obsidian/phd/weekly-report/25/251228.md

Objectives

• Auto LLM inference config tuner

Key Results

• [2/10] Workload grouping methods
• [9/10] Build the first version auto tuner system
• [8/10] Check the current situation of parallelism config optimization
• [4/10] Understand the possibility/challenges in LLM inference compute graph arrangement automatically
• [1/10] Define the IR for automatic optimization
• [5/10] Profile different parallelism setup with real trace and analysis their difference

Last Week

• [KR1] Run benchmark for different workload classifications and prove that different classification method will shift the best config and different workload groups need different configs to maximize the goodput.
• [misc] Prepare for IPADS group meeting presentation.
• [misc] Prepare for the ChinaSys presentation.

Next Week

• Define the workload classification space and find the method to group workload.