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An anonymous reader quotes InsideHPC: Today Julia Computing announced the Julia 1.0 programming language release, "the most important Julia milestone since Julia was introduced in February 2012." As the first complete, reliable, stable and forward-compatible Julia release, version 1.0 is the fastest, simplest and most productive open-source programming language for scientific, numeric and mathematical computing. "With today's Julia 1.0 release, Julia now provides the language stability that commercial customers require together with the unique combination of lightning speed and high productivity that gives Julia its competitive advantage compared with Python, R, C++ and Java."
The Register reports: Created by Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman, the language was designed to excel at data science, machine learning, and scientific computing.... Six years ago, Julia's creators framed their goals thus:

"We want a language that's open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that's homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled...."

In a julialang.org post announcing the milestone, the minders of the language claim to have achieved some of their goals.

overheardinpdx quotes HPCwire: Wednesday a company called Bounded Floating Point announced a "breakthrough patent in processor design, which allows representation of real numbers accurate to the last digit for the first time in computer history. This bounded floating point system is a game changer for the computing industry, particularly for computationally intensive functions such as weather prediction, GPS, and autonomous vehicles," said the inventor, Alan Jorgensen, PhD. "By using this system, it is possible to guarantee that the display of floating point values is accurate to plus or minus one in the last digit..."

The innovative bounded floating point system computes two limits (or bounds) that contain the represented real number. These bounds are carried through successive calculations. When the calculated result is no longer sufficiently accurate the result is so marked, as are all further calculations made using that value. It is fail-safe and performs in real time.
Jorgensen is described as a cyber bounty hunter and part time instructor at the University of Nevada, Las Vegas teaching computer science to non-computer science students. In November he received US Patent number 9,817,662 -- "Apparatus for calculating and retaining a bound on error during floating point operations and methods thereof." But in a followup, HPCwire reports: After this article was published, a number of readers raised concerns about the originality of Jorgensen's techniques, noting the existence of prior art going back years. Specifically, there is precedent in John Gustafson's work on unums and interval arithmetic both at Sun and in his 2015 book, The End of Error, which was published 19 months before Jorgensen's patent application was filed. We regret the omission of this information from the original article.

Slashdot reader overheardinpdx shares a video from the SC17 supercomputing conference where Bruce Tulloch from BitScope "describes a low-cost Rasberry Pi cluster that Los Alamos National Lab is using to simulate large-scale supercomputers." Slashdot reader mspohr describes them as "five rack-mount Bitscope Cluster Modules, each with 150 Raspberry Pi boards with integrated network switches." With each of the 750 chips packing four cores, it offers a 3,000-core highly parallelizable platform that emulates an ARM-based supercomputer, allowing researchers to test development code without requiring a power-hungry machine at significant cost to the taxpayer. The full 750-node cluster, running 2-3 W per processor, runs at 1000W idle, 3000W at typical and 4000W at peak (with the switches) and is substantially cheaper, if also computationally a lot slower. After development using the Pi clusters, frameworks can then be ported to the larger scale supercomputers available at Los Alamos National Lab, such as Trinity and Crossroads.
BitScope's Tulloch points out the cluster is fully integrated with the network switching infrastructure at Los Alamos National Lab, and applauds the Raspberry Bi cluster as "affordable, scalable, highly parallel testbed for high-performance-computing system-software developers."