Optimizations

Eugo applies automatic optimizations to your code to make it run faster and more efficiently, without lifting a finger.

Auto Offload to GPU

When possible, Eugo will automatically offload your code to the GPU for faster execution. This is especially useful for computationally intensive tasks like matrix multiplication, deep learning, and other parallelizable tasks.

Auto Vectorization

Eugo will automatically vectorize your code to take advantage of SIMD instructions on modern CPUs. This can result in significant speedups for certain types of operations, like element-wise arithmetic on large arrays.

Auto Parallelization

Using Ray, Eugo will automatically parallelize your code to take advantage of multiple CPU cores. This can result in orders of magnitude faster execution times for tasks that can be split into independent subtasks, like map-reduce operations.

Auto JIT Compilation (Python 3.13+)

Eugo applies the Python 3.13 Just-In-Time (JIT) compiler to compile your code into machine code for faster execution. This can result in significant speedups, especially for tight loops and numerical computations. Similar to Numba, the Python JIT automatically compiles Python code into native code. On Eugo, however, we compile your code on one node and distribute it across the cluster.