Initial commit: obsidian to gitea

phd/weekly-report/25/251207.md

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+Objectives
+- Auto LLM inference config tuner
+
+Key Results
+- [7/10] Build the first version auto tuner system
+- [7/10] Check the current situation of parallelism config optimization
+- [4/10] Understand the possibility/challenges in LLM inference compute graph arrangement automatically
+- [0/10] Trace vLLM compute graph and data flow
+- [3/10] Implement a minimal Rust inference framework
+- [1/10] Define the IR for automatic optimization
+- [5/10] Profile different parallelism setup with real trace and analysis their difference
+- [0/10] Meta-analysis for the theory maximum improvement with heterogenous setup [offtrack]
+
+Last Week
+- [KR1] Update workload generator from real workload, give a more precise spec abstraction. [c969f366](https://ipads.se.sjtu.edu.cn:1312/wangjh/auto-tuner/-/commit/c969f366b05cad03447e1d7bdd9f30785dd792e4)~[7407149d](https://ipads.se.sjtu.edu.cn:1312/wangjh/auto-tuner/-/commit/7407149d1052d3d610fd1fb3e51ce60068ba4981)
+- [KR1] Benchmark and compare for generated workloads and raw workloads. Find that if input/output length are generated, will cause the performance varies a lot.
+
+Next Week
+- Find the root cause for workload performance variation.
+- Summary the intelligence for auto tuning path.