466 research outputs found
Massively parallel approximate Gaussian process regression
We explore how the big-three computing paradigms -- symmetric multi-processor
(SMC), graphical processing units (GPUs), and cluster computing -- can together
be brought to bare on large-data Gaussian processes (GP) regression problems
via a careful implementation of a newly developed local approximation scheme.
Our methodological contribution focuses primarily on GPU computation, as this
requires the most care and also provides the largest performance boost.
However, in our empirical work we study the relative merits of all three
paradigms to determine how best to combine them. The paper concludes with two
case studies. One is a real data fluid-dynamics computer experiment which
benefits from the local nature of our approximation; the second is a synthetic
data example designed to find the largest design for which (accurate) GP
emulation can performed on a commensurate predictive set under an hour.Comment: 24 pages, 6 figures, 1 tabl
Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS
GROMACS is a widely used package for biomolecular simulation, and over the
last two decades it has evolved from small-scale efficiency to advanced
heterogeneous acceleration and multi-level parallelism targeting some of the
largest supercomputers in the world. Here, we describe some of the ways we have
been able to realize this through the use of parallelization on all levels,
combined with a constant focus on absolute performance. Release 4.6 of GROMACS
uses SIMD acceleration on a wide range of architectures, GPU offloading
acceleration, and both OpenMP and MPI parallelism within and between nodes,
respectively. The recent work on acceleration made it necessary to revisit the
fundamental algorithms of molecular simulation, including the concept of
neighborsearching, and we discuss the present and future challenges we see for
exascale simulation - in particular a very fine-grained task parallelism. We
also discuss the software management, code peer review and continuous
integration testing required for a project of this complexity.Comment: EASC 2014 conference proceedin
GLB: Lifeline-based Global Load Balancing library in X10
We present GLB, a programming model and an associated implementation that can
handle a wide range of irregular paral- lel programming problems running over
large-scale distributed systems. GLB is applicable both to problems that are
easily load-balanced via static scheduling and to problems that are hard to
statically load balance. GLB hides the intricate syn- chronizations (e.g.,
inter-node communication, initialization and startup, load balancing,
termination and result collection) from the users. GLB internally uses a
version of the lifeline graph based work-stealing algorithm proposed by
Saraswat et al. Users of GLB are simply required to write several pieces of
sequential code that comply with the GLB interface. GLB then schedules and
orchestrates the parallel execution of the code correctly and efficiently at
scale. We have applied GLB to two representative benchmarks: Betweenness
Centrality (BC) and Unbalanced Tree Search (UTS). Among them, BC can be
statically load-balanced whereas UTS cannot. In either case, GLB scales well--
achieving nearly linear speedup on different computer architectures (Power,
Blue Gene/Q, and K) -- up to 16K cores
Developing Efficient Discrete Simulations on Multicore and GPU Architectures
In this paper we show how to efficiently implement parallel discrete simulations on multicoreandGPUarchitecturesthrougharealexampleofanapplication: acellularautomatamodel of laser dynamics. We describe the techniques employed to build and optimize the implementations using OpenMP and CUDA frameworks. We have evaluated the performance on two different hardware platforms that represent different target market segments: high-end platforms for scientific computing, using an Intel Xeon Platinum 8259CL server with 48 cores, and also an NVIDIA Tesla V100GPU,bothrunningonAmazonWebServer(AWS)Cloud;and on a consumer-oriented platform, using an Intel Core i9 9900k CPU and an NVIDIA GeForce GTX 1050 TI GPU. Performance results were compared and analyzed in detail. We show that excellent performance and scalability can be obtained in both platforms, and we extract some important issues that imply a performance degradation for them. We also found that current multicore CPUs with large core numbers can bring a performance very near to that of GPUs, and even identical in some cases.Ministerio de Economía, Industria y Competitividad, Gobierno de España (MINECO), and the Agencia Estatal de Investigación (AEI) of Spain, cofinanced by FEDER funds (EU) TIN2017-89842
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