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
VoxHRNet - Whole Brain Segmentation with Full Volume Neural Network

VoxHRNet This is the official implementation of the following paper: Whole Brain Segmentation with Full Volume Neural Network Yeshu Li, Jonathan Cui,

Microsoft 12 Nov 24, 2022
Code for Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021)

Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021) Single-cause Perturbation (SCP) is a framework to estimate the m

Zhaozhi Qian 9 Sep 28, 2022
Conversion between units used in magnetism

convmag Conversion between various units used in magnetism The conversions between base units available are: T - G : 1e4

0 Jul 15, 2021
A unofficial pytorch implementation of PAN(PSENet2): Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network Requirements pytorch 1.1+ torchvision 0.3+ pyclipper opencv3 gcc

zhoujun 400 Dec 26, 2022
Revisiting Temporal Alignment for Video Restoration

Revisiting Temporal Alignment for Video Restoration [arXiv] Kun Zhou, Wenbo Li, Liying Lu, Xiaoguang Han, Jiangbo Lu We provide our results at Google

52 Dec 25, 2022
Meta-meta-learning with evolution and plasticity

Evolve plastic networks to be able to automatically acquire novel cognitive (meta-learning) tasks

5 Jun 28, 2022
Luminous is a framework for testing the performance of Embodied AI (EAI) models in indoor tasks.

Luminous is a framework for testing the performance of Embodied AI (EAI) models in indoor tasks. Generally, we intergrete different kind of functional

28 Jan 08, 2023
A multi-scale unsupervised learning for deformable image registration

A multi-scale unsupervised learning for deformable image registration Shuwei Shao, Zhongcai Pei, Weihai Chen, Wentao Zhu, Xingming Wu and Baochang Zha

ShuweiShao 2 Apr 13, 2022
Baseline of DCASE 2020 task 4

Couple Learning for SED This repository provides the data and source code for sound event detection (SED) task. The improvement of the Couple Learning

21 Oct 18, 2022
Canonical Capsules: Unsupervised Capsules in Canonical Pose (NeurIPS 2021)

Canonical Capsules: Unsupervised Capsules in Canonical Pose (NeurIPS 2021) Introduction This is the official repository for the PyTorch implementation

165 Dec 07, 2022
[CVPR'21] Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

IVOS-W Paper Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild Zhaoyun Yin, Jia Zheng, Weixin Luo, Shenhan Qian, Hanli

SVIP Lab 38 Dec 12, 2022
Code for ACM MM2021 paper "Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection"

CTDNet The PyTorch code for ACM MM2021 paper "Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection" Requirements Python 3.6

CVTEAM 28 Oct 20, 2022
This repository is all about spending some time the with the original problem posed by Minsky and Papert

This repository is all about spending some time the with the original problem posed by Minsky and Papert. Working through this problem is a great way to begin learning computer vision.

Jaissruti Nanthakumar 1 Jan 23, 2022
DyNet: The Dynamic Neural Network Toolkit

The Dynamic Neural Network Toolkit General Installation C++ Python Getting Started Citing Releases and Contributing General DyNet is a neural network

Chris Dyer's lab @ LTI/CMU 3.3k Jan 06, 2023
An investigation project for SISR.

SISR-Survey An investigation project for SISR. This repository is an official project of the paper "From Beginner to Master: A Survey for Deep Learnin

Juncheng Li 79 Oct 20, 2022
Convolutional Neural Network for 3D meshes in PyTorch

MeshCNN in PyTorch SIGGRAPH 2019 [Paper] [Project Page] MeshCNN is a general-purpose deep neural network for 3D triangular meshes, which can be used f

Rana Hanocka 1.4k Jan 04, 2023
Gif-caption - A straightforward GIF Captioner written in Python

Broksy's GIF Captioner Have you ever wanted to easily caption a GIF without havi

3 Apr 09, 2022
Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection".

A2S-USOD Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection". Code will be released upon

15 Dec 16, 2022
Implementation of the bachelor's thesis "Real-time stock predictions with deep learning and news scraping".

Real-time stock predictions with deep learning and news scraping This repository contains a partial implementation of my bachelor's thesis "Real-time

David Álvarez de la Torre 0 Feb 09, 2022
A Quick and Dirty Progressive Neural Network written in TensorFlow.

prog_nn .▄▄ · ▄· ▄▌ ▐ ▄ ▄▄▄· ▐ ▄ ▐█ ▀. ▐█▪██▌•█▌▐█▐█ ▄█▪ •█▌▐█ ▄▀▀▀█▄▐█▌▐█▪▐█▐▐▌ ██▀

SynPon 53 Dec 12, 2022