1,888 research outputs found
A Multi-GPU Programming Library for Real-Time Applications
We present MGPU, a C++ programming library targeted at single-node multi-GPU
systems. Such systems combine disproportionate floating point performance with
high data locality and are thus well suited to implement real-time algorithms.
We describe the library design, programming interface and implementation
details in light of this specific problem domain. The core concepts of this
work are a novel kind of container abstraction and MPI-like communication
methods for intra-system communication. We further demonstrate how MGPU is used
as a framework for porting existing GPU libraries to multi-device
architectures. Putting our library to the test, we accelerate an iterative
non-linear image reconstruction algorithm for real-time magnetic resonance
imaging using multiple GPUs. We achieve a speed-up of about 1.7 using 2 GPUs
and reach a final speed-up of 2.1 with 4 GPUs. These promising results lead us
to conclude that multi-GPU systems are a viable solution for real-time MRI
reconstruction as well as signal-processing applications in general.Comment: 15 pages, 10 figure
Accelerated Modeling of Near and Far-Field Diffraction for Coronagraphic Optical Systems
Accurately predicting the performance of coronagraphs and tolerancing optical
surfaces for high-contrast imaging requires a detailed accounting of
diffraction effects. Unlike simple Fraunhofer diffraction modeling, near and
far-field diffraction effects, such as the Talbot effect, are captured by
plane-to-plane propagation using Fresnel and angular spectrum propagation. This
approach requires a sequence of computationally intensive Fourier transforms
and quadratic phase functions, which limit the design and aberration
sensitivity parameter space which can be explored at high-fidelity in the
course of coronagraph design. This study presents the results of optimizing the
multi-surface propagation module of the open source Physical Optics Propagation
in PYthon (POPPY) package. This optimization was performed by implementing and
benchmarking Fourier transforms and array operations on graphics processing
units, as well as optimizing multithreaded numerical calculations using the
NumExpr python library where appropriate, to speed the end-to-end simulation of
observatory and coronagraph optical systems. Using realistic systems, this
study demonstrates a greater than five-fold decrease in wall-clock runtime over
POPPY's previous implementation and describes opportunities for further
improvements in diffraction modeling performance.Comment: Presented at SPIE ASTI 2018, Austin Texas. 11 pages, 6 figure
Application of graphics processing units to search pipelines for gravitational waves from coalescing binaries of compact objects
We report a novel application of a graphics processing unit (GPU) for the purpose of accelerating the search pipelines for gravitational waves from coalescing binaries of compact objects. A speed-up of 16-fold in total has been achieved with an NVIDIA GeForce 8800 Ultra GPU card compared with one core of a 2.5 GHz Intel Q9300 central processing unit (CPU). We show that substantial improvements are possible and discuss the reduction in CPU count required for the detection of inspiral sources afforded by the use of GPUs
TensorFlow Doing HPC
TensorFlow is a popular emerging open-source programming framework supporting
the execution of distributed applications on heterogeneous hardware. While
TensorFlow has been initially designed for developing Machine Learning (ML)
applications, in fact TensorFlow aims at supporting the development of a much
broader range of application kinds that are outside the ML domain and can
possibly include HPC applications. However, very few experiments have been
conducted to evaluate TensorFlow performance when running HPC workloads on
supercomputers. This work addresses this lack by designing four traditional HPC
benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG)
solver and Fast Fourier Transform (FFT). We analyze their performance on two
supercomputers with accelerators and evaluate the potential of TensorFlow for
developing HPC applications. Our tests show that TensorFlow can fully take
advantage of high performance networks and accelerators on supercomputers.
Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical
communication bandwidth on our testing platform. We find an approximately 2x,
1.7x and 1.8x performance improvement when increasing the number of GPUs from
two to four in the matrix-matrix multiply, CG and FFT applications
respectively. All our performance results demonstrate that TensorFlow has high
potential of emerging also as HPC programming framework for heterogeneous
supercomputers.Comment: Accepted for publication at The Ninth International Workshop on
Accelerators and Hybrid Exascale Systems (AsHES'19
Landau Gauge Fixing on GPUs
In this paper we present and explore the performance of Landau gauge fixing
in GPUs using CUDA. We consider the steepest descent algorithm with Fourier
acceleration, and compare the GPU performance with a parallel CPU
implementation. Using lattice volumes, we find that the computational
power of a single Tesla C2070 GPU is equivalent to approximately 256 CPU cores.Comment: 10 pages, 3 figures and 3 table
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