Graft.jl – General purpose graph analytics for Julia

Graft.jl – Gen...

This blog post describes my work on Graft.jl, a general purpose graph analysis package for Julia. For those unfamiliar with graph algorithms, a quick introduction might help. Proposal My proposal, titled ParallelGraphs, was to develop a parallelized/distributed graph algorithms library. However, in the first month or so, we decided to work towards a more general framework that supports data […]

Technical preview: Native GPU programming with CUDAnative.jl

Technical preview: N...

After 2 years of slow but steady development, we would like to announce the first preview release of native GPU programming capabilities for Julia. You can now write your CUDA kernels in Julia, albeit with some restrictions, making it possible to use Julia’s high-level language features to write high-performance GPU code. The programming support we’re […]

Julia 0.5 Highlights

Julia 0.5 Highlights...

To follow along with the examples in this blog post and run them live, you can go to JuliaBox, create a free login, and open the “Julia 0.5 Highlights” notebook under “What’s New in 0.5”. The notebook can also be downloaded from here. Julia 0.5 is a pivotal release. It introduces more transformative features than […]

Generalizing Abstract Arrays: opportunities and challenges

Generalizing Abstrac...

Introduction: generic algorithms with AbstractArrays Somewhat unusually, this blog post is future-looking: it mostly focuses on things that don’t yet exist. Its purpose is to lay out the background for community discussion about possible changes to the core API for AbstractArrays, and serves as background reading and reference material for a more focused “julep” (a […]

Technical preview: Native GPU programming with CUDAnative.jl

Technical preview: N...

After 2 years of slow but steady development, we would like to announce the first preview release of native GPU programming capabilities for Julia. You can now write your CUDA kernels in Julia, albeit with some restrictions, making it possible to use Julia’s high-level language features to write high-performance GPU code. The programming support we’re […]

Covariance As Signed Area Of Rectangles

Covariance As Signed...

A colleague at work pointed me to a stats.stackexchange answer with an intuitive explanation of covariance: For each pair of points, draw the rectangle with these points at opposite corners. Treat the rectangle’s area as signed, with the same sign as the slope of the line between the two points. If you add up all […]

Stefan Karpinski – “Solving the Two Language Problem”

Stefan Karpinski ...

Bio: Stefan Karpinski is one of the co-creators of the Julia programming language and a co-founder of Julia Computing, Inc., which provides support, consulting and training for commercial usage of Julia. He has previously worked as a software engineer and data scientist at Akamai, Citrix, and Etsy. He is currently a Research Engineer at NYU […]

Introducing the Eclipse Julia plugin – JuliaDT

Introducing the Ecli...

This post was originally posted on the Julia Blog by Taimur el-Halawani, and kindly contributed to Open Data Science. JuliaDT is an alpha release of an Eclipse-based IDE for Julia. Current trends in Big Data and Data Science make Julia a natural choice when tackling the latest business challenges. The plugin aims to make Julia’s […]

Julia Receives Funding to Improve Stats and Functionality

Julia Receives Fundi...

> By Simon Byrne | This post originally appeared in its entirety on juliacomputing.com and was kindly contributed to ODSC< Statistics Roadmap Julia Computing has recently received funding from the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative. One of the main components of this project is to improve the statistics and data science functionality […]