  Modern software packages have become increasingly complex with millions of
lines of code and references to many external libraries. Redundant operations
are a common performance limiter in these code bases. Missed compiler
optimization opportunities, inappropriate data structure and algorithm choices,
and developers' inattention to performance are some common reasons for the
existence of redundant operations. Developers mainly depend on compilers to
eliminate redundant operations. However, compilers' static analysis often
misses optimization opportunities due to ambiguities and limited analysis
scope; automatic optimizations to algorithmic and data structural problems are
out of scope.
  We develop LoadSpy, a whole-program profiler to pinpoint redundant memory
load operations, which are often a symptom of many redundant operations. The
strength of LoadSpy exists in identifying and quantifying redundant load
operations in programs and associating the redundancies with program execution
contexts and scopes to focus developers' attention on problematic code. LoadSpy
works on fully optimized binaries, adopts various optimization techniques to
reduce its overhead, and provides a rich graphic user interface, which make it
a complete developer tool. Applying LoadSpy showed that a large fraction of
redundant loads is common in modern software packages despite highest levels of
automatic compiler optimizations. Guided by LoadSpy, we optimize several
well-known benchmarks and real-world applications, yielding significant
speedups.

Performance;Programming Languages;Software Engineering