Abstract:
In a program, not all the bits of a variable are
always used during execution. Identifying the minimum number
of bits necessary to represent a variable in a program can
potentially provide optimization opportunities. Providing the
knowledge of bitwidths to a compilation and execution framework
will be advantageous if it could use that information to optimize
the execution of the program, for instance, being able to select
instructions for SIMD vectorization. This paper introduces a
framework to exploit the potential vectorizations hidden in a
program which is not exposed during static compilation time. Our
framework unlocks instruction level data parallelism by using the
bitwidths of array like variables that depend on runtime input.
Our framework shows a maximum achievable performance gain
of 37% and a mean achievable performance gain of 11% against
the ICC compiler on our micro benchmark suite.
Citation:
K. Sundararajah and S. Jayasena, "Model-based input-adaptive vectorization," 2016 Moratuwa Engineering Research Conference (MERCon), 2016, pp. 66-71, doi: 10.1109/MERCon.2016.7480117.
