A Performance Analysis of Vector Length Agnostic Code

Abstract

Vector extensions are a popular mean to exploit data parallelism in applications. Over recent years, the most commonly used extensions have been growing in vector length and amount of vector instructions. However, code portability remains a problem when speaking about a compute continuum. Hence, vector length agnostic (VLA) architectures have been proposed for the future generations of ARM and RISC-V processors. With these architectures, code is vectorized independently of the vector length of the target hardware platform. It is therefore possible to tune software to a generic vector length. To understand the performance impact of VLA code compared to vector length specific code, we analyze the current capabilities of code generation for ARM’s SVE architecture. Our experiments show that VLA code reaches about 90% of the performance of vector length specific code, i.e. a 10% overhead is inferred due to global predication of instructions. Furthermore, we show that code performance is not increasing proportionally with increasing vector lengths due to the higher memory demands.