config-optimizer

name	config-optimizer
description	Use this skill to find the optimal Sirius configuration for TPC-H workloads at any scale factor. Trigger when the user wants to tune performance, optimize config parameters, find the best thread count, batch size, or cache mode, or benchmark different Sirius configurations against each other. Also use when the user mentions "config tuning", "parameter sweep", or "optimal settings".
disable-model-invocation	true

Sirius Config Optimizer

Tune Sirius configuration parameters for optimal TPC-H performance at various scale factors. Systematically explore the configuration space, evaluate each configuration with profiling, and report the best settings.

Reference: See .claude/skills/_shared/build-and-query.md for shared infrastructure (build modes, query execution).

Configuration Parameters

Parameter	Range	Description
sirius.executor.pipeline.num_threads	1–10	Number of threads (streams) for GPU pipeline tasks
sirius.executor.duckdb_scan.num_threads	1–10	Number of threads for DuckDB scan operator
sirius.executor.duckdb_scan.cache	none, parquet, table_gpu, table_host	Cache mode: none, parquet row groups in pinned memory, scanned table on GPU, scanned table in pinned memory
sirius.operator_params.scan_task_batch_size	500MB–5GB	Batch size in bytes for scan tasks
sirius.operator_params.concat_batch_bytes	500MB–5GB	Batch size in bytes for concatenation tasks
sirius.operator_params.hash_partition_bytes	500MB–5GB	Batch size in bytes for hash partition tasks

Workflow

1. Gather context:

   • Ask the user for the baseline configuration file (or check $SIRIUS_CONFIG_FILE)
   • Ask for target TPC-H scale factor and dataset location
   • If the dataset doesn't exist, use the /dataset-manager skill to generate it

2. Establish baseline:

   • Use the /profile-analyzer skill to evaluate baseline performance
   • Collect metrics: query latency, GPU utilization, memory usage

3. Explore the configuration space using a systematic approach (grid search, random search, or Bayesian optimization). For each configuration:

   • Update the config file using scripts/patch_config.py
   • Run the same TPC-H queries and collect performance metrics
   • Compare against baseline and previous configurations
   • Back up the configuration and results for each test

4. Analyze results to identify the optimal configuration.

5. Report the optimal configuration and its performance metrics, along with insights.

Changing Configuration

Use scripts/patch_config.py to modify parameters:

cd scripts
pixi run python patch_config.py sirius.cfg \
    --opt sirius.executor.pipeline.num_threads=4 \
    --opt sirius.executor.duckdb_scan.cache=parquet \
    --opt sirius.operator_params.scan_task_batch_size=536870912

Arguments:

• config_file_path — Path to the Sirius configuration file
• --opt — Key-value pair for the config parameter

Examples

# Example 1: Tune for SF10
# User: "Find the best config for TPC-H SF10"
# 1. Generate data if needed
# 2. Run baseline with current config
# 3. Sweep thread counts: 1, 2, 4, 6, 8
# 4. Sweep cache modes: none, parquet, table_gpu
# 5. Fine-tune batch sizes around best thread/cache combo

# Example 2: Compare two specific configs
# User: "Compare 4 threads vs 8 threads on SF100"
# 1. Run SF100 queries with num_threads=4, collect timings
# 2. Run SF100 queries with num_threads=8, collect timings
# 3. Present comparison table with speedup ratios

Before Running

• Ask the user if they want to rebuild the code if there are upstream changes
• Build with pixi: pixi run make release
• Ensure SIRIUS_CONFIG_FILE is set and points to a valid config file

Output

Create an optimization report with:

• Baseline configuration and performance metrics
• Each configuration tested, parameters changed, and resulting metrics
• Optimal configuration saved to optimal_sirius.cfg
• Performance report saved to optimal_config_report.txt
• Insights and recommendations for future tuning