A testcase generation tool for Persistent Memory Programs.

Overview

PMFuzz

PMFuzz

PMFuzz is a testcase generation tool to generate high-value tests cases for PM testing tools (XFDetector, PMDebugger, PMTest and Pmemcheck)

If you find PMFuzz useful in your research, please cite:

Sihang Liu, Suyash Mahar, Baishakhi Ray, and Samira Khan
PMFuzz: Test Case Generation for Persistent Memory Programs
The International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2021

BibTex

@inproceedings{liu2021pmfuzz,
  title={PMFuzz: Test Case Generation for Persistent Memory Programs},
  author={Liu, Sihang and Mahar, Suyash and Ray, Baishakhi and Khan, Samira},
  booktitle={Proceedings of the Twenty-sixth International Conference on Architectural Support for Programming Languages and Operating Systems},
  year={2021}
}

Dependencies

PMFuzz was tested using the following environment configuration, other versions may work:

  1. Ubuntu 18.04
  2. NDCTL v64 or higher
  3. libunwind (libunwind-dev)
  4. libini-config (libini-config-dev)
  5. Python 3.8
  6. GNUMake >= 3.82
  7. Kernel version 5.4
  8. Anaconda or virtualenv (recommended)

For compiling documentation:

  1. doxygen
  2. pdflatex
  3. doxypypy

Compiling PMFuzz

Build PMFuzz and AFL

make -j $(nproc --all)

Install PMFuzz

sudo make install

Now, pmfuzz-fuzz should be available as an executable:

pmfuzz-fuzz --help

The following man pages are also installed:

man 1 pmfuzz-fuzz
man 7 libpmfuzz
man 7 libfakepmfuzz

To uninstall PMFuzz, run the following command:

sudo make uninstall

Compiling PMFuzz Docker image

PMFuzz also comes with a docker file to automatically configure and install pmfuzz. To build the image, run the following command from the root of the repository:

docker build -t pmfuzz-v0.9 .

The raw dockerfile is also available here: /Dockerfile.

Using PMFuzz

After installing PMFuzz, use annotations by including the PMFuzz header file:

#include "pmfuzz/pmfuzz.h"

int main() {
	printf("PMFuzz version: %s\n", pmfuzz_version_str);
}

The program would then have to be linked with either libpmfuzz or libfakepmfuzz. e.g.,

example: example.o
	$(CXX) -o $@ $< -lfakepmfuzz # or -lpmfuzz

To compile a program linked with libpmfuzz, you'd need to use PMFuzz's AFL++ version of gcc/clang. Check build/bin after building PMFuzz.

For debugging, libfakepmfuzz exports the same interface but no actual tracking mechanism, allowing it to compile with any C/C++ compiler.

An example program is available in src/example. The original ASPLOS 2021 artifact is available at https://github.com/Systems-ShiftLab/pmfuzz_asplos21_ae.

libpmfuzz API is available at docs/libpmfuzz.7.md

Compiling Documentation

Run make docs from the root, and all the documentation will be linked in the docs/ directory.

Some man pages are available as markdown formatted files:

  1. docs/libpmfuzz.7.md
  2. docs/pmfuzz-fuzz.1.md

Running custom configuration

PMFuzz uses a YML based configuration to set different parameters for fuzzing, to write a custom configuration, please follow one of the existing examples in src/pmfuzz/configs/examples/ directory.

More information on PMFuzz's syntax is here.

Modifying PMFuzz

PMFuzz was written in a modular way allowing part of PMFuzz's components to be swapped with something that has the same interface. If you have a question please open a new issue or a discussion.

Other useful information

Env variables

NOTE: If a variable doesn't have a possible value next to it, that variable would be enabled by setting it to any non-empty value (including 0).

  1. USE_FAKE_MMAP=(0,1): Enables fake mmap which mounts an image in the volaile memory.
  2. PMEM_MMAP_HINT=<addr>: Address of the mount point of the pool.
  3. ENABLE_CNST_IMG=(0,1): Disables default PMDK's behaviour that generates non-identical images for same input.
  4. FI_MODE=(<empty or unset>|IMG_GEN|IMG_REP): See libpmfuzz.c
  5. FAILURE_LIST=<path-to-output-file>: See libpmfuzz.c
  6. PMFUZZ_DEBUG=(0,1): Enables debug output from libpmfuzz
  7. ENABLE_PM_PATH: Enables deep paths in PMFuzz
  8. GEN_ALL_CS: Partially disables the probabilistic generation of crash sites and more of them are generated from libpmfuzz.c
  9. IMG_CREAT_FINJ: Disables the probabilistic generation of crash sites and all of them are generated from libpmfuzz.c
  10. PMFUZZ_SKIP_TC_CHECK: Disable testcase size check in AFL++
  11. PRIMITIVE_BASELINE_MODE: Makes workload delete image on start if the pool exists

Adding git hook for development

Following command adds a pre-commit hook to check if the tests pass:

git config --local core.hooksPath .githooks/

Reasons for Common errors

1. FileNotFoundError for instance's pid file

Raised when AFL cannot bind to a free core or no core is free.

2. Random tar command failed

Check if no free disk space is left on the device

3. shmget (2): No space left on device

Run:

ipcrm -a

Warning: This removes all user owned shared memory segments, don't run with superuser privilege or on a machine with other critical applications running.

Licensing

PMFuzz is licensed under BSD-3-clause except noted otherwise.

PMFuzz uses of the following open-source software:

  1. Preeny (license)
    Preeny was modified to fix a bug in desock. All changes are contained in vendor/pathes/preeny_path
  2. AFL++ (license)
    AFL++ was modified to include support for persistent memory tracking for PMFuzz.
Owner
Systems Research at ShiftLab
Systems Research at ShiftLab
Language models are open knowledge graphs ( non official implementation )

language-models-are-knowledge-graphs-pytorch Language models are open knowledge graphs ( work in progress ) A non official reimplementation of Languag

theblackcat102 132 Dec 18, 2022
I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive constraining

I-SECRET This is the implementation of the MICCAI 2021 Paper "I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive con

13 Dec 02, 2022
The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at .

PixelNet: Representation of the pixels, by the pixels, and for the pixels. We explore design principles for general pixel-level prediction problems, f

Aayush Bansal 196 Aug 10, 2022
Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP"

DiLBERT Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP" Pretrained Model The pretrained model presented in the paper is

Kevin Roitero 2 Dec 15, 2022
Sequence-to-Sequence learning using PyTorch

Seq2Seq in PyTorch This is a complete suite for training sequence-to-sequence models in PyTorch. It consists of several models and code to both train

Elad Hoffer 514 Nov 17, 2022
[Preprint] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

Chasing Sparsity in Vision Transformers: An End-to-End Exploration Codes for [Preprint] Chasing Sparsity in Vision Transformers: An End-to-End Explora

VITA 64 Dec 08, 2022
Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models

Patch-Rotation(PatchRot) Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models Submitted to Neurips2021 To

4 Jul 12, 2021
Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The original code is written in keras.

CasRel-pytorch-reimplement Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The o

longlongman 170 Dec 01, 2022
Minimal diffusion models - Minimal code and simple experiments to play with Denoising Diffusion Probabilistic Models (DDPMs)

Minimal code and simple experiments to play with Denoising Diffusion Probabilist

Rithesh Kumar 16 Oct 06, 2022
MetaTTE: a Meta-Learning Based Travel Time Estimation Model for Multi-city Scenarios

MetaTTE: a Meta-Learning Based Travel Time Estimation Model for Multi-city Scenarios This is the official TensorFlow implementation of MetaTTE in the

morningstarwang 4 Dec 14, 2022
Delta Conformity Sociopatterns Analysis - Delta Conformity Sociopatterns Analysis

Delta_Conformity_Sociopatterns_Analysis ∆-Conformity is a local homophily measur

2 Jan 09, 2022
[ICLR 2022 Oral] F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization

F8Net Fixed-Point 8-bit Only Multiplication for Network Quantization (ICLR 2022 Oral) OpenReview | arXiv | PDF | Model Zoo | BibTex PyTorch implementa

Snap Research 76 Dec 13, 2022
Neural Architecture Search Powered by Swarm Intelligence 🐜

Neural Architecture Search Powered by Swarm Intelligence 🐜 DeepSwarm DeepSwarm is an open-source library which uses Ant Colony Optimization to tackle

288 Oct 28, 2022
MIM: MIM Installs OpenMMLab Packages

MIM provides a unified API for launching and installing OpenMMLab projects and their extensions, and managing the OpenMMLab model zoo.

OpenMMLab 254 Jan 04, 2023
TensorFlow code for the neural network presented in the paper: "Structural Language Models of Code" (ICML'2020)

SLM: Structural Language Models of Code This is an official implementation of the model described in: "Structural Language Models of Code" [PDF] To ap

73 Nov 06, 2022
Official implementation of "Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks", NeurIPS 2021.

PHDimGeneralization Official implementation of "Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks", NeurIPS 2021. Overvie

Tolga Birdal 13 Nov 08, 2022
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2020

Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2020

Phillip Lippe 1.1k Jan 07, 2023
Code of our paper "Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning"

CCOP Code of our paper Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning Requirement Install OpenSelfSup Install Detectron2

Chenhongyi Yang 21 Dec 13, 2022
Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning

Machine_Learning Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning This project is based on 2 case-studies:

Avnika Mehta 1 Jan 27, 2022
A BaSiC Tool for Background and Shading Correction of Optical Microscopy Images

BaSiC Matlab code accompanying A BaSiC Tool for Background and Shading Correction of Optical Microscopy Images by Tingying Peng, Kurt Thorn, Timm Schr

Marr Lab 34 Dec 18, 2022