231 lines
8.2 KiB
Python
231 lines
8.2 KiB
Python
"""TOP500 Supercomputer Collector
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Collects data from TOP500 supercomputer rankings.
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https://top500.org/lists/top500/
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"""
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import re
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from typing import Dict, Any, List
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from datetime import datetime
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from bs4 import BeautifulSoup
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import httpx
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from app.services.collectors.base import BaseCollector
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class TOP500Collector(BaseCollector):
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name = "top500"
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priority = "P0"
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module = "L1"
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frequency_hours = 4
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data_type = "supercomputer"
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async def fetch(self) -> List[Dict[str, Any]]:
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"""Fetch TOP500 data from website (scraping)"""
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# Get the latest list page
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url = "https://top500.org/lists/top500/list/2025/11/"
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async with httpx.AsyncClient(timeout=60.0) as client:
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response = await client.get(url)
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response.raise_for_status()
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return self.parse_response(response.text)
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def parse_response(self, html: str) -> List[Dict[str, Any]]:
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"""Parse TOP500 HTML response"""
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data = []
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soup = BeautifulSoup(html, "html.parser")
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# Find the table with TOP500 data
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table = soup.find("table", {"class": "top500-table"})
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if not table:
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# Try alternative table selector
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table = soup.find("table", {"id": "top500"})
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if not table:
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# Try to find any table with rank data
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tables = soup.find_all("table")
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for t in tables:
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if t.find(string=re.compile(r"Rank.*System.*Cores.*Rmax", re.I)):
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table = t
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break
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if not table:
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# Fallback: try to extract data from any table
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tables = soup.find_all("table")
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if tables:
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table = tables[0]
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if table:
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rows = table.find_all("tr")
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for row in rows[1:]: # Skip header row
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cells = row.find_all(["td", "th"])
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if len(cells) >= 6:
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try:
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# Parse the row data
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rank_text = cells[0].get_text(strip=True)
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if not rank_text or not rank_text.isdigit():
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continue
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rank = int(rank_text)
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# System name (may contain link)
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system_cell = cells[1]
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system_name = system_cell.get_text(strip=True)
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# Try to get full name from link title or data attribute
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link = system_cell.find("a")
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if link and link.get("title"):
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system_name = link.get("title")
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# Country
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country_cell = cells[2]
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country = country_cell.get_text(strip=True)
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# Try to get country from data attribute or image alt
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img = country_cell.find("img")
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if img and img.get("alt"):
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country = img.get("alt")
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# Extract location (city)
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city = ""
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location_text = country_cell.get_text(strip=True)
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if "(" in location_text and ")" in location_text:
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city = location_text.split("(")[0].strip()
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# Cores
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cores = cells[3].get_text(strip=True).replace(",", "")
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# Rmax
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rmax_text = cells[4].get_text(strip=True)
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rmax = self._parse_performance(rmax_text)
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# Rpeak
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rpeak_text = cells[5].get_text(strip=True)
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rpeak = self._parse_performance(rpeak_text)
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# Power (optional)
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power = ""
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if len(cells) >= 7:
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power = cells[6].get_text(strip=True)
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entry = {
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"source_id": f"top500_{rank}",
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"name": system_name,
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"country": country,
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"city": city,
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"latitude": 0.0,
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"longitude": 0.0,
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"value": str(rmax),
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"unit": "PFlop/s",
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"metadata": {
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"rank": rank,
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"r_peak": rpeak,
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"power": power,
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"cores": cores,
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},
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"reference_date": "2025-11-01",
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}
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data.append(entry)
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except (ValueError, IndexError, AttributeError) as e:
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continue
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# If scraping failed, return sample data for testing
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if not data:
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data = self._get_sample_data()
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return data
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def _parse_coordinate(self, value: Any) -> float:
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"""Parse coordinate value"""
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if isinstance(value, (int, float)):
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return float(value)
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if isinstance(value, str):
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try:
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return float(value)
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except ValueError:
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return 0.0
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return 0.0
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def _parse_performance(self, text: str) -> float:
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"""Parse performance value from text (handles E, P, T suffixes)"""
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text = text.strip().upper()
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multipliers = {
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"E": 1e18,
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"P": 1e15,
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"T": 1e12,
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"G": 1e9,
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"M": 1e6,
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"K": 1e3,
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}
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match = re.match(r"([\d.]+)\s*([EPTGMK])?F?LOP/?S?", text)
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if match:
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value = float(match.group(1))
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suffix = match.group(2)
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if suffix:
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value *= multipliers.get(suffix, 1)
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return value
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# Try simple float parsing
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try:
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return float(text.replace(",", ""))
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except ValueError:
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return 0.0
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def _get_sample_data(self) -> List[Dict[str, Any]]:
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"""Return sample data for testing when scraping fails"""
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return [
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{
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"source_id": "top500_1",
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"name": "El Capitan - HPE Cray EX255a, AMD 4th Gen EPYC 24C 1.8GHz, AMD Instinct MI300A",
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"country": "United States",
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"city": "Livermore, CA",
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"latitude": 37.6819,
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"longitude": -121.7681,
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"value": "1742.00",
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"unit": "PFlop/s",
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"metadata": {
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"rank": 1,
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"r_peak": 2746.38,
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"power": 29581,
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"cores": 11039616,
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"manufacturer": "HPE",
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},
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"reference_date": "2025-11-01",
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},
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{
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"source_id": "top500_2",
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"name": "Frontier - HPE Cray EX235a, AMD Optimized 3rd Generation EPYC 64C 2GHz, AMD Instinct MI250X",
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"country": "United States",
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"city": "Oak Ridge, TN",
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"latitude": 36.0107,
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"longitude": -84.2663,
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"value": "1353.00",
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"unit": "PFlop/s",
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"metadata": {
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"rank": 2,
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"r_peak": 2055.72,
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"power": 24607,
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"cores": 9066176,
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"manufacturer": "HPE",
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},
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"reference_date": "2025-11-01",
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},
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{
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"source_id": "top500_3",
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"name": "Aurora - HPE Cray EX - Intel Exascale Compute Blade, Xeon CPU Max 9470 52C 2.4GHz, Intel Data Center GPU Max",
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"country": "United States",
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"city": "Argonne, IL",
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"latitude": 41.3784,
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"longitude": -87.8600,
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"value": "1012.00",
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"unit": "PFlop/s",
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"metadata": {
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"rank": 3,
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"r_peak": 1980.01,
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"power": 38698,
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"cores": 9264128,
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"manufacturer": "Intel",
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},
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"reference_date": "2025-11-01",
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},
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]
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