Objectives
- Auto LLM inference config tuner

Key Results
- [6/10] 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
- [KR2] Benchmark different configs in different hardware, prove that different hardware and different workload will cause different trends of performance change. [5f2c1ec3](https://ipads.se.sjtu.edu.cn:1312/wangjh/auto-tuner/-/commit/5f2c1ec3692586031f3ecd452709a034d8217113) ~ [65d05520](https://ipads.se.sjtu.edu.cn:1312/wangjh/auto-tuner/-/commit/65d0552020041d5922e13172d9c40f8ef93a3985)
- [KR1] Build a precise workload generator from real workload. Benchmark on _quite similar_ generated workloads and find that even the similar workloads still trigger different performance.

Next Week
- Find the root cause of performance gap under similar workloads.