  Linear network coding requires arithmetic operations over Galois fields, more
specifically over finite extension fields. While coding over GF(2) reduces to
simple XOR operations, this field is less preferred for practical applications
of random linear network coding due to high chances of linear dependencies and
therefore redundant coded packets. Coding over larger fields such as GF(16) and
GF(256) does not have that issue, but is significantly slower. SIMD vector
extensions of processors such as AVX2 on x86-based systems or NEON on ARM-based
devices offer the potential to increase performance by orders of magnitude.
  In this paper we present an implementation of different algorithms and Galois
fields based on the AVX512 instruction set extension and integrate it into the
finite field library libmoepgf. We compare the performance of the new
implementation to the reference implementation based on AVX2, showing a
significant increase in throughput. In addition, we provide a survey of the
best possible coding performance offered by a variety of different platforms.
