An Overview of the Mjølner BETA System

The Mjølner BETA System is an integrated and interactive programming environment with support for industrial object oriented programming. The Mjølner BETA System is a result of the Scandinavian research project Mjølner. The integration of the various tools in the Mjølner BETA System is established by insisting that all tools in the system utilizes on single representation of the program. This representation is abstract syntax trees (ASTs). All manipulations of the ASTs by the various tools are done utilizing the metaprogramming system which defines an interface to the AST and ways to manipulate the AST

Portable reflection for C++ with Mirror

Reflection and reflective programming can be used in a broad range of tasks like implementation of serialization operations, remote procedure calls, scripting, automated user interface generation, implementation of several software design patterns, etc. C++ as one of the most prevalent programming languages however, for various reasons, lacks a standardized reflection facility. In this paper we present Mirror - a portable library adding reflection to C++ with a command-line utility automating its usage. This library supports functional style static compile-time reflection and metaprogramming and also provides two different object-oriented run-time polymorphic layers for dynamic reflection

PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development

This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. In the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on automatic, relational-database style optimization to figure out how to stage distributed computations. However, in the small, PlinyCompute presents the capable systems programmer with a persistent object data model and API (the "PC object model") and associated memory management system that has been designed from the ground-up for high performance, distributed, data-intensive computing. This contrasts with most other Big Data systems, which are constructed on top of the Java Virtual Machine (JVM), and hence must at least partially cede performance-critical concerns such as memory management (including layout and de/allocation) and virtual method/function dispatch to the JVM. This hybrid approach---declarative in the large, trusting the programmer's ability to utilize PC object model efficiently in the small---results in a system that is ideal for the development of reusable, data-intensive tools and libraries. Through extensive benchmarking, we show that implementing complex objects manipulation and non-trivial, library-style computations on top of PlinyCompute can result in a speedup of 2x to more than 50x or more compared to equivalent implementations on Spark.Comment: 48 pages, including references and Appendi

Implementing the Factory Pattern with the Help of Reflection

Reflection, reflection-based programming and metaprogramming are valuable tools for many programming tasks, like the implementation of persistence and serialization-like operations, object-relational mapping, remote method invocation, automatic generation of user-interfaces, etc., and also for the implementation of several design patterns. C++ as one of the most prevalent programming languages still lacks support for standardised, compiler-assisted reflection. In this paper we introduce in short the Mirror reflection library which is a part of an ongoing effort to add reflection to C++ and we will show how reflection can be used to greatly simplify the implementation of object factories - classes constructing instances of other classes from various external data representations

Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging

Many graphics and vision problems can be expressed as non-linear least squares optimizations of objective functions over visual data, such as images and meshes. The mathematical descriptions of these functions are extremely concise, but their implementation in real code is tedious, especially when optimized for real-time performance on modern GPUs in interactive applications. In this work, we propose a new language, Opt (available under http://optlang.org), for writing these objective functions over image- or graph-structured unknowns concisely and at a high level. Our compiler automatically transforms these specifications into state-of-the-art GPU solvers based on Gauss-Newton or Levenberg-Marquardt methods. Opt can generate different variations of the solver, so users can easily explore tradeoffs in numerical precision, matrix-free methods, and solver approaches. In our results, we implement a variety of real-world graphics and vision applications. Their energy functions are expressible in tens of lines of code, and produce highly-optimized GPU solver implementations. These solver have performance competitive with the best published hand-tuned, application-specific GPU solvers, and orders of magnitude beyond a general-purpose auto-generated solver