# pipascope

**Repository Path**: bernard5/pipascope

## Basic Information

- **Project Name**: pipascope
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-09-03
- **Last Updated**: 2025-09-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PipaScope: A Dataset for CPU Microarchitecture Performance Characterization

> **PipaScope** – Observe the pulse of performance, one cycle at a time.  
> An open dataset initiative for microarchitectural behavior analysis, led by **ZJU-SPAIL**.

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
[![Git LFS](https://img.shields.io/badge/Git%20LFS-enabled-ff69b4.svg)](https://git-lfs.com)



## 🍈 About the Name: PIPA & PipaScope

**PIPA (Progressive Intelligent Performance Analytics)** draws inspiration from *loquat* (枇杷), a fruit native to Zhejiang, China. Its lifecycle—tree (collecting), flower (analysis), and fruit (conclusion)—mirrors the performance engineering pipeline.

**PipaScope** extends this metaphor as the **observational lens** into the microarchitectural world. Just as the loquat tree absorbs nutrients from the soil, PipaScope captures low-level performance telemetry from real workloads, enabling deep insight into CPU behavior.

This dataset serves as the foundational **"soil"** for training automated performance diagnosis systems.

## 🏫 Project Ownership

PipaScope is currently led and maintained by the System Performance Analytics and Intelligence Lab (ZJU-SPAIL) at Zhejiang University.  
It is part of ongoing research into systematic performance characterization and bottleneck analysis.


## 🎯 Focus: Microarchitectural Behavior

PipaScope is designed to support research in **CPU microarchitecture performance characterization**, with a focus on:

- Instruction per Cycle (IPC) degradation
- Cache miss patterns (L1/L2/LLC)
- Memory bandwidth saturation
- Frontend/backend stalls
- Branch misprediction penalties
- TLB pressure

The goal is to build a high-quality, version-controlled dataset that enables reproducible analysis and lays the foundation for automated bottleneck identification.

## 🧩 Data Sources

The dataset includes performance profiles from:

- **SPEC CPU 2017** (both integer and floating-point benchmarks)
- **Real-world applications**, starting with **RocksDB**

Each workload is executed under diverse configurations (input sets, system settings, compiler flags) and on multiple hardware platforms (Intel/Arm) to capture a wide range of microarchitectural behaviors.


## 🛠️ Data Collection

All data is collected using standardized tools and methodologies:

- **perf** (Linux Performance Events) for hardware counter sampling
- Custom **run scripts** for SPEC CPU 2017 and real-world applications
- Metric derivation based on **PIPA-SHU** principles (multiplexing-aware counter aggregation)

All data is versioned using **Git LFS** to support large file storage and traceability.


## 📌 Status

This project is in the **early development phase**.  
The dataset is actively being built by ZJU-SPAIL members.  
Public access is read-only; contributions are not currently accepted.

Documentation and tooling will be expanded as the dataset matures.

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> *“PipaScope: where data grows like fruit, and insight blossoms from observation.”*