Visualization of program performance on concurrent computers

A distributed memory concurrent computer (such as a hypercube computer) is inherently a complex system involving the collective and simultaneous interaction of many entities engaged in computation and communication activities. Program performance evaluation in concurrent computer systems requires methods and tools for observing, analyzing, and displaying system performance. This dissertation describes a methodology for collecting and displaying, via a unique graphical approach, performance measurement information from (possibly large) concurrent computer systems. Performance data are generated and collected via instrumentation. The data are then reduced via conventional cluster analysis techniques and converted into a pictorial form to highlight important aspects of program states during execution. Local and summary statistics are calculated. Included in the suite of defined metrics are measures for quantifying and comparing amounts of computation and communication. A novel kind of data plot is introduced to visually display both temporal and spatial information describing system activity. Phenomena such as hot spots of activity are easily observed, and in some cases, patterns inherent in the application algorithms being studied are highly visible. The approach also provides a framework for a visual solution to the problem of mapping a given parallel algorithm to an underlying parallel machine. A prototype implementation applied to several case studies is presented to demonstrate the feasibility and power of the approach

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

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

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Towards Exascale Scientific Metadata Management

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions

Tools for Search Tree Visualization: The APT Tool

The control part of the execution of a constraint logic program can be conceptually shown as a search-tree, where nodes correspond to calis, and whose branches represent conjunctions and disjunctions. This tree represents the search space traversed by the program, and has also a direct relationship with the amount of work performed by the program. The nodes of the tree can be used to display information regarding the state and origin of instantiation of the variables involved in each cali. This depiction can also be used for the enumeration process. These are the features implemented in APT, a tool which runs constraint logic programs while depicting a (modified) search-tree, keeping at the same time information about the state of the variables at every moment in the execution. This information can be used to replay the execution at will, both forwards and backwards in time. These views can be abstracted when the size of the execution requires it. The search-tree view is used as a framework onto which constraint-level visualizations (such as those presented in the following chapter) can be attached

Mathematical Modelling of Chemical Diffusion through Skin using Grid-based PSEs

A Problem Solving Environment (PSE) with connections to remote distributed Grid processes is developed. The Grid simulation is itself a parallel process and allows steering of individual or multiple runs of the core computation of chemical diffusion through the stratum corneum, the outer layer of the skin. The effectiveness of this Grid-based approach in improving the quality of the simulation is assessed