eXPeditious Data Transfer

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

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

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

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.

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。

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.

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

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

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

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)
Owner
Gianni Tedesco
Computer programming is fun.
Gianni Tedesco
Transfer-Learn is an open-source and well-documented library for Transfer Learning.

Transfer-Learn is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms, or readily apply existing algorithms.

THUML @ Tsinghua University 2.2k Jan 3, 2023
Transfer style api - An API to use with Tranfer Style App, where you can use two image and transfer the style

Transfer Style API It's an API to use with Tranfer Style App, where you can use

Brian Alejandro 1 Feb 13, 2022
data/code repository of "C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer"

C2F-FWN data/code repository of "C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer" (https://arxiv.org/abs/

EKILI 46 Dec 14, 2022
PyTorch implementation of neural style transfer algorithm

neural-style-pt This is a PyTorch implementation of the paper A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias

null 770 Jan 2, 2023
Neural style transfer in PyTorch.

style-transfer-pytorch An implementation of neural style transfer (A Neural Algorithm of Artistic Style) in PyTorch, supporting CPUs and Nvidia GPUs.

Katherine Crowson 395 Jan 6, 2023
Transfer SemanticKITTI labeles into other dataset/sensor formats.

LiDAR-Transfer Transfer SemanticKITTI labeles into other dataset/sensor formats. Content Convert datasets (NUSCENES, FORD, NCLT) to KITTI format Minim

Photogrammetry & Robotics Bonn 64 Nov 21, 2022
Transfer Learning library for Deep Neural Networks.

Transfer and meta-learning in Python Each folder in this repository corresponds to a method or tool for transfer/meta-learning. xfer-ml is a standalon

Amazon 245 Dec 8, 2022
a morph transfer UGATIT for image translation.

Morph-UGATIT a morph transfer UGATIT for image translation. Introduction 中文技术文档 This is Pytorch implementation of UGATIT, paper "U-GAT-IT: Unsupervise

null 55 Nov 14, 2022
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization Official PyTorch implementation for our URST (Ultra-Resolution Sty

czczup 148 Dec 27, 2022
Official PyTorch implementation of "ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows"

ArtFlow Official PyTorch implementation of the paper: ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows Jie An*, Siyu Huang*, Yibing

null 123 Dec 27, 2022