Welcome to the 7th European Lisp Symposium!
The purpose of the European Lisp Symposium is to provide a forum for the discussion and dissemination of all aspects of design, implementationand application of any of the Lisp and Lisp-inspired dialects, including Common Lisp, Scheme, Emacs Lisp, AutoLisp, ISLISP, Dylan, Clojure, ACL2, ECMAScript, Racket, SKILL, Hop and so on. We encourage everyone interested in Lisp to participate.
This year's Highlights:
Programming, software development, and software engineering: We are taught to solve puzzles and do what we’re told. We carry these lessons into our jobs and careers without deliberation. Old fashioned software engineering aims to make no mistakes; agile aims to render programmers compliant, and commands them make money for their bosses. For the past year I’ve been exploring what creativity means during the act of writing, and I’ve been doing it by constructing a software partner that acts as a scientific engine of discovery — a partner that displays a flair for the strange that even the most daring poets can rarely match. I don’t have requirements, I don’t have specifications, and I normally don’t have a plan much beyond a guess. If my program doesn’t surprise me, I cry “failure!” and lament.
I’ll explore what programming is, how software can act as a collaborator, show you how the agile practices are like training wheels, and explain how a program can astound.
All in Lisp, of course.
Pascal Costanza works as a researcher specializing on high-performance computing at the ExaScience Life Lab for Intel Belgium. He maintains Closer, an open source project that provides a compatibility layer for the CLOS MOP across multiple Common Lisp implementations. In the past, he has implemented ContextL, the first programming language extension for Context-oriented Programming based on CLOS, and aspect-oriented extensions for CLOS. More recently, he released Arrow Street, a template library for C++11 to support semi-automatic SIMD-efficient data layouts.
We send probes into the topic hypercube bounded by machine learning, parallelism, software and contests, demonstrate existing and sketch future Lisp infrastructure, pin the future and foreign arrays down.
We take a seemingly random walk along the different paths, watch the scenery of pairwise interactions unfold and piece a puzzle together. In the purely speculative thread, we compare models of parallel computation, keeping an eye on their applicability and lisp support. In the the Python and R envy thread, we detail why lisp could be a better vehicle for scientific programming and how high performance computing is eroding lisp's largely unrealized competitive advantages. Switching to constructive mode, a basic data structure is proposed as a first step.
In the machine learning thread, lisp's unparalleled interactive capabilities meet contests, neural networks cross threads and all get in the way of the presentation.
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