2,318 research outputs found
BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images
In cryo-electron microscopy (EM), molecular structures are determined from
large numbers of projection images of individual particles. To harness the full
power of this single-molecule information, we use the Bayesian inference of EM
(BioEM) formalism. By ranking structural models using posterior probabilities
calculated for individual images, BioEM in principle addresses the challenge of
working with highly dynamic or heterogeneous systems not easily handled in
traditional EM reconstruction. However, the calculation of these posteriors for
large numbers of particles and models is computationally demanding. Here we
present highly parallelized, GPU-accelerated computer software that performs
this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI
parallelization combined with both CPU and GPU computing. The resulting BioEM
software scales nearly ideally both on pure CPU and on CPU+GPU architectures,
thus enabling Bayesian analysis of tens of thousands of images in a reasonable
time. The general mathematical framework and robust algorithms are not limited
to cryo-electron microscopy but can be generalized for electron tomography and
other imaging experiments
Using machine learning techniques to evaluate multicore soft error reliability
Virtual platform frameworks have been extended
to allow earlier soft error analysis of more realistic multicore
systems (i.e., real software stacks, state-of-the-art ISAs). The
high observability and simulation performance of underlying
frameworks enable to generate and collect more error/failurerelated data, considering complex software stack configurations,
in a reasonable time. When dealing with sizeable failure-related
data sets obtained from multiple fault campaigns, it is essential to
filter out parameters (i.e., features) without a direct relationship
with the system soft error analysis. In this regard, this paper proposes the use of supervised and unsupervised machine learning
techniques, aiming to eliminate non-relevant information as well
as identify the correlation between fault injection results and
application and platform characteristics. This novel approach
provides engineers with appropriate means that able are able to
investigate new and more efficient fault mitigation techniques.
The underlying approach is validated with an extensive data set
gathered from more than 1.2 million fault injections, comprising
several benchmarks, a Linux OS and parallelization libraries
(e.g., MPI, OpenMP), as well as through a realistic automotive
case study
Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package
Raftery, Karny, and Ettler (2010) introduce an estimation technique, which
they refer to as Dynamic Model Averaging (DMA). In their application, DMA is
used to predict the output strip thickness for a cold rolling mill, where the
output is measured with a time delay. Recently, DMA has also shown to be useful
in macroeconomic and financial applications. In this paper, we present the eDMA
package for DMA estimation implemented in R. The eDMA package is especially
suited for practitioners in economics and finance, where typically a large
number of predictors are available. Our implementation is up to 133 times
faster then a standard implementation using a single-core CPU. Thus, with the
help of this package, practitioners are able to perform DMA on a standard PC
without resorting to large clusters, which are not easily available to all
researchers. We demonstrate the usefulness of this package through simulation
experiments and an empirical application using quarterly U.S. inflation data.Comment: 21 pages, 5 figures, 2 table
Afivo: a framework for quadtree/octree AMR with shared-memory parallelization and geometric multigrid methods
Afivo is a framework for simulations with adaptive mesh refinement (AMR) on
quadtree (2D) and octree (3D) grids. The framework comes with a geometric
multigrid solver, shared-memory (OpenMP) parallelism and it supports output in
Silo and VTK file formats. Afivo can be used to efficiently simulate AMR
problems with up to about unknowns on desktops, workstations or single
compute nodes. For larger problems, existing distributed-memory frameworks are
better suited. The framework has no built-in functionality for specific physics
applications, so users have to implement their own numerical methods. The
included multigrid solver can be used to efficiently solve elliptic partial
differential equations such as Poisson's equation. Afivo's design was kept
simple, which in combination with the shared-memory parallelism facilitates
modification and experimentation with AMR algorithms. The framework was already
used to perform 3D simulations of streamer discharges, which required tens of
millions of cells
A Functional Safety OpenMP∗ for Critical Real-Time Embedded Systems
OpenMP* has recently gained attention in the embedded domain by virtue of the augmentations implemented in the last specification. Yet, the language has a minimal impact in the embedded real-time domain mostly due to the lack of reliability and resiliency mechanisms. As a result, functional safety properties cannot be guaranteed. This paper analyses in detail the latest specification to determine whether and how the compliant OpenMP implementations can guarantee functional safety. Given the conclusions drawn from the analysis, the paper describes a set of modifications to the specification, and a set of requirements for compiler and runtime systems to qualify for safety critical environments. Through the proposed solution, OpenMP can be used in critical real-time embedded systems without compromising functional safety.This work was funded by the EU project P-SOCRATES (FP7-ICT-2013- 10)
and the Spanish Ministry of Science and Innovation under contract TIN2015-
65316-P.Peer ReviewedPostprint (author's final draft
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