Docs: Harley-Seal plans for binary kernels

#138

include/simsimd/binary.h

@@ -12,8 +12,37 @@
  *  - Arm: NEON, SVE
  *  - x86: Haswell, Ice Lake
  *
+ *  The hardest part of optimizing binary similarity measures is the population count operation.
+ *  It's natively supported by almost every insrtuction set, but the throughput and latency can
+ *  be suboptimal. There are several ways to optimize this operation:
+ *
+ *  - Lookup tables, mostly using nibbles (4-bit lookups)
+ *  - Harley-Seal population counts: https://arxiv.org/pdf/1611.07612
+ *
+ *  On binary vectors, when computing Jaccard similarity we can clearly see how the CPU struggles
+ *  to compute that many population counts. There are several instructions we should keep in mind
+ *  for future optimizations:
+ *
+ *  - `_mm512_popcnt_epi64` maps to `VPOPCNTQ (ZMM, K, ZMM)`:
+ *      - On Ice Lake: 3 cycles latency, ports: 1*p5
+ *      - On Genoa: 2 cycles latency, ports: 1*FP01
+ *  - `_mm512_shuffle_epi8` maps to `VPSHUFB (ZMM, ZMM, ZMM)`:
+ *      - On Ice Lake: 1 cycles latency, ports: 1*p5
+ *      - On Genoa: 2 cycles latency, ports: 1*FP12
+ *  - `_mm512_sad_epu8` maps to `VPSADBW (ZMM, ZMM, ZMM)`:
+ *      - On Ice Lake: 3 cycles latency, ports: 1*p5
+ *      - On Zen4: 3 cycles latency, ports: 1*FP01
+ *  - `_mm512_tertiarylogic_epi64` maps to `VPTERNLOGQ (ZMM, ZMM, ZMM, I8)`:
+ *      - On Ice Lake: 1 cycles latency, ports: 1*p05
+ *      - On Zen4: 1 cycles latency, ports: 1*FP0123
+ *  - `_mm512_gf2p8mul_epi8` maps to `VPGF2P8AFFINEQB (ZMM, ZMM, ZMM)`:
+ *      - On Ice Lake: 5 cycles latency, ports: 1*p0
+ *      - On Zen4: 3 cycles latency, ports: 1*FP01
+ *
  *  x86 intrinsics: https://www.intel.com/content/www/us/en/docs/intrinsics-guide/
  *  Arm intrinsics: https://developer.arm.com/architectures/instruction-sets/intrinsics/
+ *  SSE POPCOUNT experiments by Wojciech Muła: https://github.com/WojciechMula/sse-popcount
+ *  R&D progress tracker: https://github.com/ashvardanian/SimSIMD/pull/138
  */
 #ifndef SIMSIMD_BINARY_H
 #define SIMSIMD_BINARY_H
@@ -321,19 +350,6 @@ SIMSIMD_PUBLIC void simsimd_jaccard_b8_ice(simsimd_b8_t const *a, simsimd_b8_t c
                                            simsimd_distance_t *result) {
 
     simsimd_size_t intersection = 0, union_ = 0;
-    //? On such vectors we can clearly see that the CPU struggles to perform this many parallel
-    //? population counts, because the throughput of Jaccard and Hamming in this case starts to differ.
-    //? One optimization, aside from Harley-Seal transforms can be using "shuffles" for nibble-popcount
-    //? lookups, to utilize other ports on the CPU.
-    //? https://github.com/ashvardanian/SimSIMD/pull/138
-    //
-    //  - `_mm512_popcnt_epi64` maps to `VPOPCNTQ (ZMM, K, ZMM)`:
-    //      - On Ice Lake: 3 cycles latency, ports: 1*p5
-    //      - On Genoa: 2 cycles latency, ports: 1*FP01
-    //  - `_mm512_shuffle_epi8` maps to `VPSHUFB (ZMM, ZMM, ZMM)`:
-    //      - On Ice Lake: 1 cycles latency, ports: 1*p5
-    //      - On Genoa: 2 cycles latency, ports: 1*FP12
-    //
     //  It's harder to squeeze out performance from tiny representations, so we unroll the loops for binary metrics.
     if (n_words <= 64) { // Up to 512 bits.
         __mmask64 mask = (__mmask64)_bzhi_u64(0xFFFFFFFFFFFFFFFF, n_words);