The SIMD auto-vectorization is an off-shoot of this research. But what excites me about super-optimization is to make functional optimization repeatable. Let

The SIMD auto-vectorization is an off-shoot of this research. But what excites me about super-optimization is to make functional optimization repeatable. Let me give you an example of why I'd use it for more than HPC libs. Assume you have a firewall system, which needs to determine whether a tuple of (srcip, dstip, dstport) is a safe one or not. All 3 variables fit inside registers (assume xmm). A rules engine as written traditionally would be a hashtable of some sort, always regenerated with a gperf hash (already we're in super-optimal territory partially). But what if going to RAM is 300x more expensive than a SIMD sequence over 3 registers run a few hundred times? Then writing a functional chunk of code which produces the same outputs for the same inputs, but without any storage system would be a killer feature. That is sort of the problem where a super-optimizer can take a huge amount of data and turn it into bit-twiddling checks which find hidden invariants in each data-set. Being able to take a bunch of rules which say subnet to subnet or port access patterns and compile it into a storage -less ACL mechanism (like bit 11 on port is 1, then DENY). I'm almost certain these problems were solved in the past using a super-optimized FPGA as well (well, I would if I had the budget). For me, being able to churn a dumb SWITCH loop through these systems is more interesting than chucking well written code into this.