Tag: Numba

Data Science

Accelerating Sequential Python User-Defined Functions with RAPIDS on GPUs for 100X Speedups

Custom “row-by-row” processing logic (sometimes called sequential User-Defined Functions) is prevalent in ETL workflows. The sequential nature of UDFs makes… 3 MIN READ
Data Science

Aligning Time Series at the Speed of Light

In this blog, we introduce rapidAligner – a CUDA-accelerated library to align a short time series snippet (query) in an exceedingly long stream of time series… 10 MIN READ
AI / Deep Learning

Running Python UDFs in Native NVIDIA CUDA Kernels with the RAPIDS cuDF

In this post, I introduce a design and implementation of a framework within RAPIDS cuDF that enables compiling Python user-defined functions (UDF) and inlining… 12 MIN READ
Accelerated Computing

Seven Things You Might Not Know about Numba

One of my favorite things is getting to talk to people about GPU computing and Python. The productivity and interactivity of Python combined with the high… 17 MIN READ

GPU-Accelerated Graph Analytics in Python with Numba

How to use the Numba open-source Python compiler to accelerate PageRank and graph analytics algorithms such as Densest-k-Subgraph on NVIDIA GPUs. 8 MIN READ
Accelerated Computing

CUDACasts Episode #12: Programming GPUs using CUDA Python

So far in the CUDA Python mini-series on CUDACasts, I introduced you to using the @vectorize decorator and CUDA libraries, two different methods for… < 1