Discover Exaloop's Game-Changing Performance Benefits
Welcome to Exaloop, the ultimate platform for data science and big data analytics. Exaloop redefines speed and efficiency, delivering unparalleled performance that will transform your data-driven projects.
Experience Lightning-Fast Computations
Unlock the Potential of Your Hardware
Accelerate Machine Learning and Beyond
Seamless and Supercharged
Performance for Everyone
Discover the transformative power of speed with Exaloop. Whether you're a data scientist, analyst, or developer, Exaloop’s performance-driven platform will redefine what's possible in your data-driven endeavors.
Empower Your Data Journey with Exaloop's Parallelism and Multithreading
Break Free from Bottlenecks
Harness the Power of Multiple Cores
Efficiently Scale with Big Data
Don’t Waste Your Time Reasoning About Complex Code
Embrace Parallelism and Multithreading with Exaloop
Embrace Unprecedented Speed with GPU Acceleration
Welcome to Exaloop’s GPU Acceleration: Unleash the True Power of Your Hardware
Turbocharge Your Workflows
Elevate Your AI Projects
Tackle Large Datasets Head-On
No CUDA or Low-Level Programming Required
Transform Your Data Journey with Exaloop
Unleash the Full Potential of your Data Science Libraries with Exaloop's Turbocharged Implementations
Welcome to Exaloop’s Optimized Libraries: Your Gateway to Unparalleled Performance
A Performance Boost like Never Before
Synergize with GPU
Integrate with Parallelism and Multithreading
Streamlined Performance, No Expertise Required
Redefine Your Data Science Experience
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 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.