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Gahow Wang a67e724119 docs: Phase 15 design doc + benchmark report

Design document (docs/15-performance.md):
- Roofline analysis: 112 tok/s theoretical at 1.79 TB/s
- Bottleneck quantification: cuBLAS M=1 GEMV at 8% bandwidth → 77% of step time
- Six optimizations with rationale, implementation details, and expected impact
- Ablation table with per-optimization delta measurements
- Remaining 55% roofline gap breakdown with next-step priorities

Benchmark report (docs/benchmarks/phase15-performance.md):
- Full ablation: 12.9 → 50.3 tok/s across 6 optimizations
- Per-prompt detail (8 prompts, 46-51 tok/s range)
- Concurrent throughput analysis (batch=4 vs serial)
- Phase-over-phase tracking from Phase 8 to Phase 15 (2.5 → 50.3 tok/s)
- Correctness verification (9/10 top-1 match, 52/52 API pass)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-05-23 00:39:27 +08:00

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Phase 15 Benchmark: Performance Optimization

Date: 2026-05-23 Hardware: RTX 5090 (32GB GDDR7, SM120 CC 12.0, 170 SMs, 1.79 TB/s) Model: Qwen3-8B (BF16, 36 layers, 4096 hidden, 32 Q / 8 KV GQA heads, head_dim=128) Config: greedy decoding (temperature=0), max_tokens=64, serial (batch=1)

Ablation: Each Optimization Measured Independently

#	Optimization	tok/s	Delta	ms/token	Roofline
0	Phase 14 baseline (FA2 + naive cuBLAS GEMV)	12.9	—	77.5	12%
1	+ Decode attention kernel (256 threads)	12.9	+0%	77.5	12%
2	+ Fused SiLU×Mul	13.0	+1%	76.9	12%
3	+ Fused Add+RMSNorm	13.2	+2%	75.8	12%
4	+ Custom GEMV (M=1, K-split tiled)	46.6	+253%	21.5	42%
5	+ Tensor::empty (skip cudaMemset)	50.3	+8%	19.9	45%

Comparison with HuggingFace transformers

8 prompts (short/medium/long) × max_tokens=64, greedy, serial:

System	tok/s	ms/token	Roofline
HF transformers (BF16, torch 2.8, SDPA)	36.0	27.8	32%
xserv Phase 15	50.3	19.9	45%
Roofline (1.79 TB/s, 16GB model)	112.0	8.9	100%

xserv is 140% of HF transformers throughput.

Per-Prompt Detail (Phase 15 Final)

#	Prompt	pt	ct	Time	tok/s
1	What is gravity?	12	64	1.39s	46.0
2	Hello, how are you?	14	64	1.27s	50.5
3	Explain DNA briefly.	13	64	1.25s	51.2
4	Write a detailed explanation of photosynthesis...	27	64	1.26s	50.7
5	Describe machine learning.	13	64	1.25s	51.2
6	What causes earthquakes?	12	64	1.25s	51.1
7	How does the internet work?	14	64	1.25s	51.1
8	What is the speed of light?	15	64	1.25s	51.0

Prompt 1 is slower (46.0 vs 51.x) due to first-request warmup (caching allocator cold start).

Concurrent Throughput

8 requests concurrent, max_batch=4:

Config	tok/s	Wall clock	Speedup
Serial (batch=1, custom GEMV)	50.3	—	—
Concurrent (batch=4, cuBLAS M=4)	28.2	9.09s	6.47x scheduling
Concurrent (batch=4, custom GEMV)	35.1*	~7.3s	~6x scheduling

*Note: batch=4 with custom GEMV is slower than serial because:

Batched decode path uses cuBLAS for M>1 matmuls, losing the GEMV advantage
Per-seq attention/reshape overhead in the batched path adds ~2ms/step
Custom GEMV already saturates bandwidth at M=1

Serial decode with custom GEMV is the optimal path for current architecture.

Correctness Verification

Test	Result
Top-1 logits match vs HF (10 prompts)	9/10 (90%)
Top-5 overlap vs HF (10 prompts)	4.0/5 avg
vs pre-optimization baseline	Identical (same 9/10)
API generation (52 prompts)	52/52 pass
SSE streaming	Working
Chinese prompts	Working

Phase-over-Phase Performance Tracking

Phase	Key Change	tok/s	vs HF	Roofline
8	GPT-2 inference (no cache)	2.5	7%	—
9	+ KV cache (CPU)	44.3 (GPT-2)	—	—
10	Qwen3-8B (CPU KV cache)	6.9	19%	6%
11	+ GPU KV cache	10.3	29%	9%
14	+ Flash Attention 2	12.9	36%	12%
15	+ Custom GEMV + fused + empty	50.3	140%	45%

Total speedup from Phase 10 to Phase 15: 7.3x (6.9 → 50.3 tok/s).

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