A Monitoring Language for Run Time and Post-Mortem Behavior Analysis and Visualization

UFO is a new implementation of FORMAN, a declarative monitoring language, in which rules are compiled into execution monitors that run on a virtual machine supported by the Alamo monitor architecture.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth International Workshop on Automated Debugging (AADEBUG 2003), September 2003, Ghent. cs.SE/030902

ShenZhen transportation system (SZTS): a novel big data benchmark suite

Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets

Complexity plots

In this paper, we present a novel visualization technique for assisting in observation and analysis of algorithmic\ud complexity. In comparison with conventional line graphs, this new technique is not sensitive to the units of\ud measurement, allowing multivariate data series of different physical qualities (e.g., time, space and energy) to be juxtaposed together conveniently and consistently. It supports multivariate visualization as well as uncertainty visualization. It enables users to focus on algorithm categorization by complexity classes, while reducing visual impact caused by constants and algorithmic components that are insignificant to complexity analysis. It provides an effective means for observing the algorithmic complexity of programs with a mixture of algorithms and blackbox software through visualization. Through two case studies, we demonstrate the effectiveness of complexity plots in complexity analysis in research, education and application

The CIAO Multi-Dialect Compiler and System: An Experimentation Workbench for Future (C)LP Systems

CIAO is an advanced programming environment supporting Logic and Constraint programming. It offers a simple concurrent kernel on top of which declarative and non-declarative extensions are added via librarles. Librarles are available for supporting the ISOProlog standard, several constraint domains, functional and higher order programming, concurrent and distributed programming, internet programming, and others. The source language allows declaring properties of predicates via assertions, including types and modes. Such properties are checked at compile-time or at run-time. The compiler and system architecture are designed to natively support modular global analysis, with the two objectives of proving properties in assertions and performing program optimizations, including transparently exploiting parallelism in programs. The purpose of this paper is to report on recent progress made in the context of the CIAO system, with special emphasis on the capabilities of the compiler, the techniques used for supporting such capabilities, and the results in the áreas of program analysis and transformation already obtained with the system

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