NASP/cuda-samples
63
0
Fork 0
mirror of https://github.com/NVIDIA/cuda-samples.git synced 2026-05-14 14:06:53 +08:00
Code Issues Packages Projects Releases Wiki Activity
cuda-samples/python/2_CoreConcepts/pageRank
History
Dheemanth aeab82ff30
CUDA 13.2 samples update (#432) 
- Added Python samples for CUDA Python 1.0 release
- Renamed top-level `Samples` directory to `cpp` to accommodate Python samples.
2026-05-13 17:13:18 -05:00
..
pageRank.py
CUDA 13.2 samples update (#432)
2026-05-13 17:13:18 -05:00
README.md
CUDA 13.2 samples update (#432)
2026-05-13 17:13:18 -05:00
requirements.txt
CUDA 13.2 samples update (#432)
2026-05-13 17:13:18 -05:00

README.md

Sample: PageRank Algorithm (Python)

Description

Demonstrates GPU-accelerated PageRank computation for graph analysis using RAPIDS cuGraph, with cuda.core for device, stream, and GPU timing. This sample focuses on cuda.core integration with high-level libraries (cuGraph/cuDF); for custom kernel programming (Program, LaunchConfig, launch), see the blockwiseSum sample.

What You'll Learn

  • Graph representation using cuDF DataFrames for edge lists
  • GPU-optimized PageRank via RAPIDS cuGraph library
  • Performance comparison between cuGraph GPU and CPU reference implementation
  • cuda.core device/stream management and GPU timing

Key Libraries

  • cugraph - RAPIDS GPU-accelerated graph analytics
  • cudf - RAPIDS GPU DataFrame library
  • cuda.core - Device, stream, and event APIs for GPU timing
  • cupy - GPU array library (ExternalStream for cuDF/cuGraph)
  • numpy - CPU reference implementation

Key APIs

From cuda.core:

  • Device(0) - Create device, device.set_current(), device.create_stream()
  • EventOptions(enable_timing=True) - GPU timing via stream.record()
  • cp.cuda.ExternalStream(stream.handle).use() - Make cuDF/cuGraph use cuda.core stream

From cuGraph:

  • cugraph.Graph(directed=True) - Create directed graph structure
  • Graph.from_cudf_edgelist() - Build graph from edge list DataFrame
  • cugraph.pagerank() - GPU-accelerated PageRank algorithm

From cuDF:

  • cudf.DataFrame() - GPU DataFrame for edge lists

Requirements

Hardware:

  • NVIDIA GPU with Compute Capability 7.0 or higher
  • Minimum GPU memory: 512 MB (for 10K node graph)

Software:

  • CUDA Toolkit 13.0 or newer
  • Python 3.10 or newer
  • See requirements.txt for package dependencies

Installation

cd /path/to/cuda-samples/python/2_CoreConcepts/pageRank
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

How to Run

python pageRank.py

Algorithm

The PageRank formula iteratively computes node importance:

PR(v) = (1-d)/N + d * Σ PR(u)/out_degree(u)

Where:

  • d = damping factor (typically 0.85)
  • N = total number of nodes
  • Sum is over all nodes u that link to v

Expected Output

============================================================
PageRank Algorithm (using RAPIDS cuGraph)
============================================================

Device: NVIDIA GeForce RTX ...
Compute Capability: sm_XX

Graph Parameters:
  Nodes: 10,000
  Avg edges/node: 15
  Total edges: ~150,000
  Avg in-degree: 14.9

------------------------------------------------------------
GPU PageRank (RAPIDS cuGraph)
------------------------------------------------------------
Time: X.XXX ms

Top 5 nodes by PageRank:
  1. Node XXXXX: 0.XXXXXX
  ...

------------------------------------------------------------
CPU PageRank (Reference)
------------------------------------------------------------
Time: XXXX.XXX ms
Iterations: XX

------------------------------------------------------------
PERFORMANCE SUMMARY
------------------------------------------------------------
GPU (cuGraph): X.XXX ms
CPU (Reference): XXXX.XXX ms
Speedup: XXXX.Xx

------------------------------------------------------------
VERIFICATION
------------------------------------------------------------
GPU vs CPU PageRank scores: Test PASSED

PageRank Properties:
  Sum of scores: 1.000000 (should be ~1.0)
  Sum check: ✓

Done

Files

  • pageRank.py - Python implementation using RAPIDS cuGraph
  • README.md - This file
  • requirements.txt - Sample dependencies

Why cuGraph?

RAPIDS cuGraph provides production-grade, GPU-accelerated graph analytics:

  • Highly optimized - Uses advanced GPU parallelization techniques
  • Scalable - Handles graphs with billions of edges
  • Easy to use - Simple Python API similar to NetworkX
  • Integrated - Works seamlessly with cuDF, cuML, and other RAPIDS libraries

Applications

  • Web page ranking (original Google PageRank)
  • Social network influence analysis
  • Citation network analysis
  • Recommendation systems
  • Fraud detection in financial networks

See Also