[WIP] Revisiting JVector codec

[abernardi597]

Description

I took a stab at bringing the OpenSearch JVector codec into Lucene as a codec in sandbox (see issue #14681) to see how a DiskANN-insipired index might compare to the current generation of HNSW.
I made quite a few changes along the way and wanted to cut this PR to share some of those changes/results and maybe solicit some feedback from interested parties. Most notably, I did remove the incremental graph building functionality that is used to speed up merges, though I'd like to add it back and look at the improvements in merge-time for JVector indices. I also made a PR for JVector (datastax/jvector#577) to fix a byte-order inconsistency to better leverage Lucene's bulk-read for floats.

I hooked it up to lucene-util (PR incoming) for comparison, trying to play into the strengths of each codec while also maintaining similar levels of parallelism. I ran HNSW using 32x indexing threads and force-merging into 1 segment while using 1x indexing thread for JVector backed by a 32x concurrency ForkJoinPool for its SIMD operations and ForkJoinPool.commonPool() for its other parallel operations. I also fixed oversample=1 for both and used neighborOverflow=2 and alpha=2 for JVector.

These results are from the 768-dim cohere dataset using PQ for quantization in JVector and OSQ in Lucene using a m7g.16xlarge EC2 instance.

recall	latency(ms)	netCPU	avgCpuCount	nDoc	topK	fanout	maxConn	beamWidth	quantized	visited	index(s)	index_docs/s	force_merge(s)	num_segments	index_size(MB)	vec_disk(MB)	vec_RAM(MB)	indexType	metric
0.965	1.408	1.399	0.994	100000	100	50	64	250	no	4968	5.99	16700.07	10.10	1	298.17	292.969	292.969	HNSW	COSINE
0.939	2.186	2.155	0.986	100000	100	50	64	250	no	3485	19.58	5107.77	0.01	1	318.80	292.969	292.969	JVECTOR	COSINE
0.963	1.409	1.401	0.994	100000	100	50	64	250	8 bits	5028	8.75	11431.18	12.95	1	372.84	367.737	74.768	HNSW	COSINE
0.939	9.524	9.516	0.999	100000	100	50	64	250	8 bits	3525	886.28	112.83	0.01	1	392.79	367.737	74.768	JVECTOR	COSINE
0.899	0.967	0.959	0.992	100000	100	50	64	250	4 bits	5076	8.84	11314.78	9.07	1	335.80	331.116	38.147	HNSW	COSINE
0.937	3.469	3.457	0.997	100000	100	50	64	250	4 bits	3437	148.70	672.51	0.01	1	356.17	331.116	38.147	JVECTOR	COSINE
0.669	0.681	0.673	0.988	100000	100	50	64	250	1 bits	5895	8.04	12439.36	8.84	1	308.42	303.459	10.490	HNSW	COSINE
0.730	1.056	1.044	0.989	100000	100	50	64	250	1 bits	2672	51.39	1945.90	0.01	1	328.70	303.459	10.490	JVECTOR	COSINE

This PR is not really intended to be merged, in light of some of the feedback on the previous PR (#14892) that suggests Lucene should try to incorporate some of the learnings rather than add yet another KNN engine.