{"id":2989,"date":"2025-08-19T21:04:02","date_gmt":"2025-08-19T21:04:02","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=2989"},"modified":"2025-08-20T05:10:32","modified_gmt":"2025-08-20T05:10:32","slug":"scythe-gpu-possession-scheduler","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=2989","title":{"rendered":"SCYTHE GPU Possession Scheduler"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">1. <strong>Stakeholder Communication Tool<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The charts turn abstract scheduling policies into something <strong>tangible<\/strong>:\n<ul class=\"wp-block-list\">\n<li>SLA lines + tenant latencies \u2192 investors &amp; customers can <em>see<\/em> QoS differentiation.<\/li>\n\n\n\n<li>Queue depth vs. GPU occupancy \u2192 shows why overprovisioning is wasteful and how your scheduler avoids it.<\/li>\n\n\n\n<li>Job completion distributions \u2192 fairness &amp; predictability made concrete.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>Instead of raw math, you\u2019ve now got a <strong>demo deck<\/strong> that can go in front of:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A CTO at a cloud company (\u201chere\u2019s how we guarantee tenant A\u2019s LLM inference stays &lt;30ms while tenant C\u2019s batch analytics don\u2019t starve\u201d).<\/li>\n\n\n\n<li>A DoD program office (\u201cthis ensures mission-critical signals intelligence workloads preempt non-critical batch jobs\u201d).<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">2. <strong>Experimentation Sandbox<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Seeds \u2192 variability exploration. You already ran seed sweeps and showed variance in SLA violations and job sharing.<\/li>\n\n\n\n<li>Interference matrices \u2192 you can add knobs (e.g., RF vs. NLP jobs interfere at X% more) and run comparative visualizations.<\/li>\n\n\n\n<li>Scaling \u2192 move from 3 tenants to 30 with synthetic traffic models (Poisson arrivals, bursty loads, periodic sweeps).<\/li>\n\n\n\n<li>Stress test \u2192 crank total arrival rate beyond GPU capacity and measure which tenants degrade, and how.<\/li>\n<\/ul>\n\n\n\n<p>This lets you prove robustness <em>before<\/em> deploying into messy production workloads.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">3. <strong>Productization Hook<\/strong><\/h2>\n\n\n\n<p>Your harness is now a <strong>demo-able feature<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Run it live in front of an audience \u2192 tweak SLAs interactively and watch graphs react.<\/li>\n\n\n\n<li>Wrap it in a simple web dashboard (Flask + React) \u2192 prospective customers can spin up tenants and see \u201ctheir\u201d jobs behave under your scheduler.<\/li>\n\n\n\n<li>Position it as a <strong>QoS enforcement simulator<\/strong>:\n<ul class=\"wp-block-list\">\n<li>Cloud GPU tenancy: Hugging Face, Lambda Labs, RunPod.<\/li>\n\n\n\n<li>Enterprise AI ops: inference on shared GPU clusters.<\/li>\n\n\n\n<li>Gov\/defense: ISR workloads with strict timelines.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">4. <strong>Competitive Differentiator<\/strong><\/h2>\n\n\n\n<p>Most schedulers in the wild (e.g. Kubernetes + NVIDIA MPS) don\u2019t show <strong>transparent tenant-level metrics<\/strong>.<br>Your harness makes that visibility a <em>feature<\/em>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u201cNot only do we schedule fairly, but here\u2019s how you can prove it.\u201d<\/li>\n\n\n\n<li>That becomes both a <strong>sales story<\/strong> and a <strong>compliance artifact<\/strong> (think NIST\/FedRAMP).<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">5. <strong>Bridge to Real-World Signals<\/strong><\/h2>\n\n\n\n<p>The beauty is: this isn\u2019t limited to synthetic jobs.<br>Next step = swap <code>MockScheduler<\/code> with real:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Torch jobs (<code>torch.cuda<\/code> load).<\/li>\n\n\n\n<li>RF sweeps (SoapySDR\/BladeRF workloads).<\/li>\n\n\n\n<li>NLP inference (transformer forward passes).<\/li>\n<\/ul>\n\n\n\n<p>Then your visualizations become a <strong>control tower<\/strong> for real GPU fleets.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>cd \/home\/bgilbert\/editable_files\/SignalIntelligence &amp;&amp; chmod +x test_scheduler.py &amp;&amp; .\/test_scheduler.py\n=== GPU Possession Scheduler Test Harness ===\nTarget latency SLA: 50.0ms\n\n=== Simulating default QoS (latency factor: 1.0) ===\n&#91;Step 00] Latency p95=16.7ms Occ=0.00 QPS=239.8 Batch=4 \u2192 Suggest=8\n&#91;Step 01] Latency p95=17.4ms Occ=0.11 QPS=348.6 Batch=8 \u2192 Suggest=16\n&#91;Step 02] Latency p95=30.2ms Occ=0.15 QPS=401.3 Batch=16 \u2192 Suggest=32\n&#91;Step 03] Latency p95=46.4ms Occ=0.25 QPS=464.3 Batch=32 \u2192 Suggest=32\n&#91;Step 04] Latency p95=49.3ms Occ=0.31 QPS=501.1 Batch=32 \u2192 Suggest=32\n&#91;Step 05] Latency p95=49.3ms Occ=0.35 QPS=528.1 Batch=32 \u2192 Suggest=32\n&#91;Step 06] Latency p95=50.5ms Occ=0.38 QPS=542.3 Batch=32 \u2192 Suggest=16\n&#91;Step 07] Latency p95=50.4ms Occ=0.37 QPS=542.4 Batch=16 \u2192 Suggest=8\n&#91;Step 08] Latency p95=50.3ms Occ=0.34 QPS=522.2 Batch=8 \u2192 Suggest=4\n&#91;Step 09] Latency p95=50.3ms Occ=0.31 QPS=491.3 Batch=4 \u2192 Suggest=2\n&#91;Step 10] Latency p95=50.2ms Occ=0.29 QPS=461.3 Batch=2 \u2192 Suggest=1\n&#91;Step 11] Latency p95=50.1ms Occ=0.26 QPS=428.3 Batch=1 \u2192 Suggest=1\n&#91;Step 12] Latency p95=50.0ms Occ=0.25 QPS=405.1 Batch=1 \u2192 Suggest=1\n&#91;Step 13] Latency p95=49.9ms Occ=0.23 QPS=382.8 Batch=1 \u2192 Suggest=1\n&#91;Step 14] Latency p95=49.9ms Occ=0.21 QPS=361.7 Batch=1 \u2192 Suggest=1\n&#91;Step 15] Latency p95=49.8ms Occ=0.20 QPS=347.0 Batch=1 \u2192 Suggest=1\n&#91;Step 16] Latency p95=49.7ms Occ=0.19 QPS=333.9 Batch=1 \u2192 Suggest=1\n&#91;Step 17] Latency p95=49.6ms Occ=0.18 QPS=319.8 Batch=1 \u2192 Suggest=1\n&#91;Step 18] Latency p95=49.5ms Occ=0.18 QPS=307.6 Batch=1 \u2192 Suggest=1\n&#91;Step 19] Latency p95=49.4ms Occ=0.17 QPS=299.0 Batch=1 \u2192 Suggest=1\n&#91;Step 20] Latency p95=49.4ms Occ=0.16 QPS=287.9 Batch=1 \u2192 Suggest=1\n&#91;Step 21] Latency p95=49.3ms Occ=0.16 QPS=280.2 Batch=1 \u2192 Suggest=1\n&#91;Step 22] Latency p95=49.3ms Occ=0.15 QPS=273.9 Batch=1 \u2192 Suggest=1\n&#91;Step 23] Latency p95=49.3ms Occ=0.14 QPS=266.4 Batch=1 \u2192 Suggest=1\n&#91;Step 24] Latency p95=49.3ms Occ=0.14 QPS=260.2 Batch=1 \u2192 Suggest=1\n&#91;Step 25] Latency p95=49.3ms Occ=0.13 QPS=253.8 Batch=1 \u2192 Suggest=1\n&#91;Step 26] Latency p95=49.2ms Occ=0.13 QPS=247.0 Batch=1 \u2192 Suggest=1\n&#91;Step 27] Latency p95=49.2ms Occ=0.13 QPS=241.1 Batch=1 \u2192 Suggest=1\n&#91;Step 28] Latency p95=49.2ms Occ=0.12 QPS=235.3 Batch=1 \u2192 Suggest=1\n&#91;Step 29] Latency p95=49.2ms Occ=0.12 QPS=231.1 Batch=1 \u2192 Suggest=1\n\n=== Simulating high QoS (latency factor: 0.8) ===\n&#91;Step 00] Latency p95=7.5ms Occ=0.08 QPS=266.6 Batch=2 \u2192 Suggest=4\n&#91;Step 01] Latency p95=16.5ms Occ=0.05 QPS=250.9 Batch=4 \u2192 Suggest=8\n&#91;Step 02] Latency p95=19.5ms Occ=0.06 QPS=301.8 Batch=8 \u2192 Suggest=8\n&#91;Step 03] Latency p95=19.4ms Occ=0.08 QPS=378.5 Batch=8 \u2192 Suggest=8\n&#91;Step 04] Latency p95=19.3ms Occ=0.08 QPS=402.9 Batch=8 \u2192 Suggest=8\n&#91;Step 05] Latency p95=19.1ms Occ=0.09 QPS=419.0 Batch=8 \u2192 Suggest=8\n&#91;Step 06] Latency p95=21.8ms Occ=0.08 QPS=409.8 Batch=8 \u2192 Suggest=8\n&#91;Step 07] Latency p95=21.6ms Occ=0.08 QPS=421.2 Batch=8 \u2192 Suggest=8\n&#91;Step 08] Latency p95=21.5ms Occ=0.09 QPS=431.0 Batch=8 \u2192 Suggest=8\n&#91;Step 09] Latency p95=21.3ms Occ=0.08 QPS=444.9 Batch=8 \u2192 Suggest=8\n&#91;Step 10] Latency p95=21.2ms Occ=0.09 QPS=461.0 Batch=8 \u2192 Suggest=8\n&#91;Step 11] Latency p95=21.1ms Occ=0.08 QPS=468.0 Batch=8 \u2192 Suggest=8\n&#91;Step 12] Latency p95=21.5ms Occ=0.08 QPS=461.6 Batch=8 \u2192 Suggest=8\n&#91;Step 13] Latency p95=21.4ms Occ=0.08 QPS=463.5 Batch=8 \u2192 Suggest=8\n&#91;Step 14] Latency p95=21.3ms Occ=0.09 QPS=462.7 Batch=8 \u2192 Suggest=8\n&#91;Step 15] Latency p95=21.2ms Occ=0.09 QPS=461.5 Batch=8 \u2192 Suggest=8\n&#91;Step 16] Latency p95=21.1ms Occ=0.09 QPS=460.4 Batch=8 \u2192 Suggest=8\n&#91;Step 17] Latency p95=21.1ms Occ=0.08 QPS=466.9 Batch=8 \u2192 Suggest=8\n&#91;Step 18] Latency p95=21.0ms Occ=0.08 QPS=468.5 Batch=8 \u2192 Suggest=8\n&#91;Step 19] Latency p95=20.9ms Occ=0.09 QPS=470.3 Batch=8 \u2192 Suggest=8\n\n=== Simulating low QoS (latency factor: 1.2) ===\n&#91;Step 00] Latency p95=25.4ms Occ=0.08 QPS=315.2 Batch=8 \u2192 Suggest=16\n&#91;Step 01] Latency p95=29.3ms Occ=0.18 QPS=428.4 Batch=16 \u2192 Suggest=32\n&#91;Step 02] Latency p95=51.3ms Occ=0.28 QPS=484.3 Batch=32 \u2192 Suggest=16\n&#91;Step 03] Latency p95=50.6ms Occ=0.30 QPS=483.6 Batch=16 \u2192 Suggest=8\n&#91;Step 04] Latency p95=49.6ms Occ=0.28 QPS=454.9 Batch=8 \u2192 Suggest=8\n&#91;Step 05] Latency p95=48.6ms Occ=0.25 QPS=441.9 Batch=8 \u2192 Suggest=8\n&#91;Step 06] Latency p95=47.6ms Occ=0.23 QPS=446.9 Batch=8 \u2192 Suggest=8\n&#91;Step 07] Latency p95=46.5ms Occ=0.22 QPS=433.6 Batch=8 \u2192 Suggest=8\n&#91;Step 08] Latency p95=45.5ms Occ=0.22 QPS=419.3 Batch=8 \u2192 Suggest=8\n&#91;Step 09] Latency p95=44.5ms Occ=0.22 QPS=409.1 Batch=8 \u2192 Suggest=8\n&#91;Step 10] Latency p95=43.5ms Occ=0.21 QPS=400.9 Batch=8 \u2192 Suggest=8\n&#91;Step 11] Latency p95=42.4ms Occ=0.20 QPS=393.4 Batch=8 \u2192 Suggest=8\n&#91;Step 12] Latency p95=41.4ms Occ=0.19 QPS=389.7 Batch=8 \u2192 Suggest=8\n&#91;Step 13] Latency p95=40.4ms Occ=0.19 QPS=393.2 Batch=8 \u2192 Suggest=8\n&#91;Step 14] Latency p95=39.4ms Occ=0.19 QPS=391.7 Batch=8 \u2192 Suggest=16\n&#91;Step 15] Latency p95=39.5ms Occ=0.19 QPS=396.0 Batch=16 \u2192 Suggest=32\n&#91;Step 16] Latency p95=54.4ms Occ=0.20 QPS=405.6 Batch=32 \u2192 Suggest=16\n&#91;Step 17] Latency p95=54.2ms Occ=0.21 QPS=409.0 Batch=16 \u2192 Suggest=8\n&#91;Step 18] Latency p95=54.0ms Occ=0.20 QPS=406.6 Batch=8 \u2192 Suggest=4\n&#91;Step 19] Latency p95=53.9ms Occ=0.20 QPS=399.3 Batch=4 \u2192 Suggest=2\n\n=== Simulating load spike for default QoS ===\n&#91;Step 00] Latency p95=49.2ms Occ=0.12 QPS=230.3 Batch=2 \u2192 Suggest=2 \n&#91;Step 01] Latency p95=49.2ms Occ=0.12 QPS=229.1 Batch=2 \u2192 Suggest=2 \n&#91;Step 02] Latency p95=49.1ms Occ=0.11 QPS=229.6 Batch=2 \u2192 Suggest=2 \n&#91;Step 03] Latency p95=49.1ms Occ=0.11 QPS=227.0 Batch=2 \u2192 Suggest=2 \n&#91;Step 04] Latency p95=49.1ms Occ=0.11 QPS=227.3 Batch=2 \u2192 Suggest=2 \n&#91;Step 05] Latency p95=49.1ms Occ=0.11 QPS=226.0 Batch=2 \u2192 Suggest=2 \n&#91;Step 06] Latency p95=49.1ms Occ=0.10 QPS=225.0 Batch=2 \u2192 Suggest=2 \n&#91;Step 07] Latency p95=49.0ms Occ=0.10 QPS=223.2 Batch=2 \u2192 Suggest=2 \n&#91;Step 08] Latency p95=49.0ms Occ=0.10 QPS=221.3 Batch=2 \u2192 Suggest=2 \n&#91;Step 09] Latency p95=49.0ms Occ=0.10 QPS=219.0 Batch=2 \u2192 Suggest=2 \n&#91;Step 10] Latency p95=49.0ms Occ=0.10 QPS=216.8 Batch=2 \u2192 Suggest=2 \ud83d\udd25 SPIKE \ud83d\udd25\n&#91;Step 11] Latency p95=48.9ms Occ=0.11 QPS=215.3 Batch=2 \u2192 Suggest=2 \ud83d\udd25 SPIKE \ud83d\udd25\n&#91;Step 12] Latency p95=48.9ms Occ=0.11 QPS=213.0 Batch=2 \u2192 Suggest=2 \ud83d\udd25 SPIKE \ud83d\udd25\n&#91;Step 13] Latency p95=48.9ms Occ=0.12 QPS=211.2 Batch=2 \u2192 Suggest=2 \ud83d\udd25 SPIKE \ud83d\udd25\n&#91;Step 14] Latency p95=48.8ms Occ=0.12 QPS=208.8 Batch=2 \u2192 Suggest=2 \ud83d\udd25 SPIKE \ud83d\udd25\n&#91;Step 15] Latency p95=48.8ms Occ=0.13 QPS=206.6 Batch=2 \u2192 Suggest=2 \ud83d\udd25 SPIKE \ud83d\udd25\n&#91;Step 16] Latency p95=48.8ms Occ=0.13 QPS=204.7 Batch=2 \u2192 Suggest=2 \ud83d\udd25 SPIKE \ud83d\udd25\n&#91;Step 17] Latency p95=48.7ms Occ=0.13 QPS=203.2 Batch=2 \u2192 Suggest=2 \ud83d\udd25 SPIKE \ud83d\udd25\n&#91;Step 18] Latency p95=48.7ms Occ=0.14 QPS=201.1 Batch=2 \u2192 Suggest=2 \ud83d\udd25 SPIKE \ud83d\udd25\n&#91;Step 19] Latency p95=48.7ms Occ=0.14 QPS=199.2 Batch=2 \u2192 Suggest=2 \ud83d\udd25 SPIKE \ud83d\udd25\n&#91;Step 20] Latency p95=48.7ms Occ=0.14 QPS=196.8 Batch=2 \u2192 Suggest=2 \n&#91;Step 21] Latency p95=48.7ms Occ=0.14 QPS=192.5 Batch=2 \u2192 Suggest=2 \n&#91;Step 22] Latency p95=48.7ms Occ=0.13 QPS=186.3 Batch=2 \u2192 Suggest=2 \n&#91;Step 23] Latency p95=39.8ms Occ=0.12 QPS=178.5 Batch=2 \u2192 Suggest=4 \n&#91;Step 24] Latency p95=26.2ms Occ=0.11 QPS=172.0 Batch=4 \u2192 Suggest=8 \n&#91;Step 25] Latency p95=21.1ms Occ=0.11 QPS=166.8 Batch=8 \u2192 Suggest=16 \n&#91;Step 26] Latency p95=21.1ms Occ=0.10 QPS=164.0 Batch=16 \u2192 Suggest=32 \n&#91;Step 27] Latency p95=21.1ms Occ=0.11 QPS=166.4 Batch=32 \u2192 Suggest=32 \n&#91;Step 28] Latency p95=27.0ms Occ=0.11 QPS=172.9 Batch=32 \u2192 Suggest=32 \n&#91;Step 29] Latency p95=40.5ms Occ=0.12 QPS=181.9 Batch=32 \u2192 Suggest=32 \n\n=== Simulating multi-QoS interaction ===\n\n&#91;Step 00] Shared occupancy: 0.33\n  HIGH: Latency p95=20.8ms Batch=4 QPS=452.5\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=181.7\n  LOW: Latency p95=53.7ms Batch=4 QPS=391.1\n\n&#91;Step 01] Shared occupancy: 0.55\n  HIGH: Latency p95=22.5ms Batch=8 QPS=439.9\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=183.8\n  LOW: Latency p95=52.8ms Batch=2 QPS=379.6\n\n&#91;Step 02] Shared occupancy: 0.69\n  HIGH: Latency p95=22.8ms Batch=8 QPS=433.4\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=185.3\n  LOW: Latency p95=51.8ms Batch=1 QPS=367.3\n\n&#91;Step 03] Shared occupancy: 0.60\n  HIGH: Latency p95=26.8ms Batch=8 QPS=427.3\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=187.5\n  LOW: Latency p95=50.9ms Batch=1 QPS=354.6\n\n&#91;Step 04] Shared occupancy: 0.61\n  HIGH: Latency p95=27.8ms Batch=8 QPS=421.3\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=190.5\n  LOW: Latency p95=50.0ms Batch=1 QPS=342.8\n\n&#91;Step 05] Shared occupancy: 0.44\n  HIGH: Latency p95=27.8ms Batch=8 QPS=418.6\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=191.8\n  LOW: Latency p95=49.1ms Batch=1 QPS=331.5\n\n&#91;Step 06] Shared occupancy: 0.56\n  HIGH: Latency p95=27.8ms Batch=8 QPS=414.6\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=193.7\n  LOW: Latency p95=48.1ms Batch=1 QPS=321.2\n\n&#91;Step 07] Shared occupancy: 0.65\n  HIGH: Latency p95=28.5ms Batch=8 QPS=409.2\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=195.4\n  LOW: Latency p95=47.2ms Batch=1 QPS=311.4\n\n&#91;Step 08] Shared occupancy: 0.56\n  HIGH: Latency p95=28.5ms Batch=8 QPS=407.2\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=197.5\n  LOW: Latency p95=46.3ms Batch=1 QPS=302.2\n\n&#91;Step 09] Shared occupancy: 0.69\n  HIGH: Latency p95=28.9ms Batch=8 QPS=402.8\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=197.8\n  LOW: Latency p95=45.4ms Batch=1 QPS=294.1\n\n&#91;Step 10] Shared occupancy: 0.64\n  HIGH: Latency p95=28.9ms Batch=8 QPS=398.9\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=200.3\n  LOW: Latency p95=44.4ms Batch=1 QPS=286.2\n\n&#91;Step 11] Shared occupancy: 0.62\n  HIGH: Latency p95=28.9ms Batch=8 QPS=396.5\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=202.4\n  LOW: Latency p95=43.5ms Batch=1 QPS=278.7\n\n&#91;Step 12] Shared occupancy: 0.69\n  HIGH: Latency p95=28.9ms Batch=8 QPS=393.7\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=202.8\n  LOW: Latency p95=42.6ms Batch=1 QPS=272.2\n\n&#91;Step 13] Shared occupancy: 0.54\n  HIGH: Latency p95=28.9ms Batch=8 QPS=390.7\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=204.5\n  LOW: Latency p95=41.7ms Batch=1 QPS=265.6\n\n&#91;Step 14] Shared occupancy: 0.69\n  HIGH: Latency p95=29.3ms Batch=8 QPS=387.1\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=206.3\n  LOW: Latency p95=40.7ms Batch=1 QPS=259.3\n\n&#91;Step 15] Shared occupancy: 0.45\n  HIGH: Latency p95=29.3ms Batch=8 QPS=384.5\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=208.1\n  LOW: Latency p95=39.8ms Batch=2 QPS=253.9\n\n&#91;Step 16] Shared occupancy: 0.31\n  HIGH: Latency p95=29.2ms Batch=8 QPS=383.1\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=210.2\n  LOW: Latency p95=38.9ms Batch=4 QPS=249.8\n\n&#91;Step 17] Shared occupancy: 0.56\n  HIGH: Latency p95=29.1ms Batch=8 QPS=380.6\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=211.4\n  LOW: Latency p95=37.9ms Batch=8 QPS=248.2\n\n&#91;Step 18] Shared occupancy: 0.55\n  HIGH: Latency p95=30.3ms Batch=8 QPS=376.9\n  DEFAULT: Latency p95=40.5ms Batch=4 QPS=213.3\n  LOW: Latency p95=37.7ms Batch=16 QPS=247.5\n\n&#91;Step 19] Shared occupancy: 0.65\n  HIGH: Latency p95=30.6ms Batch=8 QPS=372.9\n  DEFAULT: Latency p95=41.5ms Batch=4 QPS=213.5\n  LOW: Latency p95=46.2ms Batch=16 QPS=250.1\n\n&#91;Step 20] Shared occupancy: 0.65\n  HIGH: Latency p95=34.2ms Batch=8 QPS=369.0\n  DEFAULT: Latency p95=42.5ms Batch=4 QPS=211.5\n  LOW: Latency p95=50.8ms Batch=8 QPS=251.7\n\n&#91;Step 21] Shared occupancy: 0.55\n  HIGH: Latency p95=34.0ms Batch=8 QPS=367.3\n  DEFAULT: Latency p95=42.5ms Batch=4 QPS=210.3\n  LOW: Latency p95=50.6ms Batch=4 QPS=251.6\n\n&#91;Step 22] Shared occupancy: 0.54\n  HIGH: Latency p95=33.8ms Batch=8 QPS=365.6\n  DEFAULT: Latency p95=42.5ms Batch=4 QPS=208.8\n  LOW: Latency p95=50.3ms Batch=2 QPS=250.2\n\n&#91;Step 23] Shared occupancy: 0.51\n  HIGH: Latency p95=33.6ms Batch=8 QPS=364.9\n  DEFAULT: Latency p95=42.5ms Batch=4 QPS=209.8\n  LOW: Latency p95=50.1ms Batch=1 QPS=246.7\n\n&#91;Step 24] Shared occupancy: 0.44\n  HIGH: Latency p95=33.4ms Batch=8 QPS=363.9\n  DEFAULT: Latency p95=42.5ms Batch=4 QPS=209.2\n  LOW: Latency p95=49.8ms Batch=1 QPS=242.2\n\n&#91;Step 25] Shared occupancy: 0.38\n  HIGH: Latency p95=33.2ms Batch=8 QPS=363.4\n  DEFAULT: Latency p95=42.5ms Batch=4 QPS=209.5\n  LOW: Latency p95=49.6ms Batch=1 QPS=238.1\n\n&#91;Step 26] Shared occupancy: 0.42\n  HIGH: Latency p95=33.0ms Batch=8 QPS=363.1\n  DEFAULT: Latency p95=42.5ms Batch=4 QPS=208.9\n  LOW: Latency p95=49.3ms Batch=1 QPS=234.0\n\n&#91;Step 27] Shared occupancy: 0.54\n  HIGH: Latency p95=32.9ms Batch=8 QPS=363.0\n  DEFAULT: Latency p95=42.5ms Batch=4 QPS=209.5\n  LOW: Latency p95=49.1ms Batch=1 QPS=230.0\n\n&#91;Step 28] Shared occupancy: 0.41\n  HIGH: Latency p95=32.7ms Batch=8 QPS=361.2\n  DEFAULT: Latency p95=42.5ms Batch=4 QPS=209.6\n  LOW: Latency p95=48.8ms Batch=1 QPS=226.1\n\n&#91;Step 29] Shared occupancy: 0.68\n  HIGH: Latency p95=32.5ms Batch=8 QPS=361.6\n  DEFAULT: Latency p95=42.5ms Batch=4 QPS=210.2\n  LOW: Latency p95=48.6ms Batch=1 QPS=222.9\n\n=== Simulation Complete ===\nSummary of final recommendations:\nHIGH: Suggested batch=8, P95 Latency=32.5ms, QPS=361.6\nDEFAULT: Suggested batch=4, P95 Latency=42.5ms, QPS=210.2\nLOW: Suggested batch=1, P95 Latency=48.6ms, QPS=222.9\n<\/code><\/pre>\n\n\n\n<p>2<\/p>\n\n\n\n<p>Semi Related:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># We'll generate an updated trajectory inference module with TDoA support\n# and a tiny demo that synthesizes TDoA measurements and runs inference.\n\nfrom textwrap import dedent\nfrom pathlib import Path\n\nmodule_code = dedent(r\"\"\"\n# rf_sequence_recovery_tdoa.py\n# Trajectory inference from sparse RF sightings with full TDoA residuals.\n# Drop-in: replace\/augment your previous rf_sequence_recovery.py with this file.\n\nfrom __future__ import annotations\nfrom dataclasses import dataclass, field\nfrom typing import Dict, List, Tuple, Optional\nimport numpy as np\nimport math\nimport heapq\n\n# --------------------------\n# Constants\n# --------------------------\nC_MPS = 299_792_458.0  # speed of light (m\/s)\n\n# --------------------------\n# Data containers\n# --------------------------\n\n@dataclass\nclass Sensor:\n    sensor_id: str\n    xy: np.ndarray  # shape (2,), meters in a local ENU\n    clock_bias_s: float = 0.0  # optional fixed clock bias (sec); use 0 if unknown\n\n@dataclass\nclass RFSighting:\n    t: float                       # seconds\n    xy: Optional&#91;np.ndarray] = None  # rough geofix (m); None if not available\n    sigma_pos: Optional&#91;float] = None  # 1-sigma of geofix (m)\n    sensor_id: Optional&#91;str] = None\n    aoa_rad: Optional&#91;float] = None  # bearing at sensor (rad), 0 = +x\n    sigma_aoa: Optional&#91;float] = None # rad\n    snr_db: Optional&#91;float] = None\n\n@dataclass\nclass TDoAMeasurement:\n    \"\"\"A single TDoA observation between two sensors (i, j) near time t.\n    tdoa_s = t_i - t_j (time of arrival difference in seconds).\n    sigma_s = standard deviation (seconds).\n    Optionally set weight to down\/up-weight this pair (e.g., by cross-corr. peak quality).\n    \"\"\"\n    t: float\n    sensor_i: str\n    sensor_j: str\n    tdoa_s: float\n    sigma_s: float\n    weight: float = 1.0\n\n# --------------------------\n# Mobility graph\n# --------------------------\n\nclass MobilityGraph:\n    def __init__(self, nodes_xy: np.ndarray, adj: List&#91;List&#91;int]],\n                 edge_dist: np.ndarray, edge_vmax: np.ndarray,\n                 default_vmax: float = 15.0):\n        self.nodes_xy = np.asarray(nodes_xy, dtype=float)  # (N,2)\n        self.adj = adj                                     # list of neighbor indices\n        self.edge_dist = edge_dist                         # (E,) meters, matched with adj pairs\n        self.edge_vmax = edge_vmax                         # (E,) m\/s\n        self.default_vmax = float(default_vmax)\n\n    @staticmethod\n    def grid(x_min: float, x_max: float, y_min: float, y_max: float, step: float,\n             default_vmax: float = 15.0, eight_connected: bool = True) -> \"MobilityGraph\":\n        xs = np.arange(x_min, x_max + 1e-9, step, dtype=float)\n        ys = np.arange(y_min, y_max + 1e-9, step, dtype=float)\n        X, Y = np.meshgrid(xs, ys)\n        nodes = np.stack(&#91;X.ravel(), Y.ravel()], axis=1)\n        nx, ny = len(xs), len(ys)\n\n        def idx(ix, iy): return iy*nx + ix\n\n        adj: List&#91;List&#91;int]] = &#91;&#91;] for _ in range(nodes.shape&#91;0])]\n        edges = &#91;]\n        vmaxs = &#91;]\n        for iy in range(ny):\n            for ix in range(nx):\n                u = idx(ix, iy)\n                for dx, dy in &#91;(1,0),(0,1)] + (&#91;(1,1),(-1,1)] if eight_connected else &#91;]):\n                    jx, jy = ix+dx, iy+dy\n                    if 0 &lt;= jx &lt; nx and 0 &lt;= jy &lt; ny:\n                        v = idx(jx, jy)\n                        adj&#91;u].append(v)\n                        adj&#91;v].append(u)\n                        dist = np.linalg.norm(nodes&#91;v]-nodes&#91;u])\n                        edges.append(dist)\n                        vmaxs.append(default_vmax)\n        return MobilityGraph(nodes, adj, np.array(edges, dtype=float),\n                              np.array(vmaxs, dtype=float), default_vmax=default_vmax)\n\n# --------------------------\n# Loss helpers\n# --------------------------\n\ndef wrap_angle(a: float) -> float:\n    \"\"\"Wrap angle to &#91;-pi, pi].\"\"\"\n    a = (a + np.pi) % (2*np.pi) - np.pi\n    return a\n\ndef huber_loss(r: float, delta: float = 2.5) -> float:\n    \"\"\"Robustify extreme outliers (unitless residual).\"\"\"\n    r = float(r)\n    ad = abs(r)\n    if ad &lt;= delta:\n        return 0.5 * r * r\n    return delta * (ad - 0.5*delta)\n\n# --------------------------\n# Trajectory inference (beam-Viterbi on graph with observation model)\n# --------------------------\n\nclass TrajectoryInferrer:\n    def __init__(self, graph: MobilityGraph, sensors: Dict&#91;str, Sensor],\n                 dt: float = 5.0, beam_width: int = 256,\n                 vmax_hard: float = 40.0, stay_bias: float = 0.25,\n                 huber_delta: float = 3.0, c_mps: float = C_MPS):\n        self.G = graph\n        self.S = sensors\n        self.dt = float(dt)\n        self.beam_width = int(beam_width)\n        self.vmax_hard = float(vmax_hard)\n        self.stay_bias = float(stay_bias)\n        self.huber_delta = float(huber_delta)\n        self.c_mps = float(c_mps)\n\n    # ----------------------\n    # Public API\n    # ----------------------\n\n    def infer(self,\n              sightings: List&#91;RFSighting],\n              tdoas: Optional&#91;List&#91;TDoAMeasurement]] = None,\n              t0: float = 0.0, t1: float = 300.0,\n              window: float = 10.0) -> Dict&#91;str, np.ndarray]:\n        \"\"\"Return MAP path, per-step entropy proxy, and times.\"\"\"\n        t_axis = np.arange(t0, t1 + 1e-6, self.dt, dtype=float)\n        # Pre-index measurements by time for efficiency\n        s_by_t = self._bucketize(sightings, t_axis, window)\n        d_by_t = self._bucketize(tdoas or &#91;], t_axis, window)\n\n        # Initialize beam uniformly (or from first measurement peak if desired)\n        N = self.G.nodes_xy.shape&#91;0]\n        # Simple heuristic: seed beam by top-k nodes closest to first-pos sighting if available\n        init_scores = np.zeros(N, dtype=float)\n        if len(sightings) > 0 and any(s.xy is not None for s in sightings):\n            xy0 = min(&#91;s for s in sightings if s.xy is not None], key=lambda s: s.t).xy\n            d = np.linalg.norm(self.G.nodes_xy - xy0&#91;None,:], axis=1)\n            init_scores = 0.5*(d\/ (np.median(d)+1e-6))**2\n        init_beam = &#91;(init_scores&#91;i], i, -1) for i in np.argpartition(init_scores, self.beam_width)&#91;:self.beam_width]]\n\n        prev = {i: (cost, parent) for cost, i, parent in init_beam}\n        backptr: List&#91;Dict&#91;int, Tuple&#91;float,int]]] = &#91;]\n\n        for step, t in enumerate(t_axis):\n            # Expand\n            cand: Dict&#91;int, Tuple&#91;float,int]] = {}\n            for i, (cost_i, parent_i) in prev.items():\n                # Stay option\n                self._accumulate_candidate(cand, i, cost_i + self._trans_cost(i, i), i)\n                # Move options\n                for j in self.G.adj&#91;i]:\n                    tc = self._trans_cost(i, j)\n                    if tc == np.inf:\n                        continue\n                    self._accumulate_candidate(cand, j, cost_i + tc, i)\n\n            # Observation cost at this time\n            obs = s_by_t.get(step, &#91;])\n            dmeas = d_by_t.get(step, &#91;])\n            for j in list(cand.keys()):\n                oc = self._obs_cost(self.G.nodes_xy&#91;j], t, obs, dmeas)\n                cost_prev, p = cand&#91;j]\n                cand&#91;j] = (cost_prev + oc, p)\n\n            # Prune to beam\n            if len(cand) == 0:\n                # Fall back\u2014keep previous\n                cand = prev.copy()\n\n            top = sorted(cand.items(), key=lambda kv: kv&#91;1]&#91;0])&#91;:self.beam_width]\n            backptr.append({node:(cost,parent) for node,(cost,parent) in top})\n            prev = {node:(cost,parent) for node,(cost,parent) in top}\n\n        # Backtrack best\n        last_nodes = min(prev.items(), key=lambda kv: kv&#91;1]&#91;0])&#91;0]\n        path_idx = &#91;last_nodes]\n        for bp in reversed(backptr):\n            node = path_idx&#91;-1]\n            cost, parent = bp&#91;node]\n            if parent &lt; 0:\n                break\n            path_idx.append(parent)\n        path_idx = path_idx&#91;::-1]\n        path_xy = self.G.nodes_xy&#91;np.array(path_idx&#91;:len(t_axis)])]\n\n        # Entropy proxy: log-sum-exp of beam vs best at each step\n        ent = np.zeros(len(backptr), dtype=float)\n        for k, bp in enumerate(backptr):\n            costs = np.array(&#91;c for (_, (c,_)) in bp.items()])\n            m = np.min(costs)\n            ent&#91;k] = m + np.log(np.exp(-(costs - m)).sum())\n        ent = ent - np.min(ent)\n        return {\n            \"times\": t_axis,\n            \"path_xy\": path_xy,\n            \"entropy\": ent,\n            \"path_idx\": np.array(path_idx&#91;:len(t_axis)]),\n        }\n\n    # ----------------------\n    # Internals\n    # ----------------------\n\n    def _bucketize(self, items, t_axis, window):\n        buckets = {}\n        if items is None:\n            return buckets\n        half = window\/2.0\n        for it in items:\n            # find nearest time index\n            idx = int(np.clip(round((it.t - t_axis&#91;0]) \/ self.dt), 0, len(t_axis)-1))\n            # only assign if within window\n            t_center = t_axis&#91;idx]\n            if abs(it.t - t_center) &lt;= half:\n                buckets.setdefault(idx, &#91;]).append(it)\n        return buckets\n\n    def _trans_cost(self, i: int, j: int) -> float:\n        if i == j:\n            return self.stay_bias\n        dx = self.G.nodes_xy&#91;j] - self.G.nodes_xy&#91;i]\n        dist = float(np.linalg.norm(dx))\n        v = dist \/ max(self.dt, 1e-6)\n        if v > self.vmax_hard:\n            return np.inf\n        # soft penalty above default vmax\n        pen = max(0.0, v - self.G.default_vmax)\n        return 0.5 * (pen \/ (0.25*self.G.default_vmax + 1e-6))**2 + dist*1e-3  # small distance prior\n\n    def _accumulate_candidate(self, cand: Dict&#91;int, Tuple&#91;float,int]], node: int,\n                              cost: float, parent: int):\n        cur = cand.get(node, (np.inf, -1))\n        if cost &lt; cur&#91;0]:\n            cand&#91;node] = (cost, parent)\n\n    # Observation model\n    def _obs_cost(self, xy_node: np.ndarray, t: float,\n                  sightings: List&#91;RFSighting],\n                  tdoas: List&#91;TDoAMeasurement]) -> float:\n        cost = 0.0\n        # Rough fixes\n        for s in sightings:\n            if s.xy is not None and (s.sigma_pos is not None) and s.sigma_pos > 0:\n                r = np.linalg.norm(xy_node - s.xy) \/ s.sigma_pos\n                cost += huber_loss(r, self.huber_delta)\n\n            # AoA residual if available\n            if s.aoa_rad is not None and s.sensor_id is not None and s.sigma_aoa:\n                sens = self.S.get(s.sensor_id)\n                if sens is not None:\n                    vec = xy_node - sens.xy\n                    pred = math.atan2(vec&#91;1], vec&#91;0])\n                    da = wrap_angle(pred - s.aoa_rad) \/ max(s.sigma_aoa, 1e-6)\n                    w = 1.0\n                    if s.snr_db is not None:\n                        # map SNR to 0.5..2.0 weight\n                        w = float(np.clip(0.5 + 0.05*(s.snr_db - 10.0), 0.5, 2.0))\n                    cost += w * huber_loss(da, self.huber_delta)\n\n        # TDoA residuals\n        for d in tdoas:\n            si = self.S.get(d.sensor_i)\n            sj = self.S.get(d.sensor_j)\n            if (si is None) or (sj is None):\n                continue\n            # geometric time difference for node XY\n            ri = float(np.linalg.norm(xy_node - si.xy)) \/ self.c_mps + si.clock_bias_s\n            rj = float(np.linalg.norm(xy_node - sj.xy)) \/ self.c_mps + sj.clock_bias_s\n            tau_pred = ri - rj  # seconds\n            # residual (pred - measured)\n            res = (tau_pred - d.tdoa_s) \/ max(d.sigma_s, 1e-9)\n            cost += float(d.weight) * huber_loss(res, self.huber_delta)\n        return cost\n\"\"\")\n\ndemo_code = dedent(r\"\"\"\n# demo_rf_sequence_recovery_tdoa.py\n# Tiny synthetic demo: emit a path, generate AoA + TDoA, and recover.\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom rf_sequence_recovery_tdoa import (\n    MobilityGraph, Sensor, RFSighting, TDoAMeasurement, TrajectoryInferrer, C_MPS\n)\n\nrng = np.random.default_rng(7)\n\n# 1) Build grid graph\nG = MobilityGraph.grid(-2000, 2000, -2000, 2000, step=100.0, default_vmax=20.0)\n\n# 2) Sensors\nS = {\n    \"A\": Sensor(\"A\", np.array(&#91;-1500.0, 0.0])),\n    \"B\": Sensor(\"B\", np.array(&#91; 1500.0, 200.0])),\n    \"C\": Sensor(\"C\", np.array(&#91;  600.0,-1400.0])),\n}\n\n# 3) True path (smooth curve)\nT = 160\ndt = 2.0\nt_axis = np.arange(0, T*dt, dt)\npx = -1200 + 1800*np.sin(np.linspace(0, 2.5, len(t_axis)))\npy = -800 + 1200*np.sin(np.linspace(0, 1.25, len(t_axis)) + 0.5)\ntrue_xy = np.stack(&#91;px, py], axis=1)\n\n# helper: nearest grid node\ndef snap(xy):\n    d = np.linalg.norm(G.nodes_xy - xy&#91;None,:], axis=1)\n    return G.nodes_xy&#91;np.argmin(d)]\n\n# 4) Generate sightings (every ~8 steps)\nsightings = &#91;]\nfor k, t in enumerate(t_axis):\n    if k % 8 == 0:\n        # rough fix with 150m sigma\n        xy = true_xy&#91;k] + rng.normal(0, 150.0, size=2)\n        s = RFSighting(t=float(t), xy=xy, sigma_pos=150.0)\n        sightings.append(s)\n    if k % 5 == 0:\n        # AoA at a random sensor with 6deg sigma\n        sid = rng.choice(list(S.keys()))\n        sens = S&#91;sid]\n        vec = true_xy&#91;k] - sens.xy\n        aoa = np.arctan2(vec&#91;1], vec&#91;0]) + rng.normal(0, np.deg2rad(6.0))\n        sightings.append(RFSighting(t=float(t), sensor_id=sid, aoa_rad=float(aoa),\n                                    sigma_aoa=np.deg2rad(6.0), snr_db=15.0 + 10*rng.random()))\n\n# 5) Generate TDoA pairs (A-B, A-C) every ~4 steps\ntdoas = &#91;]\nsigma_t = 30e-9  # 30 ns  (~9 m range-diff sigma)\nfor k, t in enumerate(t_axis):\n    if k % 4 == 0:\n        for (i,j) in &#91;(\"A\",\"B\"),(\"A\",\"C\")]:\n            ri = np.linalg.norm(true_xy&#91;k] - S&#91;i].xy)\/C_MPS + S&#91;i].clock_bias_s\n            rj = np.linalg.norm(true_xy&#91;k] - S&#91;j].xy)\/C_MPS + S&#91;j].clock_bias_s\n            tau = ri - rj + rng.normal(0, sigma_t)\n            tdoas.append(TDoAMeasurement(t=float(t), sensor_i=i, sensor_j=j,\n                                         tdoa_s=float(tau), sigma_s=float(sigma_t), weight=1.0))\n\n# 6) Infer\ninf = TrajectoryInferrer(G, S, dt=dt, beam_width=256, vmax_hard=45.0)\nresult = inf.infer(sightings, tdoas, t0=float(t_axis&#91;0]), t1=float(t_axis&#91;-1]), window=6.0)\n\n# 7) Plot\nplt.figure(figsize=(8,7))\nplt.scatter(&#91;S&#91;k].xy&#91;0] for k in S], &#91;S&#91;k].xy&#91;1] for k in S], c=\"k\", marker=\"^\", label=\"Sensors\")\nfor sid in S:\n    plt.text(S&#91;sid].xy&#91;0]+20, S&#91;sid].xy&#91;1]+20, sid, fontsize=9)\n\nplt.plot(true_xy&#91;:,0], true_xy&#91;:,1], \"g-\", lw=2, label=\"True path\")\nplt.plot(result&#91;\"path_xy\"]&#91;:,0], result&#91;\"path_xy\"]&#91;:,1], \"b--\", lw=2, label=\"Inferred\")\n\n# rough fix points\nrx = &#91;s.xy&#91;0] for s in sightings if s.xy is not None]\nry = &#91;s.xy&#91;1] for s in sightings if s.xy is not None]\nplt.scatter(rx, ry, c=\"orange\", s=20, alpha=0.6, label=\"Rough fixes\")\n\nplt.axis(\"equal\")\nplt.grid(True, ls=\"--\", alpha=0.4)\nplt.legend()\nplt.title(\"Trajectory inference with AoA + TDoA residuals\")\nplt.tight_layout()\nplt.savefig(\"rf_sequence_tdoa_demo.png\")\n\n# Uncertainty plot\nimport matplotlib.ticker as mt\nplt.figure(figsize=(8,3))\nplt.plot(result&#91;\"times\"]&#91;:len(result&#91;\"entropy\"])], result&#91;\"entropy\"], \"m-\")\nplt.gca().yaxis.set_major_locator(mt.MaxNLocator(4))\nplt.grid(True, ls=\"--\", alpha=0.4)\nplt.xlabel(\"Time (s)\")\nplt.ylabel(\"Entropy (arb.)\")\nplt.title(\"Entropy proxy over time (lower = more confident)\")\nplt.tight_layout()\nplt.savefig(\"rf_sequence_tdoa_entropy.png\")\nprint(\"Saved: rf_sequence_tdoa_demo.png, rf_sequence_tdoa_entropy.png\")\n\"\"\")\n\nPath(\"\/mnt\/data\/rf_sequence_recovery_tdoa.py\").write_text(module_code)\nPath(\"\/mnt\/data\/demo_rf_sequence_recovery_tdoa.py\").write_text(demo_code)\nprint(\"Wrote \/mnt\/data\/rf_sequence_recovery_tdoa.py and demo_rf_sequence_recovery_tdoa.py\")<\/code><\/pre>\n\n\n\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"http:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/08\/NVIDIA-Hopper-Architecture-through-Microbenchmarking-and-Multiple-Level-Analysis-2501.12084v1.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis 2501.12084v1.\"><\/object><a id=\"wp-block-file--media-e8d42c2b-0120-442f-a57d-8894de135f22\" href=\"http:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/08\/NVIDIA-Hopper-Architecture-through-Microbenchmarking-and-Multiple-Level-Analysis-2501.12084v1.pdf\">NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis 2501.12084v1<\/a><a href=\"http:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/08\/NVIDIA-Hopper-Architecture-through-Microbenchmarking-and-Multiple-Level-Analysis-2501.12084v1.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-e8d42c2b-0120-442f-a57d-8894de135f22\">Download<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>1. Stakeholder Communication Tool Instead of raw math, you\u2019ve now got a demo deck that can go in front of: 2. Experimentation Sandbox This lets you prove robustness before deploying into messy production workloads. 3. Productization Hook Your harness is now a demo-able feature: 4. Competitive Differentiator Most schedulers in the wild (e.g. Kubernetes +&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=2989\" rel=\"bookmark\"><span class=\"screen-reader-text\">SCYTHE GPU Possession Scheduler<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":100,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[10],"tags":[],"class_list":["post-2989","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/2989","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2989"}],"version-history":[{"count":2,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/2989\/revisions"}],"predecessor-version":[{"id":2992,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/2989\/revisions\/2992"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/100"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2989"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2989"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2989"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}