As scientific computing users migrate to petaflop platforms that promise to generate multi-terabyte datasets, there is a growing need in the community to be able to embed sophisticated analysis algorithms in the computing platforms' storage systems. Data Warehouse Appliances (DWAs) are attractive for this work, due to their ability to store and process massive datasets efficiently. While DWAs have been utilized effectively in data-mining and informatics applications, they remain largely unproven in scientific workloads. In this paper we present our experiences in adapting two mesh analysis algorithms to function on five different DWA architectures: two Netezza database appliances, an XtremeData dbX database, a LexisNexis DAS, and multiple Hadoop MapReduce clusters. The main contribution of this work is insight into the differences between these DWAs from a user's perspective. In addition, we present performance measurements for ten DWA systems to help understand the impact of different architectural trade-offs in these systems.
This paper describes our approach to adapting a text document similarity classifier based on the Term Frequency Inverse Document Frequency (TFIDF) metric [11] to reconfigurable hardware. The TFIDF classifier is used to detect web attacks in HTTP data. In our reconfigurable hardware approach, we design a streaming, real-time classifier by simplifying an existing sequential algorithm and manipulating the classifier's model to allow decision information to be represented compactly. We have developed a set of software tools to help automate the process of converting training data to synthesizable hardware and to provide a means of trading off between accuracy and resource utilization. The Xilinx Virtex 5-LX implementation requires two orders of magnitude less memory than the original algorithm. At 166MB/s (80X the software) the hardware implementation is able to achieve Gigabit network throughput at the same accuracy as the original algorithm.
The Feature Characterization Library (FCLib) is a software library that simplifies the process of interrogating, analyzing, and understanding complex data sets generated by finite element applications. This document provides an overview of the library, a description of both the design philosophy and implementation of the library, and examples of how the library can be utilized to extract understanding from raw datasets.
Field programmable gate arrays (FPGAs) have been used as alternative computational de-vices for over a decade; however, they have not been used for traditional scientific com-puting due to their perceived lack of floating-point performance. In recent years, there hasbeen a surge of interest in alternatives to traditional microprocessors for high performancecomputing. Sandia National Labs began two projects to determine whether FPGAs wouldbe a suitable alternative to microprocessors for high performance scientific computing and,if so, how they should be integrated into the system. We present results that indicate thatFPGAs could have a significant impact on future systems. FPGAs have thepotentialtohave order of magnitude levels of performance wins on several key algorithms; however,there are serious questions as to whether the system integration challenge can be met. Fur-thermore, there remain challenges in FPGA programming and system level reliability whenusing FPGA devices.4 AcknowledgmentArun Rodrigues provided valuable support and assistance in the use of the Structural Sim-ulation Toolkit within an FPGA context. Curtis Janssen and Steve Plimpton provided valu-able insights into the workings of two Sandia applications (MPQC and LAMMPS, respec-tively).5
Visualization in scientific computing refers to the process of transforming data produced by a simulation into graphical representations that help scientific users interpret the results. While the back-end rendering phase of this work can be performed efficiently in graphics card hardware, the front-end 'post processing' portion of visualization is currently performed entirely in software. Field-Programmable Gate Arrays (FPGAs) are an attractive option for accelerating post-processing operations because they enable users to offload computations into reconfigurable hardware. A key challenge in utilizing FPGAs for this work is developing an infrastructure that allows FPGAs to be integrated into a distributed visualization system. We propose a networked approach, where each post-processing FPGA is equipped with specialized network interface (NI) hardware that is capable of transporting graphics commands across the network to existing rendering resources. In this paper we discuss a NI for FPGAs that is comprised of a Chromium OpenGL interface, a TCP Offload Engine, and a Gigabit Ethernet module. A prototype system has been tested for a distributed isosurfacing application.