Blog
Technical deep-dives, benchmark analysis, and Python performance insights from the Epochly team.
NumPy Optimization Guide: When GPU Helps and When It Doesn't
A practical guide to accelerating NumPy workloads. GPU offload, vectorization, and memory layout -- with measured results showing when each technique pays off.
The Python Performance Landscape in 2026
A survey of Python performance tools in 2026: interpreters, compilers, GPU frameworks, and where each fits. Honest assessment, not a sales pitch.
Understanding JIT Compilation for Python: How It Works and When It Helps
How JIT compilation makes Python faster, what happens under the hood, and which workloads benefit. Includes measured results and common pitfalls.
The Journey from 1x to 1,350x: Progressive Python Enhancement
How Epochly progressively enhances Python performance from monitoring to GPU acceleration -- without rewriting a single line of your code.
Honest Benchmarks: What Epochly Can and Can't Do
A transparent look at Epochly's performance across different workload types. Including the ones where it doesn't help.
Profiling Python: Where Your Time Actually Goes
A practical guide to profiling Python code. Find your real bottlenecks before optimizing. With before/after analysis of how optimization shifts bottlenecks.
Python's GIL Deep Dive: What Every Developer Should Know
A technical deep dive into Python's Global Interpreter Lock -- what it does, why it exists, how it affects performance, and what you can do about it.