Bridging cultures that have often been distant, Julia combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing. Julia is designed to be easy and fast. Julia questions notions generally held as "laws of nature" by practitioners of numerical computing: 
1. High-level dynamic programs have to be slow. 
2. One must prototype in one language and then rewrite in another language for speed or deployment, and 
3. There are parts of a system for the programmer, and other parts best left untouched as they are built by the experts. 
We introduce the Julia programming language and its design --- a dance between specialization and abstraction. Specialization allows for custom treatment. Multiple dispatch, a technique from computer science, picks the right algorithm for the right circumstance. Abstraction, what good computation is really about, recognizes what remains the same after differences are stripped away. Abstractions in mathematics are captured as code through another technique from computer science, generic programming. 
Julia shows that one can have machine performance without sacrificing human convenience.

Julia: A Fresh Approach to Numerical Computing

http://www.umiacs.umd.edu/users/vishkin/XMT/HochsteinBVG-journal-paper.pdf

A pilot study to compare programming effort for two parallel programming models

: CONTEXT: Writing software for the current generation of parallel systems requires significant programmer effort, and the community is seeking alternatives that reduce effort while still achieving good performance. OBJECTIVE: Measure the effect of parallel programming models (message-passing vs. PRAM-like) on programmer effort. DESIGN, SETTING, and SUBJECTS: One group of subjects implemented sparse-matrix dense-vector multiplication using message-passing (MPI), and a second group solved the same problem using a PRAM-like model (XMTC). The subjects were students in two graduate-level classes: one class was taught MPI and the other was taught XMTC. MAIN OUTCOME MEASURES: Development time, program correctness. RESULTS: Mean XMTC development time was 4.8 hours less than mean MPI development time (95% confidence interval, 2.0-7.7), a 46% reduction. XMTC programs were more likely to be correct, but the difference in correctness rates was not statistically significant (p=.16). CONCLUSIONS: XMTC solutions for this particular problem required less effort than MPI equivalents, but further studies are necessary which examine different types of problems and different levels of programmer experience.

A Pilot Study to Compare Programming Effort for Two Parallel Programming Models (PREPRINT)

High Performance Computing - HIPC 2007

: Star-P is an interactive parallel scientific computing environment. It aims to make parallel programming more accessible. Star-P borrows ideas from Matlab-P, but is a new development. Currently only a Matlab interface for Star-P is available, but STAR-P is not limited to being a parallel Matlab. It combines all four parallel Matlab approaches in one environment, as described in the parallel Matlab survey: embarrassingly parallel, message passing, backend support and compilation. It also integrates several parallel numerical libraries into one single easy-to-use piece of software. The focus of Star-P is to improve user productivity in parallel programming. We believe that Star-P can dramatically reduce the difficulty of programming parallel computers by reducing the time needed for development and debugging. To achieve productivity, it is important that the user interface is intuitive to the user. For example, a large class of scientific users are already familiar with the Matlab language. So it is beneficial to use Matlab as a parallel programming language. Additions to the language are minimal in keeping with the philosophy to avoid re-learning. Also, as a design goal, our system does not distinguish between serial data and parallel data.

/pdf/star-p-high-productivity-parallel-computing-541krjgdqv.pdf

Star-P: High Productivity Parallel Computing

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Star-P: High Productivity Parallel Computing