5,674 research outputs found
Performance and Optimization Abstractions for Large Scale Heterogeneous Systems in the Cactus/Chemora Framework
We describe a set of lower-level abstractions to improve performance on
modern large scale heterogeneous systems. These provide portable access to
system- and hardware-dependent features, automatically apply dynamic
optimizations at run time, and target stencil-based codes used in finite
differencing, finite volume, or block-structured adaptive mesh refinement
codes.
These abstractions include a novel data structure to manage refinement
information for block-structured adaptive mesh refinement, an iterator
mechanism to efficiently traverse multi-dimensional arrays in stencil-based
codes, and a portable API and implementation for explicit SIMD vectorization.
These abstractions can either be employed manually, or be targeted by
automated code generation, or be used via support libraries by compilers during
code generation. The implementations described below are available in the
Cactus framework, and are used e.g. in the Einstein Toolkit for relativistic
astrophysics simulations
CoreTSAR: Task Scheduling for Accelerator-aware Runtimes
Heterogeneous supercomputers that incorporate computational accelerators
such as GPUs are increasingly popular due to their high
peak performance, energy efficiency and comparatively low cost.
Unfortunately, the programming models and frameworks designed
to extract performance from all computational units still lack the
flexibility of their CPU-only counterparts. Accelerated OpenMP
improves this situation by supporting natural migration of OpenMP
code from CPUs to a GPU. However, these implementations currently
lose one of OpenMP’s best features, its flexibility: typical
OpenMP applications can run on any number of CPUs. GPU implementations
do not transparently employ multiple GPUs on a node
or a mix of GPUs and CPUs. To address these shortcomings, we
present CoreTSAR, our runtime library for dynamically scheduling
tasks across heterogeneous resources, and propose straightforward
extensions that incorporate this functionality into Accelerated
OpenMP. We show that our approach can provide nearly linear
speedup to four GPUs over only using CPUs or one GPU while
increasing the overall flexibility of Accelerated OpenMP
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
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