Simple, Flexible Distributed Computing in Julia

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Julia is a high-level, high-performance dynamic language for efficient, large-scale scientific and technical computing. It has been gaining traction as a an alternative to R, Matlab, and NumPy, especially in performance-demanding areas, such as “big statistics”, bioinformatics, imaging, and linear algebra. Julia provides simple, flexible primitives for distributed computing, out of the box. Scalable distributed computation systems have typically either provided specialized parallel kernels to be composed by a control program — like ScaLAPACK for linear algebra — or provided specific but generalizable distributed fameworks — like MapReduce or Pregel. The computational kernel approach provides extreme performance, but sacrifices generality and assumes a fixed set of highly reliable computational resources. The framework approach gives up raw performance in exchange for fault tolerance, easier scaling, and greater generality. Julia provides a global distributed address space, a flexible futures mechanism, automatic serialization of user data and code, elastic parallelism, and simple, integrated fault handling. These primitives allow various approaches to distributed computation to be implemented succinctly and easily, with high performance, entirely in Julia.

Photo of Stefan Karpinski

Stefan Karpinski

Julia Computing, Inc.

Stefan Karpinski is one of the co-creators and core developers of the Julia language. He is an applied mathematician and data scientist by trade, having worked at Akamai, Citrix Online, and Etsy, but currently is focused on advancing Julia’s design, implementation, documentation, and community.

Photo of Jeff Bezanson

Jeff Bezanson

The Julia Language

Before the Julia effort began, Jeff Bezanson worked as a software engineer at Interactive Supercomputing, which developed the Star-P parallel extension to MATLAB. At the company, Jeff was a principal developer of M#, an implementation of the MATLAB language running on .NET. He is now a second-year graduate student at MIT. Jeff received an A.B. in Computer Science from Harvard University in 2004, and has experience with applications of technical computing in medical imaging.

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