docs.md

Kernelbot Data Processing Skills

This document describes how to extract and process submission data from the Kernelbot database.

Database Connection

The production database is hosted on Heroku. NEVER run write operations (INSERT, UPDATE, DELETE) on this database.

# Get DATABASE_URL from Heroku
heroku config:get DATABASE_URL --app discord-cluster-manager

Database Schema

The relevant tables are in the leaderboard schema:

Table	Description
leaderboard.leaderboard	Problem definitions (id, name, deadline, task, description)
leaderboard.submission	User submissions (id, leaderboard_id, user_id, code_id, submission_time, status)
leaderboard.runs	Execution results (submission_id, score, passed, mode, runner, result)
leaderboard.user_info	User details (id, user_name)
leaderboard.gpu_type	GPU types per problem (leaderboard_id, gpu_type)
leaderboard.code_files	Actual submission code content (old_code text, code bytea)

Key Problem IDs

NVFP4 Problems

• 595: nvfp4_gemv
• 597: nvfp4_gemm
• 598: nvfp4_dual_gemm
• 730: nvfp4_group_gemm (not released yet)

AMD Problems

• 398: amd-identity
• 399: amd-fp8-mm
• 430: amd-mixture-of-experts
• 463: amd-mla-decode
• 563: amd-all2all
• 564: amd-gemm-rs
• 565: amd-ag-gemm

Run Modes

Mode	Description	Has Score?
test	Correctness tests	No
benchmark	Performance benchmarks (internal)	No
leaderboard	Official leaderboard runs	Yes
profile.0-3	Profiling runs	No

Important:

• Use mode = 'leaderboard' when joining runs to get scores.
• Lower scores are better (scores are execution time in seconds).

SQL Queries

All SQL queries are in queries.sql. Key queries:

• List all problems
• Check submission counts
• Export deduplicated submissions with code
• Get top N submissions
• Get user progression over time

Adding Support for a New Problem

Step 1: Find the Problem ID

Use the "LIST ALL PROBLEMS" query from queries.sql.

Step 2: Check Submission Counts

Use the "CHECK SUBMISSION COUNTS" query from queries.sql.

Step 3: Export Deduplicated Submissions

Use the "EXPORT DEDUPLICATED SUBMISSIONS WITH CODE" query from queries.sql.

import pandas as pd
import psycopg2

DATABASE_URL = "..."  # from heroku config:get
conn = psycopg2.connect(DATABASE_URL)

# Read query from queries.sql and modify problem IDs as needed
with open('queries.sql') as f:
    # Find and use the export query section
    pass

df = pd.read_sql(query, conn)
df.to_parquet('new_problem_submissions.parquet', index=False)

Step 4: Verify Data Quality

from analyze_submissions import load_submissions, leaderboard_summary

df = load_submissions('new_problem_submissions.parquet')
print(leaderboard_summary(df))

Accessing Submission Code

The parquet files include the full code content for each submission:

from analyze_submissions import load_submissions

df = load_submissions()

# Get a specific user's best submission
user_subs = df[(df['user_name'] == 'gau.nernst') & (df['problem_name'] == 'nvfp4_gemv')]
best = user_subs.sort_values('score').head(1)

# Access the code
code = best['code'].values[0]
print(code)

Helper Functions

Use analyze_submissions.py:

from analyze_submissions import (
    load_submissions,      # Load parquet file
    author_progression,    # See user's submissions over time
    top_contestants,       # Get leaderboard rankings
    leaderboard_summary,   # Summary stats per problem
    user_stats,            # Stats for a specific user
    format_score           # Format score with units (us, ms, s)
)

Environment Setup

uv venv .venv
source .venv/bin/activate
uv pip install pandas pyarrow psycopg2-binary

Files

File	Description
nvidia_nvfp4_submissions.parquet	Deduplicated NVIDIA NVFP4 submissions with code (~1.4 GB)
queries.sql	All SQL queries for data extraction
scripts/nvfp4/analyze_submissions.py	Helper functions library
scripts/nvfp4/get_fastest_submission.py	Print user's fastest submission
scripts/nvfp4/query_submissions.py	List submission IDs or query specific ID

Review Checklist Before Pushing

1. Verify submission counts match expectations
2. Check for any anomalies in scores (negative, extremely large, etc.)
3. Confirm deduplication worked correctly
4. Test helper functions work with the new data
5. Run python scripts/nvfp4/query_submissions.py to verify