1#!/usr/bin/env python3 2# Copyright (c) 2017-2018, Lawrence Livermore National Security, LLC. 3# Produced at the Lawrence Livermore National Laboratory. LLNL-CODE-734707. 4# All Rights reserved. See files LICENSE and NOTICE for details. 5# 6# This file is part of CEED, a collection of benchmarks, miniapps, software 7# libraries and APIs for efficient high-order finite element and spectral 8# element discretizations for exascale applications. For more information and 9# source code availability see http://github.com/ceed. 10# 11# The CEED research is supported by the Exascale Computing Project 17-SC-20-SC, 12# a collaborative effort of two U.S. Department of Energy organizations (Office 13# of Science and the National Nuclear Security Administration) responsible for 14# the planning and preparation of a capable exascale ecosystem, including 15# software, applications, hardware, advanced system engineering and early 16# testbed platforms, in support of the nation's exascale computing imperative. 17 18import pandas as pd 19import fileinput 20import pprint 21 22##### Read all input files specified on the command line, or stdin and parse 23##### the content, storing it as a pandas dataframe 24def read_logs(files=None): 25 it=fileinput.input(files) 26 state = 0 27 line='' 28 i=0 29 data = dict( 30 file='unknown', 31 backend='unknown', 32 test='unknown', 33 num_procs=0, 34 num_procs_node=0, 35 degree=0, 36 quadrature_pts=0, 37 code='libCEED', 38 ) 39 40 runs=[] 41 while True: 42 ## 43 if state%2==0: 44 ## 45 try: 46 line=next(it) 47 i=i+1 48 except StopIteration: 49 break 50 state=state+1 51 ## 52 elif state==1: 53 ## 54 state=0 55 ## Legacy header contains number of MPI tasks 56 if 'Running the tests using a total of' in line: 57 data['num_procs'] = int(line.split('a total of ',1)[1].split(None,1)[0]) 58 ## MPI tasks per node 59 elif 'tasks per node' in line: 60 data['num_procs_node'] = int(line.split(' tasks per',1)[0].rsplit(None,1)[1]) 61 ## New Benchmark Problem 62 elif "CEED Benchmark Problem" in line: 63 # Starting a new block 64 data = data.copy() 65 runs.append(data) 66 data['file'] = fileinput.filename() 67 data['test'] = line.split()[-2] + " " + line.split('-- ')[1] 68 data['case']='scalar' if (('Problem 1' in line) or ('Problem 3' in line) 69 or ('Problem 5' in line)) else 'vector' 70 elif "Hostname" in line: 71 data['hostname'] = line.split(':')[1].strip() 72 elif "Total ranks" in line: 73 data['num_procs'] = int(line.split(':')[1].strip()) 74 elif "Ranks per node" in line: 75 data['num_procs_node'] = int(line.split(':')[1].strip()) 76 ## Backend 77 elif 'libCEED Backend MemType' in line: 78 data['backend_memtype']=line.split(':')[1].strip() 79 elif 'libCEED Backend' in line: 80 data['backend']=line.split(':')[1].strip() 81 ## P 82 elif 'Basis Nodes' in line: 83 data['degree']=int(line.split(':')[1]) - 1 84 ## Q 85 elif 'Quadrature Points' in line: 86 qpts=int(line.split(':')[1]) 87 data['quadrature_pts']=qpts**3 88 ## Total DOFs 89 elif 'Global nodes' in line: 90 data['num_unknowns']=int(line.split(':')[1]) 91 if data['case']=='vector': 92 data['num_unknowns']*=3 93 ## Number of elements 94 elif 'Local Elements' in line: 95 data['num_elem']=int(line.split(':')[1].split()[0])*data['num_procs'] 96 ## CG Solve Time 97 elif 'Total KSP Iterations' in line: 98 data['ksp_its'] = int(line.split(':')[1].split()[0]) 99 elif 'CG Solve Time' in line: 100 data['time_per_it'] = float(line.split(':')[1].split()[0]) / data['ksp_its'] 101 ## CG DOFs/Sec 102 elif 'DoFs/Sec in CG' in line: 103 data['cg_iteration_dps']=1e6*float(line.split(':')[1].split()[0]) 104 ## End of output 105 106 return pd.DataFrame(runs) 107 108if __name__ == "__main__": 109 runs = read_logs() 110 print('Number of test runs read: %i'%len(runs)) 111 print(runs) 112