Search

Currently, researchers designing and implementing large-scale overlay services employ disparate techniques at each stage in the production cycle: design, implementation, experimentation, and evaluation. As a result, complex and tedious tasks are often duplicated leading to ineffective resource use and difficulty in fairly comparing competing algorithms. In this paper, we present MACEDON, an infrastructure that provides facilities to: i) specify distributed algorithms in a concise domain-specific language; ii) generate code that executes in popular evaluation infrastructures and in live networks; iii) leverage an overlay-generic API to simplify the interoperability of algorithm implementations and applications; and iv) enable consistent experimental evaluation. We have used MACEDON to implement and evaluate a number of algorithms, including AMMO, Bullet, Chord, NICE, Overcast, Pastry, Scribe, and SplitStream, typically with only a few hundred lines of MACEDON code. Using our infrastructure, we are able to accurately reproduce or exceed published results and behavior demonstrated by current publicly available implementations Duke Univ., Durham, NC, USAQC 20140704

The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently been developed. In this work, we present a density compiler for a probabilistic language with failure and both discrete and continuous distributions, and provide a proof of its soundness. The compiler greatly reduces the development effort of domain experts, which we demonstrate by solving inference problems from various scientific applications, such as modelling the global carbon cycle, using a standard Markov chain Monte Carlo framework., Logical Methods in Computer Science ; Volume 13, Issue 2 ; 1860-5974