1,156 research outputs found
A Streaming Multi-GPU Implementation of Image Simulation Algorithms for Scanning Transmission Electron Microscopy
Simulation of atomic resolution image formation in scanning transmission
electron microscopy can require significant computation times using traditional
methods. A recently developed method, termed plane-wave reciprocal-space
interpolated scattering matrix (PRISM), demonstrates potential for significant
acceleration of such simulations with negligible loss of accuracy. Here we
present a software package called Prismatic for parallelized simulation of
image formation in scanning transmission electron microscopy (STEM) using both
the PRISM and multislice methods. By distributing the workload between multiple
CUDA-enabled GPUs and multicore processors, accelerations as high as 1000x for
PRISM and 30x for multislice are achieved relative to traditional multislice
implementations using a single 4-GPU machine. We demonstrate a potentially
important application of Prismatic, using it to compute images for atomic
electron tomography at sufficient speeds to include in the reconstruction
pipeline. Prismatic is freely available both as an open-source CUDA/C++ package
with a graphical user interface and as a Python package, PyPrismatic
A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics
Mesoscopic simulations of hydrocarbon flow in source shales are challenging,
in part due to the heterogeneous shale pores with sizes ranging from a few
nanometers to a few micrometers. Additionally, the sub-continuum fluid-fluid
and fluid-solid interactions in nano- to micro-scale shale pores, which are
physically and chemically sophisticated, must be captured. To address those
challenges, we present a GPU-accelerated package for simulation of flow in
nano- to micro-pore networks with a many-body dissipative particle dynamics
(mDPD) mesoscale model. Based on a fully distributed parallel paradigm, the
code offloads all intensive workloads on GPUs. Other advancements, such as
smart particle packing and no-slip boundary condition in complex pore
geometries, are also implemented for the construction and the simulation of the
realistic shale pores from 3D nanometer-resolution stack images. Our code is
validated for accuracy and compared against the CPU counterpart for speedup. In
our benchmark tests, the code delivers nearly perfect strong scaling and weak
scaling (with up to 512 million particles) on up to 512 K20X GPUs on Oak Ridge
National Laboratory's (ORNL) Titan supercomputer. Moreover, a single-GPU
benchmark on ORNL's SummitDev and IBM's AC922 suggests that the host-to-device
NVLink can boost performance over PCIe by a remarkable 40\%. Lastly, we
demonstrate, through a flow simulation in realistic shale pores, that the CPU
counterpart requires 840 Power9 cores to rival the performance delivered by our
package with four V100 GPUs on ORNL's Summit architecture. This simulation
package enables quick-turnaround and high-throughput mesoscopic numerical
simulations for investigating complex flow phenomena in nano- to micro-porous
rocks with realistic pore geometries
Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures
Efficient parallel implementations of scientific applications on multi-core CPUs with accelerators such as GPUs and Xeon Phis is challenging. This requires - exploiting the data parallel architecture of the accelerator along with the vector pipelines of modern x86 CPU architectures, load balancing, and efficient memory transfer between different devices. It is relatively easy to meet these requirements for highly-structured scientific applications. In contrast, a number of scientific and engineering applications are unstructured. Getting performance on accelerators for these applications is extremely challenging because many of these applications employ irregular algorithms which exhibit data-dependent control-flow and irregular memory accesses. Furthermore, these applications are often iterative with dependency between steps, and thus making it hard to parallelize across steps. As a result, parallelism in these applications is often limited to a single step. Numerical simulation of charged particles beam dynamics is one such application where the distribution of work and memory access pattern at each time step is irregular. Applications with these properties tend to present significant branch and memory divergence, load imbalance between different processor cores, and poor compute and memory utilization. Prior research on parallelizing such irregular applications have been focused around optimizing the irregular, data-dependent memory accesses and control-flow during a single step of the application independent of the other steps, with the assumption that these patterns are completely unpredictable. We observed that the structure of computation leading to control-flow divergence and irregular memory accesses in one step is similar to that in the next step. It is possible to predict this structure in the current step by observing the computation structure of previous steps.
In this dissertation, we present novel machine learning based optimization techniques to address the parallel implementation challenges of such irregular applications on different HPC architectures. In particular, we use supervised learning to predict the computation structure and use it to address the control-flow and memory access irregularities in the parallel implementation of such applications on GPUs, Xeon Phis, and heterogeneous architectures composed of multi-core CPUs with GPUs or Xeon Phis. We use numerical simulation of charged particles beam dynamics simulation as a motivating example throughout the dissertation to present our new approach, though they should be equally applicable to a wide range of irregular applications. The machine learning approach presented here use predictive analytics and forecasting techniques to adaptively model and track the irregular memory access pattern at each time step of the simulation to anticipate the future memory access pattern. Access pattern forecasts can then be used to formulate optimization decisions during application execution which improves the performance of the application at a future time step based on the observations from earlier time steps. In heterogeneous architectures, forecasts can also be used to improve the memory performance and resource utilization of all the processing units to deliver a good aggregate performance. We used these optimization techniques and anticipation strategy to design a cache-aware, memory efficient parallel algorithm to address the irregularities in the parallel implementation of charged particles beam dynamics simulation on different HPC architectures. Experimental result using a diverse mix of HPC architectures shows that our approach in using anticipation strategy is effective in maximizing data reuse, ensuring workload balance, minimizing branch and memory divergence, and in improving resource utilization
ASCR/HEP Exascale Requirements Review Report
This draft report summarizes and details the findings, results, and
recommendations derived from the ASCR/HEP Exascale Requirements Review meeting
held in June, 2015. The main conclusions are as follows. 1) Larger, more
capable computing and data facilities are needed to support HEP science goals
in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of
the demand at the 2025 timescale is at least two orders of magnitude -- and in
some cases greater -- than that available currently. 2) The growth rate of data
produced by simulations is overwhelming the current ability, of both facilities
and researchers, to store and analyze it. Additional resources and new
techniques for data analysis are urgently needed. 3) Data rates and volumes
from HEP experimental facilities are also straining the ability to store and
analyze large and complex data volumes. Appropriately configured
leadership-class facilities can play a transformational role in enabling
scientific discovery from these datasets. 4) A close integration of HPC
simulation and data analysis will aid greatly in interpreting results from HEP
experiments. Such an integration will minimize data movement and facilitate
interdependent workflows. 5) Long-range planning between HEP and ASCR will be
required to meet HEP's research needs. To best use ASCR HPC resources the
experimental HEP program needs a) an established long-term plan for access to
ASCR computational and data resources, b) an ability to map workflows onto HPC
resources, c) the ability for ASCR facilities to accommodate workflows run by
collaborations that can have thousands of individual members, d) to transition
codes to the next-generation HPC platforms that will be available at ASCR
facilities, e) to build up and train a workforce capable of developing and
using simulations and analysis to support HEP scientific research on
next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
Multi-GPU Accelerated High-Fidelity Simulations of Beam-Beam Effects in Particle Colliders
Numerical simulation of beam-beam effects in particle colliders are crucial in understanding and the design of future machines such as electron-ion colliders (JLEIC), linac-ring machines (eRHIC) or LHeC. These simulations model the non-linear collision dynamics of two counter rotating beams in particle colliders for millions of turns. In particular, at each turn, the algorithm simulates the collision of two directed beams propagating at different speeds with different number of bunches each. This leads to non-pair-wise collisions of beams with different number of bunches that results in an increase in the computational load proportional to the number of bunches in the beams. Simulating these collisions for millions of turns using traditional CPUs is challenging due to the complexity in modeling non-linear dynamics of the beams and the need to simulate collision of every bunch in a reasonable amount of time.
In this Thesis, we present a high-performance scalable implementation to simulate the beam-beam effects in electron-ion colliders using a cluster of NVIDIA GPUs. The parallel implementation is optimized to minimize the communication overhead and the performance scales near linearly with number of GPUs. Further, the new code enables tracking and collision of the beams for millions of turns, thereby making the previously inaccessible long-term simulations tractable. As of now, there is no other code in existence that can accurately model the single particle non-linear dynamics and the beam-beam effects at the same time for a large enough number of turns required to verify the long-term stability of a collider
Accurate and efficient spin integration for particle accelerators
Accurate spin tracking is a valuable tool for understanding spin dynamics in
particle accelerators and can help improve the performance of an accelerator.
In this paper, we present a detailed discussion of the integrators in the spin
tracking code gpuSpinTrack. We have implemented orbital integrators based on
drift-kick, bend-kick, and matrix-kick splits. On top of the orbital
integrators, we have implemented various integrators for the spin motion. These
integrators use quaternions and Romberg quadratures to accelerate both the
computation and the convergence of spin rotations. We evaluate their
performance and accuracy in quantitative detail for individual elements as well
as for the entire RHIC lattice. We exploit the inherently data-parallel nature
of spin tracking to accelerate our algorithms on graphics processing units.Comment: 43 pages, 17 figure
CompF2: Theoretical Calculations and Simulation Topical Group Report
This report summarizes the work of the Computational Frontier topical group
on theoretical calculations and simulation for Snowmass 2021. We discuss the
challenges, potential solutions, and needs facing six diverse but related
topical areas that span the subject of theoretical calculations and simulation
in high energy physics (HEP): cosmic calculations, particle accelerator
modeling, detector simulation, event generators, perturbative calculations, and
lattice QCD (quantum chromodynamics). The challenges arise from the next
generations of HEP experiments, which will include more complex instruments,
provide larger data volumes, and perform more precise measurements.
Calculations and simulations will need to keep up with these increased
requirements. The other aspect of the challenge is the evolution of computing
landscape away from general-purpose computing on CPUs and toward
special-purpose accelerators and coprocessors such as GPUs and FPGAs. These
newer devices can provide substantial improvements for certain categories of
algorithms, at the expense of more specialized programming and memory and data
access patterns.Comment: Report of the Computational Frontier Topical Group on Theoretical
Calculations and Simulation for Snowmass 202
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