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Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures

SIAM Journal on Scientific Computing

Phipps, Eric T.; D'Elia, Marta D.; Edwards, Harold C.; Hoemmen, M.; Hu, J.; Rajamanickam, Sivasankaran R.

Quantifying simulation uncertainties is a critical component of rigorous predictive simulation. A key component of this is forward propagation of uncertainties in simulation input data to output quantities of interest. Typical approaches involve repeated sampling of the simulation over the uncertain input data and can require numerous samples when accurately propagating uncertainties from large numbers of sources. Often simulation processes from sample to sample are similar, and much of the data generated from each sample evaluation could be reused. We explore a new method for implementing sampling methods that simultaneously propagates groups of samples together in an embedded fashion, which we call embedded ensemble propagation. We show how this approach takes advantage of properties of modern computer architectures to improve performance by enabling reuse between samples, reducing memory bandwidth requirements, improving memory access patterns, improving opportunities for fine-grained parallelization, and reducing communication costs. We describe a software technique for implementing embedded ensemble propagation based on the use of C++ templates and describe its integration with various scientific computing libraries within Trilinos. We demonstrate improved performance, portability, and scalability for the approach applied to the simulation of partial differential equations on a variety of multicore and manycore architectures, including up to 16,384 cores on a Cray XK7 (Titan).

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TYPE Journal Article YEAR 2017
ScopusOSTIDOI

Kokkos/Qthreads task-parallel approach to linear algebra based graph analytics

2016 IEEE High Performance Extreme Computing Conference, HPEC 2016

Wolf, Michael W.; Edwards, Harold C.; Olivier, Stephen L.

The Graph BLAS effort to standardize a set of graph algorithms building blocks in terms of linear algebra primitives promises to deliver high performing graph algorithms and greatly impact the analysis of big data. However, there are challenges with this approach, which our data analytics miniapp miniTri exposes. In this paper, we improve upon a previously proposed task-parallel approach to linear algebra-based miniTri formulation, addressing these challenges and describing a Kokkos/Qthreads task-parallel implementation that performs as well or slightly better than the highly optimized, baseline OpenMP data-parallel implementation.

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TYPE Conference Poster YEAR 2016
ScopusOSTIDOI

Hierarchical Task-Data Parallelism using Kokkos and Qthreads

Edwards, Harold C.; Olivier, Stephen L.; Berry, Jonathan W.; Mackey, Greg; Rajamanickam, Sivasankaran R.; Wolf, Michael W.; Kim, Kyungjoo K.; Stelle, George

This report describes a new capability for hierarchical task-data parallelism using Sandia's Kokkos and Qthreads, and evaluation of this capability with sparse matrix Cholesky factor- ization and social network triangle enumeration mini-applications. Hierarchical task-data parallelism consists of a collection of tasks with executes-after dependences where each task contains data parallel operations performed on a team of hardware threads. The collection of tasks and dependences form a directed acyclic graph of tasks - a task DAG . Major chal- lenges of this research and development effort include: portability and performance across multicore CPU; manycore Intel Xeon Phi, and NVIDIA GPU architectures; scalability with respect to hardware concurrency and size of the task DAG; and usability of the application programmer interface (API).

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TYPE SAND Report YEAR 2016
OSTIDOI

Task Parallel Incomplete Cholesky Factorization using 2D Partitioned-Block Layout

Kim, Kyungjoo K.; Rajamanickam, Sivasankaran R.; Stelle, George; Edwards, Harold C.; Olivier, Stephen L.

We introduce a task-parallel algorithm for sparse incomplete Cholesky factorization that utilizes a 2D sparse partitioned-block layout of a matrix. Our factorization algorithm follows the idea of algorithms-by-blocks by using the block layout. The algorithm-byblocks approach induces a task graph for the factorization. These tasks are inter-related to each other through their data dependences in the factorization algorithm. To process the tasks on various manycore architectures in a portable manner, we also present a portable tasking API that incorporates different tasking backends and device-specific features using an open-source framework for manycore platforms i.e., Kokkos. A performance evaluation is presented on both Intel Sandybridge and Xeon Phi platforms for matrices from the University of Florida sparse matrix collection to illustrate merits of the proposed task-based factorization. Experimental results demonstrate that our task-parallel implementation delivers about 26.6x speedup (geometric mean) over single-threaded incomplete Choleskyby- blocks and 19.2x speedup over serial Cholesky performance which does not carry tasking overhead using 56 threads on the Intel Xeon Phi processor for sparse matrices arising from various application problems.

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TYPE Other Report YEAR 2016
OSTIDOI
Results 26–50 of 142
Results 26–50 of 142