~pycoders | Bookmarks (6)

The Wrong Way to Speed Up Your Code With Numba Numba can make your numeric code faster, but only if you use it right. Learn what “right” means and what to avoid. ITAMAR TURNER-TRAURING

Not Just NVIDIA: GPU Programming That Runs Everywhere This article talks about wgpu-py, a GPU portable Python library. Ever want to use the GPU but use something other than NIVIDIA? This library might be for you. ITAMAR TURNER-TRAURING

Profiling Your Numba Code Learn how to use the Profila profiler to find performance bottlenecks in your Numba code. ITAMAR TURNER-TRAURING

Beware of Misleading GPU vs CPU Benchmarks Nvidia has created GPU-based replacements for NumPy and other tools and promises significant speed-ups, but the comparison may not be accurate. Read on to learn if GPU replacements for CPU-based libraries are really that much faster. ITAMAR TURNER-TRAURING

NumPy 2 Is Coming: Preventing Breakage, Updating Your Code NumPy 2 is coming, and it’s backwards incompatible. Learn how to keep your code from breaking, and how to upgrade. ITAMAR TURNER-TRAURING

How Many CPU Cores Can You Actually Use in Parallel? “Figuring out how much parallelism your program can use is surprisingly tricky.” This article shows you why it is complicated and what you can determine. ITAMAR TURNER-TRAURING