Segcache: a memory-efficient and scalable in-memory key-value cache for small objects

Related tags

Deep LearningSegcache
Overview

Segcache: a memory-efficient and scalable in-memory key-value cache for small objects

This repo contains the code of Segcache described in the following paper:

Repository structure

Usage

Requirement

  • platform: Mac OS X or Linux
  • build tools: cmake (>=2.8)
  • compiler: gcc (>=4.8) or clang (>=3.1)
  • (optional) unit testing framework: check (>=0.10.0). See below.

Build

git clone https://github.com/Thesys-lab/Segcache.git
mkdir _build && cd _build
cmake ..
make -j

The executables can be found under _bin/ (under build directory)

To run all the tests, including those on ccommon, run:

make test

To skip building tests, replace the cmake step with the following:

cmake -DCHECK_WORKING=off ..

Run benchmarks

After building, you should have _build/bin/trace_replay_seg and _build/bin/trace_replay_slab which are the benchmarks for Segcache and Pelikan_twemcache. To run them, you can do

./trace_replay_slab trace_replay_slab.conf
./trace_replay_seg trace_replay_seg.conf

We provide example config to run the two benchmarks at benchmarks/config/examples/. Before using it, you need to change the options, specifically, you need to change trace_path to the path of your trace.

We release the five traces we use here. The traces are comparessed with zstd, you can use

zstd -d c.sbin.zst

to decompress, the raw traces are in binary format and can be directly consumed by the benchmark.

If you would like to use your traces, you can convert your trace into the following format, each request uses 20 bytes with the following format

struct request {
    uint32_t real_time, 
    uint64_t obj_id, 
    uint32_t key_size:8, 
    uint32_t val_size:24,
    uint32_t op:8,
    uint32_t ttl:24
}; 

License

This software is licensed under the Apache 2.0 license, see LICENSE for details.

Owner
TheSys Group @ CMU CS
Prof. Rashmi Vinayak's research group
TheSys Group @ CMU CS
Benchmarking Pipeline for Prediction of Protein-Protein Interactions

B4PPI Benchmarking Pipeline for the Prediction of Protein-Protein Interactions How this benchmarking pipeline has been built, and how to use it, is de

Loïc Lannelongue 4 Jun 27, 2022
Combinatorial model of ligand-receptor binding

Combinatorial model of ligand-receptor binding The binding of ligands to receptors is the starting point for many import signal pathways within a cell

Mobolaji Williams 0 Jan 09, 2022
Simple helper library to convert a collection of numpy data to tfrecord, and build a tensorflow dataset from the tfrecord.

numpy2tfrecord Simple helper library to convert a collection of numpy data to tfrecord, and build a tensorflow dataset from the tfrecord. Installation

Ryo Yonetani 2 Jan 16, 2022
An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" in Pytorch.

GLOM An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" for MNIST Dataset. To understand this

50 Oct 19, 2022
A curated list of awesome projects and resources related fastai

A curated list of awesome projects and resources related fastai

Tanishq Abraham 138 Dec 22, 2022
PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021.

GCResNet PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021. The code will

11 May 19, 2022
使用深度学习框架提取视频硬字幕;docker容器免安装深度学习库,使用本地api接口使得界面和后端识别分离;

extract-video-subtittle 使用深度学习框架提取视频硬字幕; 本地识别无需联网; CPU识别速度可观; 容器提供API接口; 运行环境 本项目运行环境非常好搭建,我做好了docker容器免安装各种深度学习包; 提供windows界面操作; 容器为CPU版本; 视频演示 https

歌者 16 Aug 06, 2022
Pytorch-Swin-Unet-V2 - a modified version of Swin Unet based on Swin Transfomer V2

Swin Unet V2 Swin Unet V2 is a modified version of Swin Unet arxiv based on Swin

Chenxu Peng 26 Dec 03, 2022
[ICCV'21] PlaneTR: Structure-Guided Transformers for 3D Plane Recovery

PlaneTR: Structure-Guided Transformers for 3D Plane Recovery This is the official implementation of our ICCV 2021 paper News There maybe some bugs in

73 Nov 30, 2022
Repository for "Exploring Sparsity in Image Super-Resolution for Efficient Inference", CVPR 2021

SMSR Reposity for "Exploring Sparsity in Image Super-Resolution for Efficient Inference" [arXiv] Highlights Locate and skip redundant computation in S

Longguang Wang 225 Dec 26, 2022
Fast Style Transfer in TensorFlow

Fast Style Transfer in TensorFlow Add styles from famous paintings to any photo in a fraction of a second! You can even style videos! It takes 100ms o

Jefferson 5 Oct 24, 2021
Kaggle | 9th place (part of) solution for the Bristol-Myers Squibb – Molecular Translation challenge

Part of the 9th place solution for the Bristol-Myers Squibb – Molecular Translation challenge translating images containing chemical structures into I

Erdene-Ochir Tuguldur 22 Nov 30, 2022
Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)

Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021) This repository is the official PyTorc

Jingyun Liang 139 Dec 29, 2022
A two-stage U-Net for high-fidelity denoising of historical recordings

A two-stage U-Net for high-fidelity denoising of historical recordings Official repository of the paper (not submitted yet): E. Moliner and V. Välimäk

Eloi Moliner Juanpere 57 Jan 05, 2023
A python module for scientific analysis of 3D objects based on VTK and Numpy

A lightweight and powerful python module for scientific analysis and visualization of 3d objects.

Marco Musy 1.5k Jan 06, 2023
Image Segmentation and Object Detection in Pytorch

Image Segmentation and Object Detection in Pytorch Pytorch-Segmentation-Detection is a library for image segmentation and object detection with report

Daniil Pakhomov 732 Dec 10, 2022
Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

ColossalAI An integrated large-scale model training system with efficient parallelization techniques. arXiv: Colossal-AI: A Unified Deep Learning Syst

HPC-AI Tech 7.9k Jan 08, 2023
FastFace: Lightweight Face Detection Framework

Light Face Detection using PyTorch Lightning

Ömer BORHAN 75 Dec 05, 2022
OCR Post Correction for Endangered Language Texts

📌 Coming soon: an update to the software including features from our paper on semi-supervised OCR post-correction, to be published in the Transaction

Shruti Rijhwani 96 Dec 31, 2022
A library for using chemistry in your applications

Chemistry in python Resources Used The following items are not made by me! Click the words to go to the original source Periodic Tab Json - Used in -

Tech Penguin 28 Dec 17, 2021