2,219 research outputs found
Lessons learned in a decade of research software engineering gpu applications
After years of using Graphics Processing Units (GPUs) to accelerate scientific applications in fields as varied as tomography, computer vision, climate modeling, digital forensics, geospatial databases, particle physics, radio astronomy, and localization microscopy, we noticed a number of technical, socio-technical, and non-technical challenges that Research Software Engineers (RSEs) may run into. While some of these challenges, such as managing different programming languages within a project, or having to deal with different memory spaces, are common to all software projects involving GPUs, others are more typical of scientific software projects. Among these challenges we include changing resolutions or scales, maintaining an application over time and making it sustainable, and evaluating both the obtained results and the achieved performance
A Tuned and Scalable Fast Multipole Method as a Preeminent Algorithm for Exascale Systems
Among the algorithms that are likely to play a major role in future exascale
computing, the fast multipole method (FMM) appears as a rising star. Our
previous recent work showed scaling of an FMM on GPU clusters, with problem
sizes in the order of billions of unknowns. That work led to an extremely
parallel FMM, scaling to thousands of GPUs or tens of thousands of CPUs. This
paper reports on a a campaign of performance tuning and scalability studies
using multi-core CPUs, on the Kraken supercomputer. All kernels in the FMM were
parallelized using OpenMP, and a test using 10^7 particles randomly distributed
in a cube showed 78% efficiency on 8 threads. Tuning of the
particle-to-particle kernel using SIMD instructions resulted in 4x speed-up of
the overall algorithm on single-core tests with 10^3 - 10^7 particles. Parallel
scalability was studied in both strong and weak scaling. The strong scaling
test used 10^8 particles and resulted in 93% parallel efficiency on 2048
processes for the non-SIMD code and 54% for the SIMD-optimized code (which was
still 2x faster). The weak scaling test used 10^6 particles per process, and
resulted in 72% efficiency on 32,768 processes, with the largest calculation
taking about 40 seconds to evaluate more than 32 billion unknowns. This work
builds up evidence for our view that FMM is poised to play a leading role in
exascale computing, and we end the paper with a discussion of the features that
make it a particularly favorable algorithm for the emerging heterogeneous and
massively parallel architectural landscape
Software engineering to sustain a high-performance computing scientific application: QMCPACK
We provide an overview of the software engineering efforts and their impact
in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code
targeting high-performance computing (HPC) systems. Aspects included are: (i)
strategic expansion of continuous integration (CI) targeting CPUs, using GitHub
Actions runners, and NVIDIA and AMD GPUs in pre-exascale systems, using
self-hosted hardware; (ii) incremental reduction of memory leaks using
sanitizers, (iii) incorporation of Docker containers for CI and
reproducibility, and (iv) refactoring efforts to improve maintainability,
testing coverage, and memory lifetime management. We quantify the value of
these improvements by providing metrics to illustrate the shift towards a
predictive, rather than reactive, sustainable maintenance approach. Our goal,
in documenting the impact of these efforts on QMCPACK, is to contribute to the
body of knowledge on the importance of research software engineering (RSE) for
the sustainability of community HPC codes and scientific discovery at scale.Comment: Accepted at the first US-RSE Conference, USRSE2023,
https://us-rse.org/usrse23/, 8 pages, 3 figures, 4 table
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
Fast Calculation of the Lomb-Scargle Periodogram Using Graphics Processing Units
I introduce a new code for fast calculation of the Lomb-Scargle periodogram,
that leverages the computing power of graphics processing units (GPUs). After
establishing a background to the newly emergent field of GPU computing, I
discuss the code design and narrate key parts of its source. Benchmarking
calculations indicate no significant differences in accuracy compared to an
equivalent CPU-based code. However, the differences in performance are
pronounced; running on a low-end GPU, the code can match 8 CPU cores, and on a
high-end GPU it is faster by a factor approaching thirty. Applications of the
code include analysis of long photometric time series obtained by ongoing
satellite missions and upcoming ground-based monitoring facilities; and
Monte-Carlo simulation of periodogram statistical properties.Comment: Accepted by ApJ. Accompanying program source (updated since
acceptance) can be downloaded from
http://www.astro.wisc.edu/~townsend/resource/download/code/culsp.tar.g
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
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