Boost Linear Algebra Computation Performance via Efficient VNNI Utilization

Intel's Vector Neural Network Instruction (VNNI) provides higher efficiency on calculating dense linear algebra (DLA) computations than conventional SIMD instructions. However, existing auto-vectorizers frequently deliver suboptimal utilization of VNNI by either failing to recognize VNNI's unique computation pattern at the innermost loops/basic blocks, or producing inferior code through constrained and rudimentary peephole optimizations/pattern matching techniques. Auto-tuning frameworks might generate proficient code but are hampered by the necessity for sophisticated pattern templates and extensive search processes.