## Exaloop Platform

##### Turbocharged Python

Stay in Python and get the performance of C with Exaloop's turbocharged Python implementation

##### Optimized Libraries

Optimized, fully-compiled versions of your favorite libraries that work seamlessly on heterogenous hardware

##### Computing Cloud

Harness the full range of the cloud's power and resources through AWS, Google Cloud or Azure

##### Online IDE

Access the Exaloop platform from the convenience of your browser, with all batteries included

##### AI Assistant

Leverage the power of ChatGPT and Copilot to write, debug and analyze code

## Go from concept to results–in minutes instead of months.

Don’t let big data impede your performance and productivity.

### Run your code faster

Exaloop lets developers and data scientists stick with Python’s ergonomics and intuitive syntax, but also leverage native code performance, parallel processing, and GPU programming. Experiment, analyze, and iterate faster than ever before.

### Get results faster

By empowering anyone, from data scientists to non-technical professionals, to explore concepts and develop data-driven applications, Exaloop allows for rapid experimentation and implementation of new ideas, unlocking opportunities for groundbreaking solutions.

### Lower your costs

Minimize your compute time and eliminate the need for low-level programming, complex software development, and rewriting code, reducing development costs and saving valuable resources.

### Harness your data

No more downsampling your data to be able to analyze it. Harness the full power of your data in less time and with less effort.

### Collaborate and solve

With Exaloop, you don’t need to be an engineer to write performant and scalable code. Leverage your team’s diverse expertise and perspectives to build robust applications and innovative solutions.

## What our users are saying

"Combining the high performance of native binaries and the intuitive natural cognitive ergonomics of Python is the best of both worlds. Bravo.”

###### @psytron GitHub

###### @psytron GitHub

"We have experienced at least a 10x speedup with very little effort - it's a game changer for our graph-based ML models”

###### CIO, Biotech Company

###### CIO, Biotech Company

"Thank you, for making Python fast like C/C++. It's real 🙂 The performance on the GPU is mind boggling."

###### @marioroy GitHub

###### @marioroy GitHub

## Benchmarks

Speedup over Python

float

pyperformance’s float benchmark — performs a series of 3-dimensional vector operations and normalizations.

Benchmark: nbody

pyperformance’s nbody benchmark — simulates the movement of several celestial objects.

pyperformance’s spectral_norm benchmark — computes the largest singular value of a particular infinite matrix.

Benchmark: primes

Counts prime numbers below a given threshold. Codon version is multi-threaded.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

pyperformance’s fannkuch benchmark — performs, for each permutation of a list, a number of element reversals until a certain condition is met, and computes the maximum number of reversals needed across all permutations. Codon version is multi-threaded.

## Speedup over Python

pyperformance’s float benchmark — performs a series of 3-dimensional vector operations and normalizations.

Benchmark: nbody

pyperformance’s nbody benchmark — simulates the movement of several celestial objects.

pyperformance’s spectral_norm benchmark — computes the largest singular value of a particular infinite matrix.

Benchmark: primes

Counts prime numbers below a given threshold. Codon version is multi-threaded.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Runtime in seconds

float

pyperformance’s float benchmark — performs a series of 3-dimensional vector operations and normalizations.

pyperformance’s nbody benchmark — simulates the movement of several celestial objects.

pyperformance’s spectral_norm benchmark — computes the largest singular value of a particular infinite matrix.

Counts prime numbers below a given threshold. Codon version is multi-threaded.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Benchmark: fannkuch

pyperformance’s fannkuch benchmark — performs, for each permutation of a list, a number of element reversals until a certain condition is met, and computes the maximum number of reversals needed across all permutations. Codon version is multi-threaded.

## Runtime in seconds

pyperformance’s nbody benchmark — simulates the movement of several celestial objects.

Counts prime numbers below a given threshold. Codon version is multi-threaded.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Benchmark: fannkuch

pyperformance’s fannkuch benchmark — performs, for each permutation of a list, a number of element reversals until a certain condition is met, and computes the maximum number of reversals needed across all permutations. Codon version is multi-threaded.

Speedup over Python

float

Benchmark: nbody

pyperformance’s nbody benchmark — simulates the movement of several celestial objects.

Benchmark: primes

Counts prime numbers below a given threshold. Codon version is multi-threaded.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

## Speedup over Python

Benchmark: nbody

pyperformance’s nbody benchmark — simulates the movement of several celestial objects.

Benchmark: primes

Counts prime numbers below a given threshold. Codon version is multi-threaded.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Runtime in seconds

float

pyperformance’s nbody benchmark — simulates the movement of several celestial objects.

Counts prime numbers below a given threshold. Codon version is multi-threaded.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Benchmark: fannkuch

## Runtime in seconds

pyperformance’s nbody benchmark — simulates the movement of several celestial objects.

Counts prime numbers below a given threshold. Codon version is multi-threaded.

Hans Boehm’s binary-trees benchmark — creates and traverses a number of binary trees.

Benchmark: fannkuch