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phd/weekly-report/25/251221.md

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+Objectives
+- Auto LLM inference config tuner
+
+Key Results
+- [9/10] 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
+- Refine the story. Focus on heterogenous workloads are classified by labels or input length, which is not enough. We should define a classification method through the grouping of similar performance under the same config.
+- Prepare slides to summarize the story and what to do next.
+- Prepare slides for IPADS group meeting.
+
+Next Week
+- Run benchmark for current workload classification to prove different classes need different configs to max the goodput.