The exponential growth of available data has increased the need for
interactive exploratory analysis. Dataset can no longer be understood
through manual crawling and simple statistics. In Geographical
Information Systems (GIS), the dataset is often composed of events
localized in space and time; and visualizing such a dataset involves
building a map of where the events occurred.
We focus in this paper on events that are localized among three
dimensions (latitude, longitude, and time), and on computing the first
step of the visualization pipeline, space-time kernel density
estimation (STKDE), which is most computationally expensive. Starting
from a gold standard implementation, we show how algorithm design and
engineering, parallel decomposition, and scheduling can be applied to
bring near real-time computing to space-time kernel density
estimation. We validate our techniques on real world datasets
extracted from infectious disease, social media, and ornithology.
The polyhedral model has proven to be very useful to optimize and parallelize a particular class of compute intensive application kernels. A polyhedral optimizer needs to have affine functions defining loop bounds, memory accesses and branching conditions. Unfortunately, this information is not always available at compile time. To broaden the scope of polyhedral optimization opportunities, runtime information can be considered. This talk will highlight the challenges of integrating polyhedral optimization in runtime systems:
- When and how to detect opportunities for polyhedral optimization?
- How to model the observed runtime behavior in a polyhedral fashion?
- How to deal at runtime with the complexity of polyhedral algorithm?
In this talk, I will present a data structure for the analysis of correlations of alarms, for root-cause analysis or prediction purposes. This is a joint work with Marc-Olivier Buob and Maxime Raynal (intern). A sequence of alarms is modeled by a directed acyclic graph. The nodes of the graph are the alarms, that are represented by a symbol and an interval of time. An arc of the graph is interpreted as a potential causality between two alarms. I will first show how to build a "compact" structure storing all the potential causal sequences of alarms and then how to weight this structure so that the actual correlations can be detected. The efficiency of the approach will be demonstrated on toy examples.