hazelcast

This document provides information on two distinct projects: Hazelcast, a real-time data platform, and Mitsuba 3, a research-oriented rendering system. Hazelcast details its capabilities, use cases, and contribution guidelines, while Mitsuba 3 focuses on its features, installation, and usage examples. Both offer comprehensive documentation and support resources for users.

Hazelcast

What is Hazelcast

The world’s leading companies trust Hazelcast to modernize applications and take instant action on data in motion to create new revenue streams, mitigate risk, and operate more efficiently. Businesses use Hazelcast’s unified real-time data platform to process streaming data, enrich it with historical context and take instant action with standard or ML/AI-driven automation - before it is stored in a database or data lake.

Hazelcast is named in the Gartner Market Guide to Event Stream Processing and a leader in the GigaOm Radar Report for Streaming Data Platforms. To join our community of CXOs, architects and developers at brands such as Lowe’s, HSBC, JPMorgan Chase, Volvo, New York Life, and others, visit hazelcast.com.

When to use Hazelcast

Hazelcast provides a platform that can handle multiple types of workloads for

building real-time applications.

Key Features

Stateful Data Processing

Hazelcast has a built-in data processing engine called

Jet, which can be used to build both streaming/real-time

and batch/static data pipelines that are elastic. A single node of Hazelcast has been proven to aggregate 10 million

events per second with

latency under 10 milliseconds. A cluster of Hazelcast nodes can process billion

events per

second.

Get Started

Follow the Getting Started

Guide

to install and start using Hazelcast.

Documentation

Read the documentation for

in-depth details about how to install Hazelcast and an overview of the features.

Get Help

You can use Slack for getting help with Hazelcast.

How to Contribute

Thanks for your interest in contributing! The easiest way is to just send a pull

request.

Building From Source

Building Hazelcast requires at minimum JDK 17. Pull the latest source from the

repository and use Maven install (or package) to build:

It is recommended to use the included Maven wrapper script.

It is also possible to use local Maven distribution with the same

version that is used in the Maven wrapper script.

Additionally, there is a quick build activated by setting the -Dquick system

property that skips validation tasks for faster local builds (e.g. tests, checkstyle

validation, javadoc, source plugins etc) and does not build extensions and distribution

modules.

Testing

Take into account that the default build executes thousands of tests which may

take a considerable amount of time. Hazelcast has 3 testing profiles:

to run quick/integration tests (those can be run

in parallel without using network by using -P parallelTest profile).

to run tests that are either slow

or cannot be run in parallel.

to run all tests serially using

network.

Some tests require Docker to run. Set -Dhazelcast.disable.docker.tests system property to ignore them.

When developing a PR it is sufficient to run your new tests and some

related subset of tests locally. Our PR builder will take care of running

the full test suite.

License

Source code in this repository is covered by one of two licenses:

The default license throughout the repository is Apache License 2.0 unless the

header specifies another license.

Acknowledgments

We owe (the good parts of) our CLI tool's user experience to

picocli.

Copyright

Copyright (c) 2008-2024, Hazelcast, Inc. All Rights Reserved.

Visit www.hazelcast.com for more info.

example:

Mitsuba Renderer 3

Documentation	Tutorial videos	Linux	MacOS	Windows	PyPI

️

Warning

️

There currently is a large amount of undocumented and unstable work going on in

the master branch. We'd highly recommend you use our

latest release

until further notice.

If you already want to try out the upcoming changes, please have a look at

this porting guide.

It should cover most of the new features and breaking changes that are coming.

Introduction

Mitsuba 3 is a research-oriented rendering system for forward and inverse light

transport simulation developed at EPFL in Switzerland.

It consists of a core library and a set of plugins that implement functionality

ranging from materials and light sources to complete rendering algorithms.

Mitsuba 3 is retargetable: this means that the underlying implementations and

data structures can transform to accomplish various different tasks. For

example, the same code can simulate both scalar (classic one-ray-at-a-time) RGB transport

or differential spectral transport on the GPU. This all builds on

Dr.Jit, a specialized just-in-time(JIT) compiler developed specifically for this project.

Main Features

• Cross-platform: Mitsuba 3 has been tested on Linux (x86_64), macOS

  (aarch64, x8664), and Windows (x8664).

• High performance: The underlying Dr.Jit compiler fuses rendering code

  into kernels that achieve state-of-the-art performance using

  an LLVM backend targeting the CPU and a CUDA/OptiX backend

  targeting NVIDIA GPUs with ray tracing hardware acceleration.

• Python first: Mitsuba 3 is deeply integrated with Python. Materials,

  textures, and even full rendering algorithms can be developed in Python,

  which the system JIT-compiles (and optionally differentiates) on the fly.

  This enables the experimentation needed for research in computer graphics and

  other disciplines.

• Differentiation: Mitsuba 3 is a differentiable renderer, meaning that it

  can compute derivatives of the entire simulation with respect to input

  parameters such as camera pose, geometry, BSDFs, textures, and volumes. It

  implements recent differentiable rendering algorithms developed at EPFL.

• Spectral & Polarization: Mitsuba 3 can be used as a monochromatic

  renderer, RGB-based renderer, or spectral renderer. Each variant can

  optionally account for the effects of polarization if desired.

Tutorial videos, documentation

We've recorded several YouTube videos that provide a gentle introduction

Mitsuba 3 and Dr.Jit. Beyond this you can find complete Juypter notebooks

covering a variety of applications, how-to guides, and reference documentation

on readthedocs.

Installation

We provide pre-compiled binary wheels via PyPI. Installing Mitsuba this way is as simple as running

pip install mitsuba

on the command line. The Python package includes thirteen variants by default:

• scalar_rgb

• scalar_spectral

• scalarspectralpolarized

• llvmadrgb

• llvmadmono

• llvmadmono_polarized

• llvmadspectral

• llvmadspectral_polarized

• cudaadrgb

• cudaadmono

• cudaadmono_polarized

• cudaadspectral

• cudaadspectral_polarized

The first two perform classic one-ray-at-a-time simulation using either a RGB

or spectral color representation, while the latter two can be used for inverse

rendering on the CPU or GPU. To access additional variants, you will need to

compile a custom version of Dr.Jit using CMake. Please see the

documentation

for details on this.

Requirements

• Python >= 3.8

• (optional) For computation on the GPU: Nvidia driver >= 495.89

• (optional) For vectorized / parallel computation on the CPU: LLVM >= 11.1

Usage

Here is a simple "Hello World" example that shows how simple it is to render a

scene using Mitsuba 3 from Python:

# Import the library using the alias "mi"import mitsuba as mi# Set the variant of the renderermi.setvariant('scalarrgb')# Load a scenescene = mi.loaddict(mi.cornellbox())# Render the sceneimg = mi.render(scene)# Write the rendered image to an EXR filemi.Bitmap(img).write('cbox.exr')

Tutorials and example notebooks covering a variety of applications can be found

in the documentation.

About

This project was created by Wenzel Jakob.

Significant features and/or improvements to the code were contributed by

Sébastien Speierer,

Nicolas Roussel,

Merlin Nimier-David,

Delio Vicini,

Tizian Zeltner,

Baptiste Nicolet,

Miguel Crespo,

Vincent Leroy, and

Ziyi Zhang.

When using Mitsuba 3 in academic projects, please cite:

@software{Mitsuba3,title = {Mitsuba 3 renderer},author = {Wenzel Jakob and Sébastien Speierer and Nicolas Roussel and Merlin Nimier-David and Delio Vicini and Tizian Zeltner and Baptiste Nicolet and Miguel Crespo and Vincent Leroy and Ziyi Zhang},note = {https://mitsuba-renderer.org},version = {3.1.1},year = 2022}