Mobile Erlang computations to enhance performance, resource usage and reliability

A software solution consists of multiple autonomous computations (i.e., execution threads) that execute concurrently (or apparently concurrently) over one or more locations to achieve a specific goal. Centralized solutions execute all computations on the same lo- cation while decentralized solutions disperse computations across different locations to increase scalability, enhance performance and reliability. Every location affects its executing computations both directly (e.g., the lack of a resource may prohibit a computation from progressing) and indirectly (e.g., an over- loaded location may slow down a computation). In a distributed environment, application developers have the luxury of executing each computation over its best-fitting location; the location (a) upon which the computation can achieve the best performance and (b) which guarantees the computation’s livelihood. Ideally, the decision to execute a computation over a location instead of another also load-balances the use of available resources such that it has the least impact over other computations (e.g., a computation should not execute over an already overloaded location further slowing down its computations). Application developers can only execute computations over their best-fitting location if their distributed programming language provides abstractions that allow them to control the locality of computations both before they are started and during their execution. In the rest of this document, section 2 briefly justifies why these two forms of locality control are required and section 3 outlines the issues that arise, and will be tackled in the talk to be held at CSAW 2014, by them.peer-reviewe

Julia: A Fresh Approach to Numerical Computing

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.Comment: 37 page

Research in computer science

Several short summaries of the work performed during this reporting period are presented. Topics discussed in this document include: (1) resilient seeded errors via simple techniques; (2) knowledge representation for engineering design; (3) analysis of faults in a multiversion software experiment; (4) implementation of parallel programming environment; (5) symbolic execution of concurrent programs; (6) two computer graphics systems for visualization of pressure distribution and convective density particles; (7) design of a source code management system; (8) vectorizing incomplete conjugate gradient on the Cyber 203/205; (9) extensions of domain testing theory and; (10) performance analyzer for the pisces system

Research in computer science

The research efforts of University of Virginia students under a NASA sponsored program are summarized and the status of the program is reported. The research includes: testing method evaluations for N version programming; a representation scheme for modeling three dimensional objects; fault tolerant protocols for real time local area networks; performance investigation of Cyber network; XFEM implementation; and vectorizing incomplete Cholesky conjugate gradients

Research in computer science

Synopses are given for NASA supported work in computer science at the University of Virginia. Some areas of research include: error seeding as a testing method; knowledge representation for engineering design; analysis of faults in a multi-version software experiment; implementation of a parallel programming environment; two computer graphics systems for visualization of pressure distribution and convective density particles; task decomposition for multiple robot arms; vectorized incomplete conjugate gradient; and iterative methods for solving linear equations on the Flex/32