The adoption of graphic processor units (GPU) in high-performance computing (HPC) infrastructures determines, in many cases, the energy consumption of those facilities. For this reason, an efficient ...management and administration of the GPU-enabled clusters is crucial for the optimum operation of the cluster. The main aim of this work is to study and design efficient mechanisms of job scheduling across GPU-enabled clusters by leveraging process malleability techniques, able to reconfigure running jobs, depending on the cluster status. This paper presents a model that improves the energy efficiency when processing a batch of jobs in an HPC cluster. The model is validated through the MPDATA algorithm, as a representative example of stencil computation used in numerical weather prediction. The proposed solution applies the efficiency metrics obtained in a new reconfiguration policy aimed at job arrays. This solution allows the reduction in the processing time of workloads up to 4.8 times and reduction in the energy consumption up to 2.4 times the cluster compared to the traditional job management, where jobs are not reconfigured during their execution.
A study on the performance of distributed training of data-driven CFD simulations Iserte, Sergio; González-Barberá, Alejandro; Barreda, Paloma ...
International journal of high performance computing applications/The international journal of high performance computing applications,
09/2023, Volume:
37, Issue:
5
Journal Article
Peer reviewed
Open access
Data-driven methods for computer simulations are blooming in many scientific areas. The traditional approach to simulating physical behaviors relies on solving partial differential equations (PDEs). ...Since calculating these iterative equations is highly both computationally demanding and time-consuming, data-driven methods leverage artificial intelligence (AI) techniques to alleviate that workload. Data-driven methods have to be trained in advance to provide their subsequent fast predictions; however, the cost of the training stage is non-negligible. This article presents a predictive model for inferencing future states of a specific fluid simulation that serves as a use case for evaluating different training alternatives. Particularly, this study compares the performance of only CPU, multi-GPU, and distributed approaches for training a time series forecasting deep learning model. With some slight code adaptations, results show and compare, in different implementations, the benefits of distributed GPU-enabled training for predicting high-accuracy states in a fraction of the time needed by the computational fluid dynamics solver.
CFD code adaptation to the FPGA architecture Rojek, Krzysztof; Halbiniak, Kamil; Kuczynski, Lukasz
The international journal of high performance computing applications,
01/2021, Volume:
35, Issue:
1
Journal Article
Peer reviewed
For the last years, we observe the intensive development of accelerated computing platforms. Although current trends indicate a well-established position of GPU devices in the HPC environment, FPGA ...(Field-Programmable Gate Array) aspires to be an alternative solution to offload the CPU computation. This paper presents a systematic adaptation of four various CFD (Computational Fluids Dynamic) kernels to the Xilinx Alveo U250 FPGA. The goal of this paper is to investigate the potential of the FPGA architecture as the future infrastructure able to provide the most complex numerical simulations in the area of fluid flow modeling. The selected kernels are customized to a real-scientific scenario, compatible with the EULAG (Eulerian/semi-Lagrangian) fluid solver. The solver is used to simulate thermo-fluid flows across a wide range of scales and is extensively used in numerical weather prediction. The proposed adaptation is focused on the analysis of the strengths and weaknesses of the FPGA accelerator, considering performance and energy efficiency. The proposed adaptation is compared with a CPU implementation that was strongly optimized to provide realistic and objective benchmarks. The performance results are compared with a set of server CPUs containing various Intel generations, including Intel SkyLake-based CPUs as Xeon Gold 6148 and Xeon Platinum 8168, as well as Intel Xeon E5-2695 CPU based on the IvyBridge architecture. Since all the kernels belong to the group of memory-bound algorithms, our main challenge is to saturate global memory bandwidth and provide data locality with the intensive BRAM (Block RAM) reusing. Our adaptation allows us to reduce the performance per watt up to 80% compared to the CPUs.
Summary
In this work, we propose a method that allows us to reduce energy consumption of an application executed on supercomputing centers. The proposed method is based on a mixed precision ...arithmetic where the precision of data is calibrated at runtime. For this reason, we develop a modified version of the random forest algorithm. The effectiveness of the proposed approach is validated with a real‐life scientific application called MPDATA, which is part of the numerical model used in weather forecasting. The energy efficiency of the proposed method is examined using two GPU‐based clusters. The first of them is the Piz Daint supercomputer, currently ranked 3rd at the TOP500 list (November 2017). It is equipped with NVIDIA Tesla P100 GPU accelerators based on the Pascal architecture. The second is the MICLAB cluster containing NVIDIA Tesla K80 based on the Kepler architecture. The achieved results show that the proposed machine learning method allows us to provide the accuracy of computation comparable with that achieved double precision and reduce the energy consumption up to 36% compared to the double precision version of MPDATA.
Choć koncepcje wolności Spinozy i Leibniza wyrastały w dobie powszechności tezy determinizmu mechanistycznego, którego powszechność została w ubiegłym wieku podważona, ontologiczna argumentacja obu ...filozofów pozostaje aktualna i warta nie tylko rewizji, a nawet porównania. Poddam pod rozwagę dwa stanowiska na rzecz wolności: Spinozjańskie utożsamienie wolności z wiedzą i Leibnizjańską obronę zasady alternatywnych możliwości. Oba stanowiska rozważę z perspektywy argumentacji kompatybilistycznej, wskazując, że rozumienie konieczności w myśli Spinozy i Leibniza nie wyklucza istnienia wolności.
Summary
This work explores the performance of single‐ and multi‐GPU computing on state‐of‐the‐art NVIDIA‐ and AMD‐based server‐class hardware using various programming interfaces to accelerate a ...real‐world scientific application for solidification modeling based on the phase‐field method. The main computations of this memory‐bound application correspond to 20 stencils computed across grid nodes. We investigate the application's scalability for two basic schemes of organizing computation: without and with hiding data transfers behind computation, combined with using either peer‐to‐peer inter‐GPU data transfers through NVIDIA NVLink and AMD Infinity interconnects or communication over the PCIe and main memory. Among the studied programming interfaces is CUDA, HIP, and OpenMP Accelerator Model. While the first two are designed to write the codes for a specific hardware platform, OpenMP enables code portability between NVIDIA and AMD GPUs. The resulting performance is experimentally assessed on computing platforms containing NVIDIA V100 (up to 8 GPUs) and A100 (one GPU), as well as AMD MI210 (one device) and MI250 (up to 8 logical GPUs).
The goal of this study is to parallelize the multidimensional positive definite advection transport algorithm (MPDATA) across a computational cluster equipped with GPUs. Our approach permits us to ...provide an extensive overlapping GPU computations and data transfers, both between computational nodes, as well as between the GPU accelerator and CPU host within a node. For this aim, we decompose a computational domain into two unequal parts which correspond to either data dependent or data independent parts. Then, data transfers can be performed simultaneously with computations corresponding to the second part. Our approach allows for achieving 16.372 Tflop/s using 136 GPUs. To estimate the scalability of the proposed approach, a performance model dedicated to MPDATA simulations is developed. We focus on the analysis of computation and communication execution times, as well as the influence of overlapping data transfers and GPU computations, with regard to the number of nodes.
Intelligence Processing Units (IPU) have proven useful for many AI applications. In this paper, we evaluate them within the emerging field of AI for simulation, where traditional numerical ...simulations are supported by artificial intelligence approaches. We focus specifically on a program for training machine learning models supporting a computational fluid dynamics application. We use custom TensorFlow provided by the Poplar Software Development Kit to adapt the program for the IPU-POD16 platform and investigate its ease of use and performance scalability. Training a model on data from OpenFOAM simulations allows us to get accurate simulation state predictions in test time. We describe how to optimize multi-threading runtime options and utilize the popdist library to overcome a performance bottleneck in feeding training data to the IPU on the host side. Due to communication overheads, using data parallelism to utilize two IPUs instead of one does not improve the throughput. However, once the intra-IPU costs have been paid, the hardware capabilities for inter-IPU communication allow for good scalability. Increasing the number of IPUs from two to 16 improves the throughput from 560.8 to 2805.8 samples/s. Additionally, the experimental results show that reducing the precision of input data storage from FP32 to FP16 allows to improve training throughput by 12%, while tuning selected runtime variables, by up to 6.3%.
•Exploration of the use of AI in CFD using IPU hardware.•Demonstration of the potential to improve the efficiency and accuracy of fluid flow simulations on these devices.•New methods for predicting complex fluid dynamics problems.•Adaptation of a training program for AI-accelerated CFD simulations to the IPU-based POD16 platform.•Evaluation and optimization of models trained on the IPU-POD16 platform in terms of performance and scalability.
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