tchigher / weld

High-performance runtime for data analytics applications

Weld

Documentation

Weld is a language and runtime for improving the performance of data-intensive applications. It optimizes across libraries and functions by expressing the core computations in libraries using a common intermediate representation, and optimizing across each framework.

Modern analytics applications combine multiple functions from different libraries and frameworks to build complex workflows. Even though individual functions can achieve high performance in isolation, the performance of the combined workflow is often an order of magnitude below hardware limits due to extensive data movement across the functions. Weld’s take on solving this problem is to lazily build up a computation for the entire workflow, and then optimizing and evaluating it only when a result is needed.

You can join the discussion on Weld on our Google Group or post on the Weld mailing list at [email protected].

Contents

• Building
  • MacOS LLVM Installation
  • Ubuntu LLVM Installation
  • Building Weld
• Documentation
• Grizzly (Pandas on Weld)
• Tools

Building

To build Weld, you need the latest stable version of Rust and LLVM/Clang++ 6.0.

To install Rust, follow the steps here. You can verify that Rust was installed correctly on your system by typing rustc into your shell. If you already have Rust and rustup installed, you can upgrade to the latest stable version with:

rustup update stable

MacOS LLVM Installation

To install LLVM on macOS, first install Homebrew. Then:

brew install llvm@6

Weld's dependencies require llvm-config on $PATH, so you may need to create a symbolic link so the correct llvm-config is picked up (note that you might need to add sudo at the start of this command):

ln -sf `brew --prefix llvm@6`/bin/llvm-config /usr/local/bin/llvm-config

To make sure this worked correctly, run llvm-config --version. You should see 6.0.x.

Ubuntu LLVM Installation

To install LLVM on Ubuntu, get the LLVM 6.0 sources and then apt-get:

On Ubuntu 16.04 (Xenial):

wget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
sudo apt-add-repository "deb http://apt.llvm.org/xenial/ llvm-toolchain-xenial-6.0 main"
sudo apt-get update
sudo apt-get install llvm-6.0-dev clang-6.0

On Ubuntu 14.04 (Trusty):

wget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
sudo apt-add-repository "deb http://apt.llvm.org/trusty/ llvm-toolchain-trusty-6.0 main"

# gcc backport is required on 14.04, for libstdc++. See https://apt.llvm.org/
sudo apt-add-repository "deb http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu trusty main"
sudo apt-get update
sudo apt-get install llvm-6.0-dev clang-6.0

Weld's dependencies require llvm-config, so you may need to create a symbolic link so the correct llvm-config is picked up. sudo may be required:

ln -s /usr/bin/llvm-config-6.0 /usr/local/bin/llvm-config

To make sure this worked correctly, run llvm-config --version. You should see 6.0.x or newer.

You will also need zlib:

sudo apt-get install zlib1g-dev

Building Weld

With LLVM and Rust installed, you can build Weld. Clone this repository, set the WELD_HOME environment variable, and build using cargo:

git clone https://www.github.com/weld-project/weld
cd weld/
export WELD_HOME=`pwd`
cargo build --release

Weld builds two dynamically linked libraries (.so files on Linux and .dylib files on Mac): libweld and libweldrt.

Finally, run the unit and integration tests:

cargo test

Documentation

The Rust Weld crate is documented here.

The docs/ directory contains documentation for the different components of Weld.

• language.md describes the syntax of the Weld IR.
• api.md describes the low-level C API for interfacing with Weld.
• python.md gives an overview of the Python API.
• tutorial.md contains a tutorial for how to build a small vector library using Weld.

Python Bindings

Weld's Python bindings are in python, with examples in examples/python.

Grizzly

Grizzly is a subset of Pandas integrated with Weld. Details on how to use Grizzly are in python/grizzly. Some example workloads that make use of Grizzly are in examples/python/grizzly. To run Grizzly, you will also need the WELD_HOME environment variable to be set, because Grizzly needs to find its own native library through this variable.

Testing

cargo test runs unit and integration tests. A test name substring filter can be used to run a subset of the tests:

cargo test <substring to match in test name>

Tools

This repository contains a number of useful command line tools which are built automatically with the main Weld repository, including an interactive REPL for inspecting and debugging programs. More information on those tools can be found under docs/tools.md.