BACKGROUND: In the current era of scientific research, efficient communication of information is paramount. As such, the nature of scholarly and scientific communication is changing; cyberinfrastructure is now absolutely necessary and new media are allowing information and knowledge to be more interactive and immediate. One approach to making knowledge more accessible is the addition of machine-readable semantic data to scholarly articles. RESULTS: The Word add-in presented here will assist authors in this effort by automatically recognizing and highlighting words or phrases that are likely information-rich, allowing authors to associate semantic data with those words or phrases, and to embed that data in the document as XML. The add-in and source code are publicly available at http://www.codeplex.com/UCSDBioLit. CONCLUSIONS: The Word add-in for ontology term recognition makes it possible for an author to add semantic data to a document as it is being written and it encodes these data using XML tags that are effectively a standard in life sciences literature. Allowing authors to mark-up their own work will help increase the amount and quality of machine-readable literature metadata.

The use of threads is becoming commonplace in both sequential and parallel programs. This paper describes our design and initial experience with non-trace based performance instrumentation techniques for threaded programs. Our goal is to provide detailed performance data while maintaining control of instrumentation costs. We have extended Paradyn's dynamic instrumentation (which can instrument programs without recompiling or relinking) to handle threaded programs.Controlling instrumentation costs means efficient instrumentation code and avoiding locks in the instrumentation. Our design is based on low contention data structures. To associate performance data with individual threads, we have all threads share the same instrumentation code and assign each thread with its own private copy of performance counters or timers. The asynchrony in a threaded program poses a major challenge to dynamic instrumentation. To implement time-based metrics on a per-thread basis, we need to instrument thread context switches, which can cause instrumentation code to interleave. Interleaved instrumentation can not only corrupt performance data, but can also cause a scenario we call self-deadlock where an instrumentation code deadlocks a thread. We introduce thread-conscious locks to avoid self-deadlock, and per-thread virtual CPU timers to reduce the chance of interleaved instrumentation accessing the same performance counter or timer, and to reduce the number of expensive timer calls at thread context switches.Our initial implementation is on SPARC Solaris 2.5 and 2.6 including multiprocessor Sun UltraSPARC Enterprise machines. We tested our tool on large multithreaded applications, including the Java Virtual Machine (JVM). We show how our new techniques helped us to speed up a Java graphics native method by 42% and consequently increase by 24% the amount of work that can be done in unit time in a game applet.

Performance is a critical issue in current massively parallel processors. However, delivery of adequate performance is not automatic and performance evaluation tools are required in order to help the programmer to understand the behaviour of a parallel program. In recent years, a wide variety of tools have been developed for this purpose, including tools for monitoring and evaluating performance and viaualization tools. However, these tools do not provide an abstract representation of performance. Massively parallel processors can generate a huge amount of performance data, and sophisticated methods for representing and displaying these data (e.g. visual and aural) are required. Performance views are not scalable in general and do not represent an abstraction of the performance data. The do-loop-surface display is proposed as an abstract representation of the performance of a particular do-loop in a program. It has been used to improve the performance of a matrix multiply parallel algorithm as well as to understand the behaviour of the following applications: matrix transposition (TRANS1) and fast fourier transform (FFT1) from the Genesis benchmarks, and the kernel of a fluid dynamics package (FIRE). These experiments were performed on a CM-5, a Meiko CS-1 and a PARSYS Supernode. The examples demonstrate that the do-loop-surface display is a useful way to represent performance. It is implemented using AVS (application visualization system), a standard data visualization package.