Vamana build optimization set and fp16 support

[bkarsin]

Series of GPU Vamana build performance optimizations that addresses #2178 and #1757. Initial estimates from #1757 were not accurate, so many other optimizations were tried (some abandoned, some successful). This PR includes:

GreedySearch optimizations:

• Multi-warp blocks - increases occupancy of GreedySearch
• Reduce shared memory used per warp
• fp16 query approximation (along with fp16 support)

RobustPrune optimizations:

• Multi-warp block support (block size depends on graph degree)
• Caching accepted candidate vectors during occlusion loop
• Remove redundant merge of candidate lists and avoid syncs

General optimizations:

• Replace prefix_sums kernel with cub variant
• Re-use distances from GreedySearch in RobustPrune kernel
• fp16 support added

Together these optimizations give significant speedups across all configs with minimal recall variance compared to the current baseline. I benchmarked performance across a range of synthetic datasets and two real-world datasets. (NOTE: finishing benchmarks and will update tables below once they are all collected).

Synthetic dataset build tests (all 1M vector datasets)

		RTX pro 6000	RTX pro 6000	H100	H100	L4	L4
TYPE	Dim/Degree	BASELINE	OPT	BASELINE	OPT	BASELINE	OPT
fp32	64/32	1.11	1.07	1.35	1.19	9.83	3.57
fp32	768 / 32	5.98	4.03	6.01	4.92	36.6	25.8
fp32	960 / 32	7.82	5.61	8.23	6.52	50.1	38.7
fp32	64/64	4.4	4.05	6.14	5.4	16.4	14.8
fp32	768/64	28.8	18.2	33.7	25.3	194	125
fp32	960/64	39.4	24.9	50.7	36.5	251	185
fp16	64/32		1.05				3.8
fp16	768 / 32		2.5				11.1
fp16	960 / 32		3.13				14.1
fp16	64/64		4.01				13.6
fp16	768/64		10.4				53.8
fp16	960/64		14.2				65.6
int8	64/32	1.06	0.966	1.69	1.74	3.5	3.35
int8	768 / 32	2.98	2.38	4.82	3.69	11.2	9.21
int8	960 / 32	3.85	3	6.39	4.32	15.1	12
int8	64/64	4.15	3.61	6.36	6.05	14.4	13.3
int8	768/64	12.9	9.74	22.4	14.9	51.7	39.7
int8	960/64	17.1	12.5	28.8	19.9	72.1	52.1

Also tested real-world BIGANN 10M (uint8 128D) and GIST (fp32 960D) datasets:

		RTX pro 6000	RTX pro 6000	H100	H100	L4	L4
Dataset	deg / iters	BASELINE	OPT	BASELINE	OPT	BASELINE	OPT
BIGANN	32 / 1.0			13.1152	12.58	40.3484	39.887
BIGANN	32 / 2.0			32.0109	30.4	90.6138	89.994
BIGANN	64 / 1.0			48.1399	43.65	126.248	120.394
BIGANN	64 / 2.0			118.444	107.91	299.2	285.543
GIST (fp32)	32 / 1.0	6.39807	4.9804	6.773	5.51	41.48	32.3789
GIST (fp32)	32 / 2.0	16.2624	12.1026	19.0785	13.7675	103.197	76.84
GIST (fp32)	64 / 1.0	22.9393	16.74	27.2307	20.1767	150.2	109.98
GIST (fp32)	64 / 2.0	60.1051	41.452	71.1611	53.6482	380.185	262.1
GIST (fp16)	32 / 1.0		2.597		3.80264
GIST (fp16)	32 / 2.0		6.176		8.692
GIST (fp16)	64 / 1.0		9.965		13.793
GIST (fp16)	64 / 2.0		23.95		34.1922

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