Vectorization
Rewriting operations so a single instruction processes several data elements at once, using the processor’s wide registers.
Eugo applies this automatically through its own compiler toolchain, which is part of why ordinary Python numeric code runs faster on the platform than it would unaided.
Performance & optimization
Related terms
- Automatic optimization: Transformations Eugo applies to your code as it runs: vectorization, GPU offloading, and low-level tuning, with no annotation from you.
- ARM Neoverse: The ARM server processor architecture Eugo’s compute nodes are built on, chosen for performance per watt on numeric workloads.
- Parallel computing: Many calculations at once rather than one after another. Two scales on Eugo: across machines in a cluster, and inside a CPU core through vectorization.