eXPeditious Data Transfer

Related tags

Deep Learningpythoncserializationprotocol-buffersmarshallingcompilersxpdt
Overview

xpdt: eXPeditious Data Transfer

PyPI version

About

xpdt is (yet another) language for defining data-types and generating code for serializing and deserializing them. It aims to produce code with little or no overhead and is based on fixed-length representations which allows for zero-copy deserialization and (at-most-)one-copy writes (source to buffer).

The generated C code, in particular, is highly optimized and often permits the elimination of data-copying for writes and enables optimizations such as loop-unrolling for fixed-length objects. This can lead to read speeds in excess of 500 million objects per second (~1.8 nsec per object).

Examples

The xpdt source language looks similar to C struct definitions:

struct timestamp {
	u32	tv_sec;
	u32	tv_nsec;
};

struct point {
	i32	x;
	i32	y;
	i32	z;
};

struct line {
	timestamp	time;
	point		line_start;
	point		line_end;
	bytes		comment;
};

Fixed width integer types from 8 to 128 bit are supported, along with the bytes type, which is a variable-length sequence of bytes.

Target Languages

The following target languages are currently supported:

  • C
  • Python

The C code is very highly optimized.

The Python code is about as well optimized for CPython as I can make it. It uses typed NamedTuple for objects, which has some small overhead over regular tuples, and it uses struct.Struct to do the packing/unpacking. I have also code-golfed the generated bytecodes down to what I think is minimal given the design constraints. As a result, performance of the pure Python code is comparable to a JSON library implemented in C or Rust.

For better performance in Python, it may be desirable to develop a Cython target. In some instances CFFI structs may be more performant since they can avoid the creation/destruction of an object for each record.

Target languages are implemented purely as jinja2 templates.

Serialization format

The serialization format for fixed-length objects is simply a packed C struct.

For any object which contains bytes type fields:

  • a 32bit unsigned record length is prepended to the struct
  • all bytes type fields are converted to u32 and contain the length of the bytes
  • all bytes contents are appended after the struct in the order in which they appear

For example, following the example above, the serialization would be:

u32 tot_len # = 41
u32 time.tv_sec
u32 time.tv_usec
i32 line_start.x
i32 line_start.y
i32 line_start.z
i32 line_end.x
i32 line_end.y
i32 line_end.z
u32 comment # = 5
u8 'H'
u8 'e'
u8 'l'
u8 'l'
u8 'o'

Features

The feature-set is, as of now, pretty slim.

There are no array / sequence / map types, and no keyed unions.

Support for such things may be added in future provided that suitable implementations exist. An implementation is suitable if:

  • It admits a zero (or close to zero) overhead implementation
  • it causes no overhead when the feature isn't being used

License

The compiler is released under the GPLv3.

The C support code/headers are released under the MIT license.

The generated code is yours.

You might also like...
Official code for the CVPR 2021 paper "How Well Do Self-Supervised Models Transfer?"

How Well Do Self-Supervised Models Transfer? This repository hosts the code for the experiments in the CVPR 2021 paper How Well Do Self-Supervised Mod

Linus Ericsson 157 Dec 16, 2022
Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch
Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch

Cross Transformers - Pytorch (wip) Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch Install $ pip install cross-t

Phil Wang 40 Dec 22, 2022
Neural style transfer as a class in PyTorch

pt-styletransfer Neural style transfer as a class in PyTorch Based on: https://github.com/alexis-jacq/Pytorch-Tutorials Adds: StyleTransferNet as a cl

Tyler Kvochick 31 Jun 27, 2022
Offcial repository for the IEEE ICRA 2021 paper Auto-Tuned Sim-to-Real Transfer.

Offcial repository for the IEEE ICRA 2021 paper Auto-Tuned Sim-to-Real Transfer.

null 47 Jun 30, 2022
transfer attack; adversarial examples; black-box attack; unrestricted Adversarial Attacks on ImageNet; CVPR2021 天池黑盒竞赛
transfer attack; adversarial examples; black-box attack; unrestricted Adversarial Attacks on ImageNet; CVPR2021 天池黑盒竞赛

transfer_adv CVPR-2021 AIC-VI: unrestricted Adversarial Attacks on ImageNet CVPR2021 安全AI挑战者计划第六期赛道2：ImageNet无限制对抗攻击 介绍 ： 深度神经网络已经在各种视觉识别问题上取得了最先进的性能。

null 25 Dec 8, 2022
PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos
PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos

PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos. By adopting a unified pipeline-based API design, PyKale enforces standardization and minimalism, via reusing existing resources, reducing repetitions and redundancy, and recycling learning models across areas.

PyKale 370 Dec 27, 2022
Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer
Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer

Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer Paper on arXiv Public PyTorch implementation of two-stage peer-reg

NNAISENSE 38 Oct 14, 2022
Instant Real-Time Example-Based Style Transfer to Facial Videos
Instant Real-Time Example-Based Style Transfer to Facial Videos

FaceBlit: Instant Real-Time Example-Based Style Transfer to Facial Videos The official implementation of FaceBlit: Instant Real-Time Example-Based Sty

Aneta Texler 131 Dec 19, 2022
An implementation of
An implementation of "Optimal Textures: Fast and Robust Texture Synthesis and Style Transfer through Optimal Transport"

Optex An implementation of Optimal Textures: Fast and Robust Texture Synthesis and Style Transfer through Optimal Transport for TU Delft CS4240. You c

Hans Brouwer 33 Jan 5, 2023
Releases(v0.0.5)

  • v0.0.5(Jan 6, 2022)

  • v0.0.4(Jan 6, 2022)

  • v0.0.3(Dec 21, 2021)

    First cut of multiplexed files support, where you can read/write structs of different types to and from the same file. A discriminator field and record length is prepended to each record.

    Fields whose names begin with underscore are now considered hidden/reserved fields. They can be use to add padding and force specific alignments.

    Improve the error messages in the tokenization stage.

    Numerous improvements to the C and python code. Added support for new types: bytearray, stringlist, intstack.

    Source code(tar.gz)
    Source code(zip)

  • v0.0.2(Jun 27, 2021)

    A new string type was added, as well as the ability to add reserved/padding fields which are set to all zeroes.

    Some language-breaking changes were made: the "type" keyword changed to "struct" and the signed integer types were renamed to the more conventional "i8" ... "i64".

    Source code(tar.gz)
    Source code(zip)

  • v0.0.1(May 23, 2021)

Owner
Gianni Tedesco
Computer programming is fun.
Gianni Tedesco
GitHub Repository
Pervasive Attention: 2D Convolutional Networks for Sequence-to-Sequence Prediction

This is a fork of Fairseq(-py) with implementations of the following models: Pervasive Attention - 2D Convolutional Neural Networks for Sequence-to-Se

Maha 490 Dec 15, 2022
PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

简体中文 | English PaddleRobotics paddleRobotics是基于paddle的机器人开源算法库集，包括人机交互、复杂运动控制、环境感知、slam定位导航等开源算法部分。 人机交互 主动多模交互技术TFVT-HRI 主动多模交互技术是通过视觉、语音、触摸传感器等输入机器人

185 Dec 26, 2022
Full-featured Decision Trees and Random Forests learner.

CID3 This is a full-featured Decision Trees and Random Forests learner. It can save trees or forests to disk for later use. It is possible to query tr

Alejandro Penate-Diaz 3 Aug 15, 2022
Springer Link Download Module for Python

♞ pupalink A simple Python module to search and download books from SpringerLink. 🧪 This project is still in an early stage of development. Expect br

Pupa Corp. 18 Nov 21, 2022
python-timbl, originally developed by Sander Canisius, is a Python extension module wrapping the full TiMBL C++ programming interface. With this module, all functionality exposed through the C++ interface is also available to Python scripts. Being able to access the API from Python greatly facilitates prototyping TiMBL-based applications.

README: python-timbl Authors: Sander Canisius, Maarten van Gompel Contact: [email 

Maarten van Gompel 16 Jan 16, 2022
A Python package for time series augmentation

tsaug tsaug is a Python package for time series augmentation. It offers a set of augmentation methods for time series, as well as a simple API to conn

Arundo Analytics 278 Jan 01, 2023
Model-based Reinforcement Learning Improves Autonomous Racing Performance

Racing Dreamer: Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars In this work, we propose to learn a racing contro

Cyber Physical Systems - TU Wien 38 Dec 06, 2022
Experiments with the Robust Binary Interval Search (RBIS) algorithm, a Query-Based prediction algorithm for the Online Search problem.

OnlineSearchRBIS Online Search with Best-Price and Query-Based Predictions This is the implementation of the Robust Binary Interval Search (RBIS) algo

S. K. 1 Apr 16, 2022
MLP-Numpy - A simple modular implementation of Multi Layer Perceptron in pure Numpy.

MLP-Numpy A simple modular implementation of Multi Layer Perceptron in pure Numpy. I used the Iris dataset from scikit-learn library for the experimen

Soroush Omranpour 1 Jan 01, 2022
This reposityory contains the PyTorch implementation of our paper "Generative Dynamic Patch Attack".

Generative Dynamic Patch Attack This reposityory contains the PyTorch implementation of our paper "Generative Dynamic Patch Attack". Requirements PyTo

Xiang Li 8 Nov 17, 2022
Accurate Phylogenetic Inference with Symmetry-Preserving Neural Networks

Accurate Phylogenetic Inference with a Symmetry-preserving Neural Network Model Claudia Solis-Lemus Shengwen Yang Leonardo Zepeda-Núñez This repositor

Leonardo Zepeda-Núñez 2 Feb 11, 2022
Modifications of the official PyTorch implementation of StyleGAN3. Let's easily generate images and videos with StyleGAN2/2-ADA/3!

Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation of the NeurIPS 2021 paper Alias-Free Generative Adversarial Net

Diego Porres 185 Dec 24, 2022
Single Image Deraining Using Bilateral Recurrent Network (TIP 2020)

Single Image Deraining Using Bilateral Recurrent Network Introduction Single image deraining has received considerable progress based on deep convolut

23 Aug 10, 2022
Si Adek Keras is software VR dangerous object detection.

Si Adek Python Keras Sistem Informasi Deteksi Benda Berbahaya Keras Python. Version 1.0 Developed by Ananda Rauf Maududi. Developed date: 24 November

Ananda Rauf 1 Dec 21, 2021
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".

Energy-based Conditional Generative Adversarial Network (ECGAN) This is the code for the NeurIPS 2021 paper "A Unified View of cGANs with and without

sianchen 22 May 28, 2022
An implementation of chunked, compressed, N-dimensional arrays for Python.

Zarr Latest Release Package Status License Build Status Coverage Downloads Gitter Citation What is it? Zarr is a Python package providing an implement

Zarr Developers 1.1k Dec 30, 2022
Analyzes your GitHub Profile and presents you with a report on how likely you are to become the next MLH Fellow!

Fellowship Prediction GitHub Profile Comparative Analysis Tool Built with BentoML Table of Contents: Features Disclaimer Technologies Used Contributin

Damir Temir 51 Dec 29, 2022
An Efficient Implementation of Analytic Mesh Algorithm for 3D Iso-surface Extraction from Neural Networks

AnalyticMesh Analytic Marching is an exact meshing solution from neural networks. Compared to standard methods, it completely avoids geometric and top

Karbo 45 Dec 21, 2022
Official implementation of GraphMask as presented in our paper Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking.

GraphMask This repository contains an implementation of GraphMask, the interpretability technique for graph neural networks presented in our ICLR 2021

Michael Schlichtkrull 29 Sep 02, 2022
code for paper -- "Seamless Satellite-image Synthesis"

Seamless Satellite-image Synthesis by Jialin Zhu and Tom Kelly. Project site. The code of our models borrows heavily from the BicycleGAN repository an

Light 14 Apr 05, 2022