1#!/usr/bin/env python 2import os,sys 3sys.path.append(os.path.join(os.environ['PETSC_DIR'], 'config')) 4from builder2 import buildExample 5from benchmarkBatch import generateBatchScript 6 7class PETSc(object): 8 def __init__(self): 9 return 10 11 def dir(self): 12 '''Return the root directory for the PETSc tree (usually $PETSC_DIR)''' 13 # This should search for a valid PETSc 14 return os.environ['PETSC_DIR'] 15 16 def arch(self): 17 '''Return the PETSc build label (usually $PETSC_ARCH)''' 18 # This should be configurable 19 return os.environ['PETSC_ARCH'] 20 21 def mpiexec(self): 22 '''Return the path for the mpi launch executable''' 23 mpiexec = os.path.join(self.dir(), self.arch(), 'bin', 'mpiexec') 24 if not os.path.isfile(mpiexec): 25 return None 26 return mpiexec 27 28 def example(self, num): 29 '''Return the path to the executable for a given example number''' 30 return os.path.join(self.dir(), self.arch(), 'lib', 'ex'+str(num)+'-obj', 'ex'+str(num)) 31 32 def source(self, library, num): 33 '''Return the path to the sources for a given example number''' 34 d = os.path.join(self.dir(), 'src', library.lower(), 'examples', 'tutorials') 35 name = 'ex'+str(num) 36 sources = [] 37 for f in os.listdir(d): 38 if f == name+'.c': 39 sources.insert(0, f) 40 elif f.startswith(name) and f.endswith('.cu'): 41 sources.append(f) 42 return map(lambda f: os.path.join(d, f), sources) 43 44class PETScExample(object): 45 def __init__(self, library, num, **defaultOptions): 46 self.petsc = PETSc() 47 self.library = library 48 self.num = num 49 self.opts = defaultOptions 50 return 51 52 @staticmethod 53 def runShellCommand(command, cwd = None, log = True): 54 import subprocess 55 56 Popen = subprocess.Popen 57 PIPE = subprocess.PIPE 58 if log: print 'Executing: %s\n' % (command,) 59 pipe = Popen(command, cwd=cwd, stdin=None, stdout=PIPE, stderr=PIPE, bufsize=-1, shell=True, universal_newlines=True) 60 (out, err) = pipe.communicate() 61 ret = pipe.returncode 62 return (out, err, ret) 63 64 def optionsToString(self, **opts): 65 '''Convert a dictionary of options to a command line argument string''' 66 a = [] 67 for key,value in opts.iteritems(): 68 if value is None: 69 a.append('-'+key) 70 else: 71 a.append('-'+key+' '+str(value)) 72 return ' '.join(a) 73 74 def run(self, numProcs = 1, log = True, **opts): 75 if self.petsc.mpiexec() is None: 76 cmd = self.petsc.example(self.num) 77 else: 78 cmd = ' '.join([self.petsc.mpiexec(), '-n', str(numProcs), self.petsc.example(self.num)]) 79 cmd += ' '+self.optionsToString(**self.opts)+' '+self.optionsToString(**opts) 80 if 'batch' in opts and opts['batch']: 81 del opts['batch'] 82 filename = generateBatchScript(self.num, numProcs, 120, ' '+self.optionsToString(**self.opts)+' '+self.optionsToString(**opts)) 83 # Submit job 84 out, err, ret = self.runShellCommand('qsub -q gpu '+filename, log = log) 85 if ret: 86 print err 87 print out 88 else: 89 out, err, ret = self.runShellCommand(cmd, log = log) 90 if ret: 91 print err 92 print out 93 return out 94 95def processSummary(moduleName, defaultStage, eventNames, times, events): 96 '''Process the Python log summary into plot data''' 97 m = __import__(moduleName) 98 reload(m) 99 # Total Time 100 times.append(m.Time[0]) 101 # Particular events 102 for name in eventNames: 103 if name.find(':') >= 0: 104 stageName, name = name.split(':', 1) 105 stage = getattr(m, stageName) 106 else: 107 stage = getattr(m, defaultStage) 108 if name in stage.event: 109 if not name in events: 110 events[name] = [] 111 try: 112 events[name].append((stage.event[name].Time[0], stage.event[name].Flops[0]/(stage.event[name].Time[0] * 1e6))) 113 except ZeroDivisionError: 114 events[name].append((stage.event[name].Time[0], 0)) 115 return 116 117def plotTime(library, num, eventNames, sizes, times, events): 118 from pylab import legend, plot, show, title, xlabel, ylabel 119 import numpy as np 120 121 arches = sizes.keys() 122 data = [] 123 for arch in arches: 124 data.append(sizes[arch]) 125 data.append(times[arch]) 126 plot(*data) 127 title('Performance on '+library+' Example '+str(num)) 128 xlabel('Number of Dof') 129 ylabel('Time (s)') 130 legend(arches, 'upper left', shadow = True) 131 show() 132 return 133 134def plotEventTime(library, num, eventNames, sizes, times, events, filename = None): 135 from pylab import close, legend, plot, savefig, show, title, xlabel, ylabel 136 import numpy as np 137 138 close() 139 arches = sizes.keys() 140 bs = events[arches[0]].keys()[0] 141 data = [] 142 names = [] 143 for event, color in zip(eventNames, ['b', 'g', 'r', 'y']): 144 for arch, style in zip(arches, ['-', ':']): 145 if event in events[arch][bs]: 146 names.append(arch+'-'+str(bs)+' '+event) 147 data.append(sizes[arch][bs]) 148 data.append(np.array(events[arch][bs][event])[:,0]) 149 data.append(color+style) 150 else: 151 print 'Could not find %s in %s-%d events' % (event, arch, bs) 152 print data 153 plot(*data) 154 title('Performance on '+library+' Example '+str(num)) 155 xlabel('Number of Dof') 156 ylabel('Time (s)') 157 legend(names, 'upper left', shadow = True) 158 if filename is None: 159 show() 160 else: 161 savefig(filename) 162 return 163 164def plotEventFlop(library, num, eventNames, sizes, times, events, filename = None): 165 from pylab import legend, plot, savefig, semilogy, show, title, xlabel, ylabel 166 import numpy as np 167 168 arches = sizes.keys() 169 bs = events[arches[0]].keys()[0] 170 data = [] 171 names = [] 172 for event, color in zip(eventNames, ['b', 'g', 'r', 'y']): 173 for arch, style in zip(arches, ['-', ':']): 174 if event in events[arch][bs]: 175 names.append(arch+'-'+str(bs)+' '+event) 176 data.append(sizes[arch][bs]) 177 data.append(1e-3*np.array(events[arch][bs][event])[:,1]) 178 data.append(color+style) 179 else: 180 print 'Could not find %s in %s-%d events' % (event, arch, bs) 181 semilogy(*data) 182 title('Performance on '+library+' Example '+str(num)) 183 xlabel('Number of Dof') 184 ylabel('Computation Rate (GF/s)') 185 legend(names, 'upper left', shadow = True) 186 if filename is None: 187 show() 188 else: 189 savefig(filename) 190 return 191 192def plotSummaryLine(library, num, eventNames, sizes, times, events): 193 from pylab import legend, plot, show, title, xlabel, ylabel 194 import numpy as np 195 showTime = False 196 showEventTime = True 197 showEventFlops = True 198 arches = sizes.keys() 199 # Time 200 if showTime: 201 data = [] 202 for arch in arches: 203 data.append(sizes[arch]) 204 data.append(times[arch]) 205 plot(*data) 206 title('Performance on '+library+' Example '+str(num)) 207 xlabel('Number of Dof') 208 ylabel('Time (s)') 209 legend(arches, 'upper left', shadow = True) 210 show() 211 # Common event time 212 # We could make a stacked plot like Rio uses here 213 if showEventTime: 214 bs = events[arches[0]].keys()[0] 215 data = [] 216 names = [] 217 for event, color in zip(eventNames, ['b', 'g', 'r', 'y']): 218 for arch, style in zip(arches, ['-', ':']): 219 if event in events[arch][bs]: 220 names.append(arch+'-'+str(bs)+' '+event) 221 data.append(sizes[arch][bs]) 222 data.append(np.array(events[arch][bs][event])[:,0]) 223 data.append(color+style) 224 else: 225 print 'Could not find %s in %s-%d events' % (event, arch, bs) 226 print data 227 plot(*data) 228 title('Performance on '+library+' Example '+str(num)) 229 xlabel('Number of Dof') 230 ylabel('Time (s)') 231 legend(names, 'upper left', shadow = True) 232 show() 233 # Common event flops 234 # We could make a stacked plot like Rio uses here 235 if showEventFlops: 236 bs = events[arches[0]].keys()[0] 237 data = [] 238 names = [] 239 for event, color in zip(eventNames, ['b', 'g', 'r', 'y']): 240 for arch, style in zip(arches, ['-', ':']): 241 if event in events[arch][bs]: 242 names.append(arch+'-'+str(bs)+' '+event) 243 data.append(sizes[arch][bs]) 244 data.append(np.array(events[arch][bs][event])[:,1]) 245 data.append(color+style) 246 else: 247 print 'Could not find %s in %s-%d events' % (event, arch, bs) 248 plot(*data) 249 title('Performance on '+library+' Example '+str(num)) 250 xlabel('Number of Dof') 251 ylabel('Computation Rate (MF/s)') 252 legend(names, 'upper left', shadow = True) 253 show() 254 return 255 256def plotSummaryBar(library, num, eventNames, sizes, times, events): 257 import numpy as np 258 import matplotlib.pyplot as plt 259 260 eventColors = ['b', 'g', 'r', 'y'] 261 arches = sizes.keys() 262 names = [] 263 N = len(sizes[arches[0]]) 264 width = 0.2 265 ind = np.arange(N) - 0.25 266 bars = {} 267 for arch in arches: 268 bars[arch] = [] 269 bottom = np.zeros(N) 270 for event, color in zip(eventNames, eventColors): 271 names.append(arch+' '+event) 272 times = np.array(events[arch][event])[:,0] 273 bars[arch].append(plt.bar(ind, times, width, color=color, bottom=bottom)) 274 bottom += times 275 ind += 0.3 276 277 plt.xlabel('Number of Dof') 278 plt.ylabel('Time (s)') 279 plt.title('GPU vs. CPU Performance on '+library+' Example '+str(num)) 280 plt.xticks(np.arange(N), map(str, sizes[arches[0]])) 281 #plt.yticks(np.arange(0,81,10)) 282 #plt.legend( (p1[0], p2[0]), ('Men', 'Women') ) 283 plt.legend([bar[0] for bar in bars[arches[0]]], eventNames, 'upper right', shadow = True) 284 285 plt.show() 286 return 287 288def getDMComplexSize(dim, out): 289 '''Retrieves the number of cells from ''' 290 size = 0 291 for line in out.split('\n'): 292 if line.strip().startswith(str(dim)+'-cells: '): 293 size = int(line.strip()[9:]) 294 break 295 return size 296 297def run_DMDA(ex, name, opts, args, sizes, times, events, log=True): 298 for n in map(int, args.size): 299 ex.run(log=log, da_grid_x=n, da_grid_y=n, **opts) 300 sizes[name].append(n*n * args.comp) 301 processSummary('summary', args.stage, args.events, times[name], events[name]) 302 return 303 304def run_DMComplex(ex, name, opts, args, sizes, times, events, log=True): 305 # This should eventually be replaced by a direct FFC/Ignition interface 306 if args.operator == 'laplacian': 307 numComp = 1 308 elif args.operator == 'elasticity': 309 numComp = args.dim 310 else: 311 raise RuntimeError('Unknown operator: %s' % args.operator) 312 313 for numBlock in [2**i for i in map(int, args.blockExp)]: 314 opts['gpu_blocks'] = numBlock 315 # Generate new block size 316 cmd = './bin/pythonscripts/PetscGenerateFEMQuadrature.py %d %d %d %d %s %s.h' % (args.dim, args.order, numComp, numBlock, args.operator, os.path.splitext(source[0])[0]) 317 print(cmd) 318 ret = os.system('python '+cmd) 319 args.files = ['['+','.join(source)+']'] 320 buildExample(args) 321 sizes[name][numBlock] = [] 322 times[name][numBlock] = [] 323 events[name][numBlock] = {} 324 for r in map(float, args.refine): 325 out = ex.run(log=log, refinement_limit=r, **opts) 326 sizes[name][numBlock].append(getDMComplexSize(args.dim, out)) 327 processSummary('summary', args.stage, args.events, times[name][numBlock], events[name][numBlock]) 328 return 329 330def outputData(sizes, times, events, name = 'output.py'): 331 if os.path.exists(name): 332 base, ext = os.path.splitext(name) 333 num = 1 334 while os.path.exists(base+str(num)+ext): 335 num += 1 336 name = base+str(num)+ext 337 with file(name, 'w') as f: 338 f.write('#PETSC_ARCH='+os.environ['PETSC_ARCH']+' '+' '.join(sys.argv)+'\n') 339 f.write('sizes = '+repr(sizes)+'\n') 340 f.write('times = '+repr(times)+'\n') 341 f.write('events = '+repr(events)+'\n') 342 return 343 344if __name__ == '__main__': 345 import argparse 346 347 parser = argparse.ArgumentParser(description = 'PETSc Benchmarking', 348 epilog = 'This script runs src/<library>/examples/tutorials/ex<num>, For more information, visit http://www.mcs.anl.gov/petsc', 349 formatter_class = argparse.ArgumentDefaultsHelpFormatter) 350 parser.add_argument('--library', default='SNES', help='The PETSc library used in this example') 351 parser.add_argument('--num', type = int, default='5', help='The example number') 352 parser.add_argument('--module', default='summary', help='The module for timing output') 353 parser.add_argument('--stage', default='Main_Stage', help='The default logging stage') 354 parser.add_argument('--events', nargs='+', help='Events to process') 355 parser.add_argument('--batch', action='store_true', default=False, help='Generate batch files for the runs instead') 356 parser.add_argument('--daemon', action='store_true', default=False, help='Run as a daemon') 357 subparsers = parser.add_subparsers(help='DM types') 358 359 parser_dmda = subparsers.add_parser('DMDA', help='Use a DMDA for the problem geometry') 360 parser_dmda.add_argument('--size', nargs='+', default=['10'], help='Grid size (implementation dependent)') 361 parser_dmda.add_argument('--comp', type = int, default='1', help='Number of field components') 362 parser_dmda.add_argument('runs', nargs='*', help='Run descriptions: <name>=<args>') 363 364 parser_dmmesh = subparsers.add_parser('DMComplex', help='Use a DMComplex for the problem geometry') 365 parser_dmmesh.add_argument('--dim', type = int, default='2', help='Spatial dimension') 366 parser_dmmesh.add_argument('--refine', nargs='+', default=['0.0'], help='List of refinement limits') 367 parser_dmmesh.add_argument('--order', type = int, default='1', help='Order of the finite element') 368 parser_dmmesh.add_argument('--operator', default='laplacian', help='The operator name') 369 parser_dmmesh.add_argument('--blockExp', nargs='+', default=range(0, 5), help='List of block exponents j, block size is 2^j') 370 parser_dmmesh.add_argument('runs', nargs='*', help='Run descriptions: <name>=<args>') 371 372 args = parser.parse_args() 373 print(args) 374 if hasattr(args, 'comp'): 375 args.dmType = 'DMDA' 376 else: 377 args.dmType = 'DMComplex' 378 379 ex = PETScExample(args.library, args.num, log_summary='summary.dat', log_summary_python = None if args.batch else args.module+'.py', preload='off') 380 source = ex.petsc.source(args.library, args.num) 381 sizes = {} 382 times = {} 383 events = {} 384 log = not args.daemon 385 386 if args.daemon: 387 import daemon 388 print 'Starting daemon' 389 daemon.createDaemon('.') 390 391 for run in args.runs: 392 name, stropts = run.split('=', 1) 393 opts = dict([t if len(t) == 2 else (t[0], None) for t in [arg.split('=', 1) for arg in stropts.split(' ')]]) 394 if args.dmType == 'DMDA': 395 sizes[name] = [] 396 times[name] = [] 397 events[name] = {} 398 run_DMDA(ex, name, opts, args, sizes, times, events, log=log) 399 elif args.dmType == 'DMComplex': 400 sizes[name] = {} 401 times[name] = {} 402 events[name] = {} 403 run_DMComplex(ex, name, opts, args, sizes, times, events, log=log) 404 outputData(sizes, times, events) 405 if not args.batch and log: plotSummaryLine(args.library, args.num, args.events, sizes, times, events) 406# Benchmark for ex50 407# ./src/benchmarks/benchmarkExample.py --events VecMDot VecMAXPY KSPGMRESOrthog MatMult VecCUSPCopyTo VecCUSPCopyFrom MatCUSPCopyTo --num 50 DMDA --size 10 20 50 100 --comp 4 CPU='pc_type=none mat_no_inode dm_vec_type=seq dm_mat_type=seqaij' GPU='pc_type=none mat_no_inode dm_vec_type=seqcusp dm_mat_type=seqaijcusp cusp_synchronize' 408# Benchmark for ex52 409# ./src/benchmarks/benchmarkExample.py --events IntegBatchCPU IntegBatchGPU IntegGPUOnly --num 52 DMComplex --refine 0.0625 0.00625 0.000625 0.0000625 --blockExp 4 --order=1 CPU='dm_view show_residual=0 compute_function batch' GPU='dm_view show_residual=0 compute_function batch gpu gpu_batches=8' 410# ./src/benchmarks/benchmarkExample.py --events IntegBatchCPU IntegBatchGPU IntegGPUOnly --num 52 DMComplex --refine 0.0625 0.00625 0.000625 0.0000625 --blockExp 4 --order=1 --operator=elasticity CPU='dm_view op_type=elasticity show_residual=0 compute_function batch' GPU='dm_view op_type=elasticity show_residual=0 compute_function batch gpu gpu_batches=8' 411# ./src/benchmarks/benchmarkExample.py --events IntegBatchCPU IntegBatchGPU IntegGPUOnly --num 52 DMComplex --dim=3 --refine 0.0625 0.00625 0.000625 0.0000625 --blockExp 4 --order=1 CPU='dim=3 dm_view show_residual=0 compute_function batch' GPU='dim=3 dm_view show_residual=0 compute_function batch gpu gpu_batches=8' 412