Profiling large-scale lazy functional programs

The LOLITA natural language processing system is an example of one of the ever increasing number of large-scale systems written entirely in a functional programming language. The system consists of over 50,000 lines of Haskell code and is able to perform a number of tasks such as semantic and pragmatic analysis of text, context scanning and query analysis. Such a system is more useful if the results are calculated in real-time, therefore the efficiency of such a system is paramount. For the past three years we have used profiling tools supplied with the Haskell compilers GHC and HBC to analyse and reason about our programming solutions and have achieved good results; however, our experience has shown that the profiling life-cycle is often too long to make a detailed analysis of a large system possible, and the profiling results are often misleading. A profiling system is developed which allows three types of functionality not previously found in a profiler for lazy functional programs. Firstly, the profiler is able to produce results based on an accurate method of cost inheritance. We have found that this reduces the possibility of the programmer obtaining misleading profiling results. Secondly, the programmer is able to explore the results after the execution of the program. This is done by selecting and deselecting parts of the program using a post-processor. This greatly reduces the analysis time as no further compilation, execution or profiling of the program is needed. Finally, the new profiling system allows the user to examine aspects of the run-time call structure of the program. This is useful in the analysis of the run-time behaviour of the program. Previous attempts at extending the results produced by a profiler in such a way have failed due to the exceptionally high overheads. Exploration of the overheads produced by the new profiling scheme show that typical overheads in profiling the LOLITA system are: a 10% increase in compilation time; a 7% increase in executable size and a 70% run-time overhead. These overheads mean a considerable saving in time in the detailed analysis of profiling a large, lazy functional program

Property-aware program sampling

Monitoring or profiling programs provides us with an understanding for its further improvement and analysis. Typically, for monitoring or profiling, the program is instrumented to execute additional code that collects necessary data. However, a widely-understood problem with this approach is that program instrumentation can result in significant execution overhead. A number of techniques based on statistical sampling have been proposed to reduce this overhead. Statistical sampling based instrumentation techniques, although effective in reducing the overall overhead, often lead to poor coverage or incomplete results. The contribution of this work is a profiling technique that we call property-aware program sampling. Our sampling technique uses program slicing to reduce the scope of instrumentation and slice fragmentsto decompose large program slices into more manageable, logically related parts for instrumentation, thereby improving the scalability of monitoring and profiling techniques. The technical underpinnings of our work include the notion of slice fragments and an efficient technique for computing a reduced set of slice fragments

Compiler analysis for trace-level speculative multithreaded architectures

Trace-level speculative multithreaded processors exploit trace-level speculation by means of two threads working cooperatively. One thread, called the speculative thread, executes instructions ahead of the other by speculating on the result of several traces. The other thread executes speculated traces and verifies the speculation made by the first thread. In this paper, we propose a static program analysis for identifying candidate traces to be speculated. This approach identifies large regions of code whose live-output values may be successfully predicted. We present several heuristics to determine the best opportunities for dynamic speculation, based on compiler analysis and program profiling information. Simulation results show that the proposed trace recognition techniques achieve on average a speed-up close to 38% for a collection of SPEC2000 benchmarks.Peer ReviewedPostprint (published version

Platform independent profiling of a QCD code

The supercomputing platforms available for high performance computing based research evolve at a great rate. However, this rapid development of novel technologies requires constant adaptations and optimizations of the existing codes for each new machine architecture. In such context, minimizing time of efficiently porting the code on a new platform is of crucial importance. A possible solution for this common challenge is to use simulations of the application that can assist in detecting performance bottlenecks. Due to prohibitive costs of classical cycle-accurate simulators, coarse-grain simulations are more suitable for large parallel and distributed systems. We present a procedure of implementing the profiling for openQCD code [1] through simulation, which will enable the global reduction of the cost of profiling and optimizing this code commonly used in the lattice QCD community. Our approach is based on well-known SimGrid simulator [2], which allows for fast and accurate performance predictions of HPC codes. Additionally, accurate estimations of the program behavior on some future machines, not yet accessible to us, are anticipated

Deformation mechanics of deep surface flaw cracks

A combined analytical and experimental program was conducted to determine the deformation characteristics of deep surface cracks in Mode I loading. An approximate plane finite element analysis was performed to make a parameter study on the influence of crack depth, crack geometry, and stress level on plastic zones, crack opening displacement, and back surface dimpling in Fe-3Si steel and 2219-T87 aluminum. Surface replication and profiling techniques were used to examine back surface dimple configurations in 2219-T87 aluminum. Interferometry and holography were used to evaluate the potential of various optical techniques to detect small surface dimples on large surface areas

Hydrographic data from R/V endeavor cruise #90

The final cruise of the NSF sponsored Warm Core Rings Program studied a Warm Core Ring (WCR) in the Fall of 1982 as it formed from a large northward meander of the Gulf Stream. This ring, known as 82-H or the eighth ring identified in 1982, formed over the New England Seamounts near 39.5 deg N, 65 deg W. Surveys using Expendable Bathythermographs, Conductivity-Temperature-Depth-Oxygen stations and Doppler Current Profiling provide a look at the genesis of a WCR. These measurements reveal that WCR 82-H separated from the Gulf Stream sometime between October 2-5. This ring was a typical WCR with a diameter of about 200 km and speeds in the high velocity core of the 175 cm/sec. Satellite imagery of 82-H following the cruise showed that it drifted WSW in the Slope Water region at almost 9 km/day, had at least one interaction with the Gulf Stream and was last observed on February 8, 1983 at 39 deg N, 72 deg W

Comparison of Four ChIP-Seq Analytical Algorithms Using Rice Endosperm H3K27 Trimethylation Profiling Data

Chromatin immunoprecipitation coupled with high throughput DNA Sequencing (ChIP-Seq) has emerged as a powerful tool for genome wide profiling of the binding sites of proteins associated with DNA such as histones and transcription factors. However, no peak calling program has gained consensus acceptance by the scientific community as the preferred tool for ChIP-Seq data analysis. Analyzing the large data sets generated by ChIP-Seq studies remains highly challenging for most molecular biology laboratories

The Annotation, Mapping, Expression and Network (AMEN) suite of tools for molecular systems biology

<p>Abstract</p> <p>Background</p> <p>High-throughput genome biological experiments yield large and multifaceted datasets that require flexible and user-friendly analysis tools to facilitate their interpretation by life scientists. Many solutions currently exist, but they are often limited to specific steps in the complex process of data management and analysis and some require extensive informatics skills to be installed and run efficiently.</p> <p>Results</p> <p>We developed the Annotation, Mapping, Expression and Network (AMEN) software as a stand-alone, unified suite of tools that enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein-protein interaction, expression profiling and proteomics data. The current version provides modules for (i) uploading and pre-processing data from microarray expression profiling experiments, (ii) detecting groups of significantly co-expressed genes, and (iii) searching for enrichment of functional annotations within those groups. Moreover, the user interface is designed to simultaneously visualize several types of data such as protein-protein interaction networks in conjunction with expression profiles and cellular co-localization patterns. We have successfully applied the program to interpret expression profiling data from budding yeast, rodents and human.</p> <p>Conclusion</p> <p>AMEN is an innovative solution for molecular systems biological data analysis freely available under the GNU license. The program is available via a website at the Sourceforge portal which includes a user guide with concrete examples, links to external databases and helpful comments to implement additional functionalities. We emphasize that AMEN will continue to be developed and maintained by our laboratory because it has proven to be extremely useful for our genome biological research program.</p