned model is used by the compiler to predict
the branch probabilities of regular uninstrumented programs, which the compiler
can then use to inform optimization decisions. We integrate our technique
directly in LLVM, supplementing the existing human-engineered compiler
heuristics. We evaluate our technique on a suite of benchmarks, demonstrating
some gains over compiling without profile information. In deployment, our
technique requires no profiling runs and has negligible effect on compilation
time.

10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph