Tune feedsim_autoscale_dlrm_mini toward production CPU profile (1800 stories, 8 inferences) (#715)

Summary:
Pull Request resolved: #715

The first sweep with async feature extraction (D...c58bc574d94c stack tip prior) confirmed the runFlatExtractors fanout reaches GlobalCPUThread correctly, but the dlrm_mini defaults left two large gaps vs production multifeed_aggregator's strobelight breakdown:

| Category          | Bench % non-idle (BGM q150 s05) | Prod target |
|-------------------|---------------------------------|-------------|
| DLRM-Inference    | 61.6%                           | 7-13%       |
| FeatureExtraction | 1.9%                            | 30-35%      |

Two knob changes get both metrics moving toward prod:

1. dlrm_inferences=64 -> 8. Shrinks the per-request DLRM matmul work 8x. Should drop the DLRM-Inference share from ~55-62% toward prod's 7-13% Ranking-Prediction share.

2. num_stories=100 -> 1800. With extractors_per_story=50 unchanged, that's 90K extractor calls/req (was 5K). Should push FeatureExtraction CPU share from 1.7-1.9% toward prod's 28-32% Ranking-FeatureExtraction share. The system has plenty of CPU headroom (BGM 80% idle past saturation, CPL 60% idle), so adding 90K extractor calls/req shouldn't break throughput.

Both knobs only affect feedsim_autoscale_dlrm_mini. The full feedsim_autoscale_dlrm job keeps its existing config. Knobs remain overridable per-run via -i.

Reviewed By: YifanYuan3

Differential Revision: D105119226

Author: excelle08

benchpress/config/jobs.yml

@@ -828,15 +828,20 @@
     - 'dlrm_model=models/dlrm_small.pt'
     - 'dlrm_batch_size=256'
     - 'dlrm_threads=1'
-    - 'dlrm_inferences=64'
+    # 8 inferences/req brings DLRM-Inference CPU share from 55-62% down
+    # toward prod multifeed_aggregator's Ranking-Prediction share of 7-13%.
+    - 'dlrm_inferences=8'
     - 'client_side_features=0'
     - 'client_batch_size=256'
     - 'client_inferences=64'
     - 'client_feature_seed=42'
     - 'client_num_dense=13'
     - 'client_num_sparse=26'
     - 'feature_complexity=5'
-    - 'num_stories=100'
+    # 1800 stories x 50 extractors = 90K extractor calls/req. Tuned to
+    # push FeatureExtraction CPU share into prod's 30-35% band; 5K
+    # calls/req previously gave only 1.7-1.9%.
+    - 'num_stories=1800'
     - 'extractors_per_story=50'
     - 'fixed_qps=100000'
     - 'fixed_qps_duration=10'