- parse_backend.py for sglang_vs_vllm and dspark_vs_default:
- compute slo_status per scenario (TTFT P95 < 3000ms, TPOT mean < 50ms)
- append slo_status to results.json
- add SLO column to report.md
- BENCHMARK_WORKFLOW.md documents slo_status in schema and checklist
- sglang_vs_vllm/run_bench.sh: add phase1/phase2 server_args per backend
- dspark_vs_default/run_bench.sh: add server_args for dspark/default
- READMEs document where to find the recorded server args
- config.env: add SHAREGPT_DATASET, SHAREGPT_CONTEXT_LEN and SHAREGPT_SCENARIOS
- run_bench.sh: add run_sharegpt() and integrate it after Phase 1 for each backend
- parse_backend.py: support both phase{1|2} and sharegpt raw output filenames
- README.md: document ShareGPT scenarios
- New experiments/dsv4_h200_sglang_vs_vllm/ with TP=8 on all 8 H200 cards
- Phase1 short-context throughput + Phase2 long context up to 200k
- Unified scenario matrix, warmup, parsing, and side-by-side comparison report
- H200 SGLang vs vLLM entry added to root README