39,382 research outputs found
Interactive analysis of high-dimensional association structures with graphical models
Graphical chain models are a capable tool for analyzing multivariate data. However, their practical use may still be cumbersome in some respect since fitting the model requires the application of an intensive selection strategy based on the calculation of an enormous number of different regressions. In this paper, we present a computer system especially designed for the calculation of graphical chain models which is not only planned to automatically carry out the model search but also to visualize the corresponding graph at each stage of the model fit on request by the user. It additionally allows to modify the graph and the model fit interactively
A Compiler and Runtime Infrastructure for Automatic Program Distribution
This paper presents the design and the implementation of a compiler and runtime infrastructure for automatic program distribution. We are building a research infrastructure that enables experimentation with various program partitioning and mapping strategies and the study of automatic distribution's effect on resource consumption (e.g., CPU, memory, communication). Since many optimization techniques are faced with conflicting optimization targets (e.g., memory and communication), we believe that it is important to be able to study their interaction.
We present a set of techniques that enable flexible resource modeling and program distribution. These are: dependence analysis, weighted graph partitioning, code and communication generation, and profiling. We have developed these ideas in the context of the Java language. We present in detail the design and implementation of each of the techniques as part of our compiler and runtime infrastructure. Then, we evaluate our design and present preliminary experimental data for each component, as well as for the entire system
Analysis of Software Binaries for Reengineering-Driven Product Line Architecture\^aAn Industrial Case Study
This paper describes a method for the recovering of software architectures
from a set of similar (but unrelated) software products in binary form. One
intention is to drive refactoring into software product lines and combine
architecture recovery with run time binary analysis and existing clustering
methods. Using our runtime binary analysis, we create graphs that capture the
dependencies between different software parts. These are clustered into smaller
component graphs, that group software parts with high interactions into larger
entities. The component graphs serve as a basis for further software product
line work. In this paper, we concentrate on the analysis part of the method and
the graph clustering. We apply the graph clustering method to a real
application in the context of automation / robot configuration software tools.Comment: In Proceedings FMSPLE 2015, arXiv:1504.0301
Multi-level Visualization of Concurrent and Distributed Computation in Erlang
This paper describes a prototype visualization system
for concurrent and distributed applications programmed
using Erlang, providing two levels of granularity of view. Both
visualizations are animated to show the dynamics of aspects of
the computation.
At the low level, we show the concurrent behaviour of the
Erlang schedulers on a single instance of the Erlang virtual
machine, which we call an Erlang node. Typically there will be
one scheduler per core on a multicore system. Each scheduler
maintains a run queue of processes to execute, and we visualize
the migration of Erlang concurrent processes from one run queue
to another as work is redistributed to fully exploit the hardware.
The schedulers are shown as a graph with a circular layout. Next
to each scheduler we draw a variable length bar indicating the
current size of the run queue for the scheduler.
At the high level, we visualize the distributed aspects of the
system, showing interactions between Erlang nodes as a dynamic
graph drawn with a force model. Speci?cally we show message
passing between nodes as edges and lay out nodes according to
their current connections. In addition, we also show the grouping
of nodes into “s_groups” using an Euler diagram drawn with
circles
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