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A newly-invented, distributed, high-performance graphical processing framework that simulates complex radio frequency (RF) propagation has been developed and demonstrated. The approach uses an advanced computer architecture and intensive multi-core system to enable highperformance data analysis at the fidelity necessary to design and develop modern sensor systems. This widely applicable simulation...
Various error models are being used in simulation of voltage-scaled arithmetic units to examine application-level tolerance of timing violations. The selection of an error model needs further consideration, as differences in error models drastically affect the performance of the application. Specifically, floating point arithmetic units (FPUs) have architectural characteristics that characterize its...
3D memory is becoming an increasingly popular technology to overcome the performance gap between memory and processors. It has led to the development of new architectures with scratchpad memory, which offer high bandwidth and user-controlled access features. The ideal performance of this scratchpad memory is peak bandwidth for any random block access. However, 3D memories come with their constraints...
This paper presents an approach for improving the overall performance of a general purpose application running as a task graph on a many-core neuromorphic supercomputer. Our task graph framework is based on graceful degradation and amelioration paradigms that strive to achieve high reliability and performance by incorporating fault tolerance and task spawning features. The optimization is applied...
Not only networks are ubiquitous in real world, but also networked dynamics provide a more precise scheme required to better understanding of surrounding phenomena and data. This network-centric approach can be applied to analyze time series data of any type. An abundant prevalence of time series observations demand inference of causality in addition to accurate prediction. In this paper, a fuzzy...
Network Monitoring Systems (NMS) are an important part of protecting Army and enterprise networks. As governments and corporations grow, the amount of traffic data collected by NMS grows proportionally. To protect users against emerging threats, it is common practice for organizations to maintain a series of custom regular expression (regex) patterns to run on NMS data. However, the growth of network...
Knights Landing (KNL) is the code name for the second-generation Intel Xeon Phi product family. KNL has generated significant interest in the data analysis and machine learning communities because its new many-core architecture targets both of these workloads. The KNL many-core vector processor design enables it to exploit much higher levels of parallelism. At the Lincoln Laboratory Supercomputing...
Excessive memory usage in software applications has become a frequent issue. A high degree of parallelism and the monitoring difficulty for the developer can quickly lead to memory shortage, or can increase the duration of garbage collection cycles. There are several solutions introduced to monitor memory usage in software. However they are neither efficient nor scalable. In this paper, we propose...
This paper shows that Julia provides sufficient performance to bridge the performance gap between productivity-oriented languages and low-level languages for complex memory intensive computation tasks such as graph traversal. We provide performance guidelines for using complex low-level data structures in high productivity languages and present the first parallel integration on the productivity-oriented...
We discuss our submission to the HPEC 2017 Static Graph Challenge on k-truss decomposition and triangle counting. Our results use an algorithm called PKT (Parallel k-truss) designed for multicore systems. We are able to process almost all Graph Challenge datasets in under a minute on a 24-core server with 128 GB memory. For a synthetic Graph500 graph with 17 million vertices and 523 million edges,...
Triangle counting has long been a challenge problem for sparse graphs containing high-degree "hub" vertices that exist in many real-world scenarios. These high-degree vertices create a quadratic number of wedges, or 2-edge paths, which for brute force algorithms require closure checking or wedge checks. Our work-in-progress builds on existing heuristics for pruning the number of wedge checks...
Large-scale inverse problems and uncertainty quantification (UQ), i.e., quantifying uncertainties in complex mathematical models and their large-scale computational implementations, is one of the outstanding challenges in computational science and will be a driver for the acquisition of future supercomputers. These methods generate significant amounts of simulation data that is used by other parts...
An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive benchmarks and challenges have proven...
The Maximal Independent Set (MIS) graph problem arises in many applications such as computer vision, information theory, molecular biology, and process scheduling. The growing scale of MIS problems suggests the use of distributed-memory hardware as a cost-effective approach to providing necessary compute and memory resources. Luby proposed four randomized algorithms to solve the MIS problem. All those...
The k-truss of a graph is a subgraph such that each edge is tightly connected to the remaining elements in the k-truss. The k-truss of a graph can also represent an important community in the graph. Finding the k-truss of a graph can be done in a polynomial amount of time, in contrast finding other subgraphs such as cliques. While there are numerous formulations and algorithms for finding the maximal...
This paper presents the details of a CUDA implementation of the Subgraph Isomorphism Graph Challenge, a new effort aimed at driving progress in the graph analytics field. challenge consists of two graph analytics: triangle counting and k-truss. We present our CUDA implementation of the graph triangle counting operation and of the k-truss subgraph decomposition. Both implementations share the same...
We describe CPU and GPU implementations of parallel triangle-counting and k-truss identification in the Galois and IrGL systems. Both systems are based on a graph-centric abstraction called the operator formulation of algorithms. Depending on the input graph, our implementations are two to three orders of magnitude faster than the reference implementations provided by the IEEE HPEC static graph challenge.
Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) is demonstrated to efficiently solve eigenvalue problems for graph Laplacians that appear in spectral clustering. For static graph partitioning, 10–20 iterations of LOBPCG without preconditioning result in ∼10× error reduction, enough to achieve 100% correctness for all Challenge datasets with known truth partitions, e.g., for graphs...
Triangle counting is a key algorithm for large graph analysis. The Graphulo library provides a framework for implementing graph algorithms on the Apache Accumulo distributed database. In this work we adapt two algorithms for counting triangles, one that uses the adjacency matrix and another that also uses the incidence matrix, to the Graphulo library for serverside processing inside Accumulo. Cloud-based...
A polystore system is a database management system composed of integrated heterogeneous database engines and multiple programming languages. By matching data to the storage engine best suited to its needs, complex analytics run faster and flexible storage choices helps improve data organization. BigDAWG (Big Data Working Group) is our prototype implementation of a polystore system. In this paper,...
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