A peer to peer approach to large scale information monitoring

Issued as final reportNational Science Foundation (U.S.

CampProf: A Visual Performance Analysis Tool for Memory Bound GPU Kernels

Current GPU tools and performance models provide some common architectural insights that guide the programmers to write optimal code. We challenge these performance models, by modeling and analyzing a lesser known, but very severe performance pitfall, called 'Partition Camping', in NVIDIA GPUs. Partition Camping is caused by memory accesses that are skewed towards a subset of the available memory partitions, which may degrade the performance of memory-bound CUDA kernels by up to seven-times. No existing tool can detect the partition camping effect in CUDA kernels. We complement the existing tools by developing 'CampProf', a spreadsheet based, visual analysis tool, that detects the degree to which any memory-bound kernel suffers from partition camping. In addition, CampProf also predicts the kernel's performance at all execution configurations, if its performance parameters are known at any one of them. To demonstrate the utility of CampProf, we analyze three different applications using our tool, and demonstrate how it can be used to discover partition camping. We also demonstrate how CampProf can be used to monitor the performance improvements in the kernels, as the partition camping effect is being removed. The performance model that drives CampProf was developed by applying multiple linear regression techniques over a set of specific micro-benchmarks that simulated the partition camping behavior. Our results show that the geometric mean of errors in our prediction model is within 12% of the actual execution times. In summary, CampProf is a new, accurate, and easy-to-use tool that can be used in conjunction with the existing tools to analyze and improve the overall performance of memory-bound CUDA kernels

How to Write a Good Paper in Computer Science and How Will It Be Measured by ISI Web of Knowledge

The academic world has come to place enormous weight on bibliometric measures to assess the value of scientific publications. Our paper has two major goals. First, we discuss the limits of numerical assessment tools as applied to computer science publications. Second, we give guidelines on how to write a good paper, where to submit the manuscript, and how to deal with the reviewing process. We report our experience as editors of International Journal of Computers Communications & Control (IJCCC). We analyze two important aspects of publishing: plagiarism and peer reviewing. As an example, we discuss the promotion assessment criteria used in the Romanian academic system. We express openly our concerns about how our work is evaluated, especially by the existent bibliometric products. Our conclusion is that we should combine bibliometric measures with human interpretation. Keywords: scientific publication, publication assessment, plagiarism, reviewing, bibliometric indices

Compression Methods for Structured Floating-Point Data and their Application in Climate Research

The use of new technologies, such as GPU boosters, have led to a dramatic increase in the computing power of High-Performance Computing (HPC) centres. This development, coupled with new climate models that can better utilise this computing power thanks to software development and internal design, led to the bottleneck moving from solving the differential equations describing Earth’s atmospheric interactions to actually storing the variables. The current approach to solving the storage problem is inadequate: either the number of variables to be stored is limited or the temporal resolution of the output is reduced. If it is subsequently determined that another vari- able is required which has not been saved, the simulation must run again. This thesis deals with the development of novel compression algorithms for structured floating-point data such as climate data so that they can be stored in full resolution. Compression is performed by decorrelation and subsequent coding of the data. The decorrelation step eliminates redundant information in the data. During coding, the actual compression takes place and the data is written to disk. A lossy compression algorithm additionally has an approx- imation step to unify the data for better coding. The approximation step reduces the complexity of the data for the subsequent coding, e.g. by using quantification. This work makes a new scientific contribution to each of the three steps described above. This thesis presents a novel lossy compression method for time-series data using an Auto Regressive Integrated Moving Average (ARIMA) model to decorrelate the data. In addition, the concept of information spaces and contexts is presented to use information across dimensions for decorrela- tion. Furthermore, a new coding scheme is described which reduces the weaknesses of the eXclusive-OR (XOR) difference calculation and achieves a better compression factor than current lossless compression methods for floating-point numbers. Finally, a modular framework is introduced that allows the creation of user-defined compression algorithms. The experiments presented in this thesis show that it is possible to in- crease the information content of lossily compressed time-series data by applying an adaptive compression technique which preserves selected data with higher precision. An analysis for lossless compression of these time- series has shown no success. However, the lossy ARIMA compression model proposed here is able to capture all relevant information. The reconstructed data can reproduce the time-series to such an extent that statistically rele- vant information for the description of climate dynamics is preserved. Experiments indicate that there is a significant dependence of the com- pression factor on the selected traversal sequence and the underlying data model. The influence of these structural dependencies on prediction-based compression methods is investigated in this thesis. For this purpose, the concept of Information Spaces (IS) is introduced. IS contributes to improv- ing the predictions of the individual predictors by nearly 10% on average. Perhaps more importantly, the standard deviation of compression results is on average 20% lower. Using IS provides better predictions and consistent compression results. Furthermore, it is shown that shifting the prediction and true value leads to a better compression factor with minimal additional computational costs. This allows the use of more resource-efficient prediction algorithms to achieve the same or better compression factor or higher throughput during compression or decompression. The coding scheme proposed here achieves a better compression factor than current state-of-the-art methods. Finally, this paper presents a modular framework for the development of compression algorithms. The framework supports the creation of user- defined predictors and offers functionalities such as the execution of bench- marks, the random subdivision of n-dimensional data, the quality evalua- tion of predictors, the creation of ensemble predictors and the execution of validity tests for sequential and parallel compression algorithms. This research was initiated because of the needs of climate science, but the application of its contributions is not limited to it. The results of this the- sis are of major benefit to develop and improve any compression algorithm for structured floating-point data

Myths and Legends in High-Performance Computing

In this thought-provoking article, we discuss certain myths and legends that are folklore among members of the high-performance computing community. We gathered these myths from conversations at conferences and meetings, product advertisements, papers, and other communications such as tweets, blogs, and news articles within and beyond our community. We believe they represent the zeitgeist of the current era of massive change, driven by the end of many scaling laws such as Dennard scaling and Moore's law. While some laws end, new directions are emerging, such as algorithmic scaling or novel architecture research. Nevertheless, these myths are rarely based on scientific facts, but rather on some evidence or argumentation. In fact, we believe that this is the very reason for the existence of many myths and why they cannot be answered clearly. While it feels like there should be clear answers for each, some may remain endless philosophical debates, such as whether Beethoven was better than Mozart. We would like to see our collection of myths as a discussion of possible new directions for research and industry investment

Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities

A Spatial Characterization of the Sagittarius Dwarf Galaxy Tidal Tails

We measure the spatial density of F turnoff stars in the Sagittarius dwarf tidal stream, from Sloan Digital Sky Survey (SDSS) data, using statistical photometric parallax. We find a set of continuous, consistent parameters that describe the leading Sgr stream's position, direction, and width for 15 stripes in the North Galactic Cap, and 3 stripes in the South Galactic Cap. We produce a catalog of stars that has the density characteristics of the dominant leading Sgr tidal stream that can be compared with simulations. We find that the width of the leading (North) tidal tail is consistent with recent triaxial and axisymmetric halo model simulations. The density along the stream is roughly consistent common disruption models in the North, but possibly not in the South. We explore the possibility that one or more of the dominant Sgr streams has been mis-identified, and that one or more of the `bifurcated' pieces is the real Sgr tidal tail, but we do not reach definite conclusions. If two dwarf progenitors are assumed, fits to the planes of the dominant and `bifurcated' tidal tails favor an association of the Sgr dwarf spheroidal galaxy with the dominant Southern stream and the `bifurcated' stream in the North. In the North Galactic Cap, the best fit Hernquist density profile for the smooth component of the stellar halo is oblate, with a flattening parameter q = 0.53, and a scale length of r_0 = 6.73. The Southern data for both the tidal debris and the smooth component of the stellar halo do not match the model fits to the North, although the stellar halo is still overwhelmingly oblate. Finally, we verify that we can reproduce the parameter fits on the asynchronous Milkyway@home volunteer computing platform.Comment: 35 pages, 8 figures, 9 tables. Accepted for publication in The Astrophysical Journa