mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2026-05-14 14:06:53 +08:00
aeab82ff30
- Added Python samples for CUDA Python 1.0 release - Renamed top-level `Samples` directory to `cpp` to accommodate Python samples.
4.2 KiB
4.2 KiB
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 analyticscudf- RAPIDS GPU DataFrame librarycuda.core- Device, stream, and event APIs for GPU timingcupy- 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 viastream.record()cp.cuda.ExternalStream(stream.handle).use()- Make cuDF/cuGraph use cuda.core stream
From cuGraph:
cugraph.Graph(directed=True)- Create directed graph structureGraph.from_cudf_edgelist()- Build graph from edge list DataFramecugraph.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
uthat link tov
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 cuGraphREADME.md- This filerequirements.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