LibAMM Benchmark Suite

LibAMM is the DataSys benchmark suite for Approximate Matrix Multiplication (AMM). The framework-neutral algorithm implementations live in AMM-Algorithms. The existing isage-amms package name and sage.libs.amms import path are retained for compatibility.

Overview

This repository contains the benchmark suite for evaluating Approximate Matrix Multiplication (AMM) algorithms. It provides comprehensive performance evaluation tools, scripts, and datasets.

🎯 Purpose

This is a benchmark-only repository. For AMM algorithm implementations, please refer to:

• Algorithm library: DataSysResearch/AMM-Algorithms
• PyPI Package: pip install isage-amms

📂 Structure

benchmark/
├── scripts/          # Evaluation scripts
├── config.csv        # Benchmark configurations
├── perfLists/        # Performance metric lists
└── src/              # Benchmark runner code

tools/
├── data_manager.py   # Dataset management
└── setup_data.sh     # Dataset setup script

🚀 Quick Start

Prerequisites

• Python 3.8+
• AMM algorithms package: pip install isage-amms

Run Benchmarks

cd benchmark
python scripts/run_benchmark.py --config config.csv

📊 Benchmark Types

1. End-to-End (E2E): Full pipeline evaluation
2. Downstream Tasks: PCA, CCA, DNN inference, QCD
3. Performance Profiling: CPU cycles, instructions, energy
4. Scalability: Thread scaling, batch size, event rate

🔗 Ecosystem

• DataSys owns this benchmark and the framework-neutral AMM implementation library.
• SAGE consumes AMM capabilities through its application and orchestration interfaces.
• IntelliStream incubated the project before its graduation to DataSys.

📖 Documentation

See benchmark/README.md for detailed benchmark documentation.

📄 License

Apache License 2.0 - see LICENSE file.