In-Situ Statistical Analysis of Autotune Simulation Data using Graphical Processing Units

Ranjan, Niloo¹  Sanyal, Jibonananda¹  New, Joshua Ryan¹ 

[1] ORNL

关键词: simulation;    graphical processing units;    accelerators;    linear algebra;    in-situ analytics;   

DOI  :  10.2172/1093099
RP-ID  :  ORNL/TM-2013/387
PID  :  OSTI ID: 1093099
Others  :  Other: BT0305000
Others  :  CEBT105

美国|英语

来源: SciTech Connect

【 摘 要 】

Developing accurate building energy simulation models to assist energy efficiency at speed and scale is one of the research goals of the Whole-Building and Community Integration group, which is a part of Building Technologies Research and Integration Center (BTRIC) at Oak Ridge National Laboratory (ORNL). The aim of the Autotune project is to speed up the automated calibration of building energy models to match measured utility or sensor data. The workflow of this project takes input parameters and runs EnergyPlus simulations on Oak Ridge Leadership Computing Facility s (OLCF) computing resources such as Titan, the world s second fastest supercomputer. Multiple simulations run in parallel on nodes having 16 processors each and a Graphics Processing Unit (GPU). Each node produces a 5.7 GB output file comprising 256 files from 64 simulations. Four types of output data covering monthly, daily, hourly, and 15-minute time steps for each annual simulation is produced. A total of 270TB+ of data has been produced. In this project, the simulation data is statistically analyzed in-situ using GPUs while annual simulations are being computed on the traditional processors. Titan, with its recent addition of 18,688 Compute Unified Device Architecture (CUDA) capable NVIDIA GPUs, has greatly extended its capability for massively parallel data processing. CUDA is used along with C/MPI to calculate statistical metrics such as sum, mean, variance, and standard deviation leveraging GPU acceleration. The workflow developed in this project produces statistical summaries of the data which reduces by multiple orders of magnitude the time and amount of data that needs to be stored. These statistical capabilities are anticipated to be useful for sensitivity analysis of EnergyPlus simulations.