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A task-based linear algebra Building Blocks approach for scalable graph analytics

2015 IEEE High Performance Extreme Computing Conference, HPEC 2015

Wolf, Michael W.; Berry, Jonathan W.; Stark, Dylan S.

It is challenging to obtain scalable HPC performance on real applications, especially for data science applications with irregular memory access and computation patterns. To drive co-design efforts in architecture, system, and application design, we are developing miniapps representative of data science workloads. These in turn stress the state of the art in Graph BLAS-like Graph Algorithm Building Blocks (GABB). In this work, we outline a Graph BLAS-like, linear algebra based approach to miniTri, one such miniapp. We describe a task-based prototype implementation and give initial scalability results.

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TYPE Conference Poster YEAR 2015
ScopusOSTIDOI

Advantages to modeling relational data using hypergraphs versus graphs

2016 IEEE High Performance Extreme Computing Conference, HPEC 2016

Wolf, Michael W.; Klinvex, Alicia M.; Dunlavy, Daniel D.

Driven by the importance of relational aspects of data to decision-making, graph algorithms have been developed, based on simplified pairwise relationships, to solve a variety of problems. However, evidence has shown that hypergraphs - generalizations of graphs with (hyper)edges that connect any number of vertices - can better model complex, non-pairwise relationships in data and lead to better informed decisions. In this work, we compare graph and hypergraph models in the context of spectral clustering. For these problems, we demonstrate that hypergraphs are computationally more efficient and can better model complex, non-pairwise relationships for many datasets.

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TYPE Conference Poster YEAR 2016
ScopusOSTIDOI

ECP Report: Update on Proxy Applications and Vendor Interactions

Ang, Jim A.; Sweeney, Christine S.; Wolf, Michael W.; Ellis, John E.; Ghosh, Sayan G.; Kagawa, Ai K.; Huang, Yunzhi H.; Rajamanickam, Sivasankaran R.; Ramakrishnaiah, Vinay R.; Schram, Malachi S.; Yoo, Shinjae Y.

The ExaLearn miniGAN team (Ellis and Rajamanickam) have released miniGAN, a generative adversarial network(GAN) proxy application, through the ECP proxy application suite. miniGAN is the first machine learning proxy application in the suite (note: the ECP CANDLE project did previously release some benchmarks) and models the performance for training generator and discriminator networks. The GAN's generator and discriminator generate plausible 2D/3D maps and identify fake maps, respectively. miniGAN aims to be a proxy application for related applications in cosmology (CosmoFlow, ExaGAN) and wind energy (ExaWind). miniGAN has been developed so that optimized mathematical kernels (e.g., kernels provided by Kokkos Kernels) can be plugged into to the proxy application to explore potential performance improvements. miniGAN has been released as open source software and is available through the ECP proxy application website (https://proxyapps.exascaleproject.ordecp-proxy-appssuite/) and on GitHub (https://github.com/SandiaMLMiniApps/miniGAN). As part of this release, a generator is provided to generate a data set (series of images) that are inputs to the proxy application.

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TYPE Other Report YEAR 2020
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Factors impacting performance of multithreaded triangular solve

Wolf, Michael W.; Heroux, Michael A.; Boman, Erik G.

As computational science applications grow more parallel with multi-core supercomputers having hundreds of thousands of computational cores, it will become increasingly difficult for solvers to scale. Our approach is to use hybrid MPI/threaded numerical algorithms to solve these systems in order to reduce the number of MPI tasks and increase the parallel efficiency of the algorithm. However, we need efficient threaded numerical kernels to run on the multi-core nodes in order to achieve good parallel efficiency. In this paper, we focus on improving the performance of a multithreaded triangular solver, an important kernel for preconditioning. We analyze three factors that affect the parallel performance of this threaded kernel and obtain good scalability on the multi-core nodes for a range of matrix sizes.

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TYPE Conference YEAR 2010
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Fast linear algebra-based triangle counting with KokkosKernels

2017 IEEE High Performance Extreme Computing Conference, HPEC 2017

Wolf, Michael W.; Deveci, Mehmet D.; Berry, Jonathan W.; Hammond, Simon D.; Rajamanickam, Sivasankaran R.

Triangle counting serves as a key building block for a set of important graph algorithms in network science. In this paper, we address the IEEE HPEC Static Graph Challenge problem of triangle counting, focusing on obtaining the best parallel performance on a single multicore node. Our implementation uses a linear algebra-based approach to triangle counting that has grown out of work related to our miniTri data analytics miniapplication [1] and our efforts to pose graph algorithms in the language of linear algebra. We leverage KokkosKernels to implement this approach efficiently on multicore architectures. Our performance results are competitive with the fastest known graph traversal-based approaches and are significantly faster than the Graph Challenge reference implementations, up to 670,000 times faster than the C++ reference and 10,000 times faster than the Python reference on a single Intel Haswell node.

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TYPE Conference Poster YEAR 2017
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Results 1–25 of 66
Results 1–25 of 66