7,956 research outputs found
From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation
Starting from a high-level problem description in terms of partial
differential equations using abstract tensor notation, the Chemora framework
discretizes, optimizes, and generates complete high performance codes for a
wide range of compute architectures. Chemora extends the capabilities of
Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient
manner for complex applications, without low-level code tuning. Chemora
achieves parallelism through MPI and multi-threading, combining OpenMP and
CUDA. Optimizations include high-level code transformations, efficient loop
traversal strategies, dynamically selected data and instruction cache usage
strategies, and JIT compilation of GPU code tailored to the problem
characteristics. The discretization is based on higher-order finite differences
on multi-block domains. Chemora's capabilities are demonstrated by simulations
of black hole collisions. This problem provides an acid test of the framework,
as the Einstein equations contain hundreds of variables and thousands of terms.Comment: 18 pages, 4 figures, accepted for publication in Scientific
Programmin
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
Air pollution modelling using a graphics processing unit with CUDA
The Graphics Processing Unit (GPU) is a powerful tool for parallel computing.
In the past years the performance and capabilities of GPUs have increased, and
the Compute Unified Device Architecture (CUDA) - a parallel computing
architecture - has been developed by NVIDIA to utilize this performance in
general purpose computations. Here we show for the first time a possible
application of GPU for environmental studies serving as a basement for decision
making strategies. A stochastic Lagrangian particle model has been developed on
CUDA to estimate the transport and the transformation of the radionuclides from
a single point source during an accidental release. Our results show that
parallel implementation achieves typical acceleration values in the order of
80-120 times compared to CPU using a single-threaded implementation on a 2.33
GHz desktop computer. Only very small differences have been found between the
results obtained from GPU and CPU simulations, which are comparable with the
effect of stochastic transport phenomena in atmosphere. The relatively high
speedup with no additional costs to maintain this parallel architecture could
result in a wide usage of GPU for diversified environmental applications in the
near future.Comment: 5 figure
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