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Miller, Barton P.

Publications and source records attributed to Miller, Barton P..

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

The Anatomy of Mr. Scan: A Dissection of Performance of an Extreme Scale GPU-Based Clustering Algorithm, In: 2014 5th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems

The emergence of leadership class systems with GPU-equipped nodes has the potential to vastly increase the performance of existing distributed applications. However, the inclusion of GPU computation into existing extreme scale distributed applications can reveal scalability issues that were absent in the CPU version. The issues exposed in scaling by a GPU can become limiting factors to overall application performance. We developed an extreme scale GPU-based application to perform data clustering on multi-billion point datasets. In this application, called Mr. Scan, we ran into several of these performance limiting issues. Through the use of complete end-to-end benchmarking of Mr. Scan (measuring time from reading and distribution to final output), we were able to identify three major sources of real world performance issues: data distribution, GPU load balancing, and system specific issues such as start-up time. These issues comprised a vast majority of the run time of Mr. Scan. Data distribution alone accounted for 68% of the total run time of Mr. Scan when processing 6.5 billion points on Cray Titan at 8192 nodes. With improvements in these areas, we have been able able to cut total run time of Mr. Scan from 17.5 minutes to 8.3 minutes when clustering 6.5 billion points.

Computer Science↗
DOE OSTI2013

Building a Community Infrastructure for Scalable On-Line Performance Analysis Tools around Open|SpeedShop

In this project we created a community tool infrastructure for program development tools targeting Petascale class machines and beyond. This includes tools for performance analysis, debugging, and correctness tools, as well as tuning and optimization frameworks. The developed infrastructure provides a comprehensive and extensible set of individual tool building components. We started with the basic elements necessary across all tools in such an infrastructure followed by a set of generic core modules that allow a comprehensive performance analysis at scale. Further, we developed a methodology and workflow that allows others to add or replace modules, to integrate parts into their own tools, or to customize existing solutions. In order to form the core modules, we built on the existing Open|SpeedShop infrastructure and decomposed it into individual modules that match the necessary tool components. At the same time, we addressed the challenges found in performance tools for petascale systems in each module. When assembled, this instantiation of community tool infrastructure provides an enhanced version of Open|SpeedShop, which, while completely different in its architecture, provides scalable performance analysis for petascale applications through a familiar interface. This project also built upon and enhances capabilities and reusability of project partner components as specified in the original project proposal. The overall project team’s work over the project funding cycle was focused on several areas of research, which are described in the following sections. The reminder of this report also highlights related work as well as preliminary work that supported the project. In addition to the project partners funded by the Office of Science under this grant, the project team included several collaborators who contribute to the overall design of the envisioned tool infrastructure. In particular, the project team worked closely with the other two DOE NNSA laboratories Los Alamos and Sandia leveraging co-funding for Krell by ASC’s Common Computing Environment (CCE) program as laid out in the original proposal. The ASC CCE co-funding, coordinated through LLNL, was for 50% of the total project funding, with the ASC CCE portion of the funding going entirely to Krell, while the ASCR funding itself was split between Krell and the funded partners. This report covers the entire project from both funding sources. Additionally, the team leveraged the expertise of software engineering researchers from Carnegie Mellon University, who specialize in software framework design, in order to achieve a broadly acceptable component framework. The Component Based Tool Framework (CBTF) software has been released to the community. Information related to the project and the released software can be found on the CBTF wiki page at: http://sourceforge.net/p/cbtf/wiki/Home.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Mr. Scan: extreme scale density-based clustering using a tree-based network of GPGPU nodes, In: SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

Density-based clustering algorithms are a widely-used class of data mining techniques that can find irregularly shaped clusters and cluster data without prior knowledge of the number of clusters it contains. DBSCAN is the most well-known density-based clustering algorithm. We introduce our version of DBSCAN, called Mr. Scan, which uses a hybrid parallel implementation that combines the MRNet tree-based distribution network with GPGPU-equipped nodes. Mr. Scan avoids the problems of existing implementations by effectively partitioning the point space and by optimizing DBSCAN's computation over dense data regions. We tested Mr. Scan on both a geolocated Twitter dataset and image data obtained from the Sloan Digital Sky Survey. At its largest scale, Mr. Scan clustered 6.5 billion points from the Twitter dataset on 8,192 GPU nodes on Cray Titan in 17.3 minutes. All other parallel DBSCAN implementations have only demonstrated the ability to cluster up to 100 million points.

Computer Science↗
DOE OSTI2012

Institute for Scalable Application Development Software

Work by the University of Wisconsin as part of the DOE SciDAC CScADS includes the following accomplishments: Research on tool componentization, with concentration on the: InstructionAPI and InstructionSemanticsAPI ParseAPI DataflowAPI Co-organized a series of high successful workshops with Prof. John Mellor-Crummey, Rice University, on Performance Tools for Petascale Computing, held in Snowbird, Utah and Lake Tahoe, California in July or August of 2007 through 2012. Investigated the use of multicore in numerical libraries Dyninst porting to 32- and 64bit Power/PowerPC (including BlueGene) and 32- and 64-bit Pentium platforms. Applying our toolkits to advanced problems in binary code parsing associated with dealing with legacy and malicious code.

97 MATHEMATICS AND COMPUTING↗