gem5  v22.1.0.0
storage.test.cc
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28 
29 #include <gtest/gtest-spi.h>
30 #include <gtest/gtest.h>
31 
32 #include <cmath>
33 
34 #include "base/gtest/cur_tick_fake.hh"
35 #include "base/gtest/logging.hh"
36 #include "base/stats/storage.hh"
37 
38 using namespace gem5;
39 
40 // Instantiate the fake class to have a valid curTick of 0
41 GTestTickHandler tickHandler;
42 
44 void increaseTick() { tickHandler.setCurTick(curTick() + 1); }
45 
47 struct ValueSamples
48 {
49  statistics::Counter value;
50  statistics::Counter numSamples;
51 
52  ValueSamples(statistics::Counter value, statistics::Counter num_samples)
53  : value(value), numSamples(num_samples)
54  {
55  }
56 };
57 
59 TEST(StatsStatStorTest, SetValueResult)
60 {
61  statistics::StatStor stor(nullptr);
62  statistics::Counter val;
63 
64  val = 10;
65  stor.set(val);
66  ASSERT_EQ(stor.value(), val);
67  ASSERT_EQ(stor.result(), statistics::Result(val));
68 
69  val = 1234;
70  stor.set(val);
71  ASSERT_EQ(stor.value(), val);
72  ASSERT_EQ(stor.result(), statistics::Result(val));
73 }
74 
76 TEST(StatsStatStorTest, Prepare)
77 {
78  statistics::StatStor stor(nullptr);
79  statistics::Counter val;
80 
81  val = 10;
82  stor.set(val);
83  stor.prepare(nullptr);
84  ASSERT_EQ(stor.value(), val);
85  ASSERT_EQ(stor.result(), statistics::Result(val));
86 }
87 
89 TEST(StatsStatStorTest, IncDec)
90 {
91  statistics::StatStor stor(nullptr);
92  statistics::Counter diff_val = 10;
93  statistics::Counter val = 0;
94 
95  stor.inc(diff_val);
96  val += diff_val;
97  ASSERT_EQ(stor.value(), val);
98 
99  stor.inc(diff_val);
100  val += diff_val;
101  ASSERT_EQ(stor.value(), val);
102 
103  stor.dec(diff_val);
104  val -= diff_val;
105  ASSERT_EQ(stor.value(), val);
106 
107  stor.dec(diff_val);
108  val -= diff_val;
109  ASSERT_EQ(stor.value(), val);
110 }
111 
117 TEST(StatsStatStorTest, ZeroReset)
118 {
119  statistics::StatStor stor(nullptr);
120  statistics::Counter val = 10;
121 
122  ASSERT_TRUE(stor.zero());
123 
124  stor.reset(nullptr);
125  ASSERT_TRUE(stor.zero());
126 
127  stor.reset(nullptr);
128  stor.inc(val);
129  ASSERT_FALSE(stor.zero());
130 }
131 
133 TEST(StatsAvgStorTest, SetValueResult)
134 {
135  statistics::AvgStor stor(nullptr);
136  statistics::Counter val;
137  statistics::Result total = 0;
138  Tick last_reset = 0;
139  Tick last_tick = 0;
140 
141  val = 10;
142  stor.set(val);
143  last_tick = curTick();
144  ASSERT_EQ(stor.value(), val);
145  ASSERT_EQ(stor.result(), statistics::Result(total + val) /
146  statistics::Result(curTick() - last_reset + 1));
147  increaseTick();
148 
149  total += val * (curTick() - last_tick);
150  val = 1234;
151  stor.set(val);
152  last_tick = curTick();
153  ASSERT_EQ(stor.value(), val);
154  ASSERT_EQ(stor.result(), statistics::Result(total + val) /
155  statistics::Result(curTick() - last_reset + 1));
156  increaseTick();
157 }
158 
159 #if TRACING_ON
163 TEST(StatsAvgStorDeathTest, Result)
164 {
165  statistics::AvgStor stor(nullptr);
166  increaseTick();
167  ASSERT_DEATH(stor.result(), ".+");
168 }
169 #endif
170 
175 TEST(StatsAvgStorTest, Prepare)
176 {
177  statistics::AvgStor stor(nullptr);
178  statistics::Counter val = 10;
179  statistics::Result total = 0;
180  Tick last_reset = 0;
181  Tick last_tick = 0;
182 
183  val = 10;
184  stor.set(val);
185  last_tick = curTick();
186  ASSERT_EQ(stor.value(), val);
187  ASSERT_EQ(stor.result(), statistics::Result(total + val) /
188  statistics::Result(curTick() - last_reset + 1));
189  increaseTick();
190 
191  total += val * (curTick() - last_tick);
192  stor.prepare(nullptr);
193  last_tick = curTick();
194  ASSERT_EQ(stor.value(), val);
195  ASSERT_EQ(stor.result(), statistics::Result(total + val) /
196  statistics::Result(curTick() - last_reset + 1));
197  increaseTick();
198 }
199 
201 TEST(StatsAvgStorTest, IncDec)
202 {
203  statistics::AvgStor stor(nullptr);
204  statistics::Counter diff_val = 10;
205  statistics::Counter val = 0;
206 
207  stor.set(diff_val);
208  val += diff_val;
209  ASSERT_EQ(stor.value(), val);
210 
211  stor.inc(diff_val);
212  val += diff_val;
213  ASSERT_EQ(stor.value(), val);
214 
215  stor.inc(diff_val);
216  val += diff_val;
217  ASSERT_EQ(stor.value(), val);
218 
219  stor.dec(diff_val);
220  val -= diff_val;
221  ASSERT_EQ(stor.value(), val);
222 
223  stor.dec(diff_val);
224  val -= diff_val;
225  ASSERT_EQ(stor.value(), val);
226 }
227 
233 TEST(StatsAvgStorTest, ZeroReset)
234 {
235  statistics::AvgStor stor(nullptr);
236  statistics::Counter val = 10;
237 
238  ASSERT_TRUE(stor.zero());
239 
240  stor.reset(nullptr);
241  ASSERT_TRUE(stor.zero());
242 
243  // Set current value to val, reset total and increase tick, so that the
244  // next call to set will update the total to be different from zero
245  stor.inc(val);
246  stor.reset(nullptr);
247  increaseTick();
248  stor.inc(val);
249  ASSERT_FALSE(stor.zero());
250 }
251 
252 #if TRACING_ON
254 TEST(StatsDistStorDeathTest, BucketSize0)
255 {
256  EXPECT_ANY_THROW(statistics::DistStor::Params params(0, 5, 0));
257 }
258 #endif
259 
265 TEST(StatsDistStorTest, ZeroReset)
266 {
267  statistics::DistStor::Params params(0, 99, 10);
268  statistics::DistStor stor(&params);
269  statistics::Counter val = 10;
270  statistics::Counter num_samples = 5;
271 
272  ASSERT_TRUE(stor.zero());
273 
274  stor.reset(&params);
275  stor.sample(val, num_samples);
276  ASSERT_FALSE(stor.zero());
277 
278  stor.reset(&params);
279  ASSERT_TRUE(stor.zero());
280 }
281 
286 TEST(StatsDistStorTest, Size)
287 {
288  statistics::Counter val = 10;
289  statistics::Counter num_samples = 5;
290  statistics::Counter size = 20;
291  statistics::DistData data;
292 
293  statistics::DistStor::Params params(0, 19, 1);
294  statistics::DistStor stor(&params);
295 
296  ASSERT_EQ(stor.size(), size);
297  stor.sample(val, num_samples);
298  ASSERT_EQ(stor.size(), size);
299  stor.prepare(&params, data);
300  ASSERT_EQ(stor.size(), size);
301  stor.reset(&params);
302  ASSERT_EQ(stor.size(), size);
303  stor.zero();
304  ASSERT_EQ(stor.size(), size);
305 }
306 
314 void
315 checkExpectedDistData(const statistics::DistData& data,
316  const statistics::DistData& expected_data, bool no_log = true)
317 {
318  ASSERT_EQ(data.type, expected_data.type);
319  ASSERT_EQ(data.min, expected_data.min);
320  ASSERT_EQ(data.max, expected_data.max);
321  ASSERT_EQ(data.bucket_size, expected_data.bucket_size);
322  ASSERT_EQ(data.min_val, expected_data.min_val);
323  ASSERT_EQ(data.max_val, expected_data.max_val);
324  ASSERT_EQ(data.sum, expected_data.sum);
325  ASSERT_EQ(data.squares, expected_data.squares);
326  if (!no_log) {
327  ASSERT_EQ(data.logs, expected_data.logs);
328  }
329  ASSERT_EQ(data.samples, expected_data.samples);
330  ASSERT_EQ(data.cvec.size(), expected_data.cvec.size());
331  for (int i = 0; i < expected_data.cvec.size(); i++) {
332  ASSERT_EQ(data.cvec[i], expected_data.cvec[i]);
333  }
334 }
335 
346 void
347 prepareCheckDistStor(statistics::DistStor::Params& params,
348  ValueSamples* values, int num_values, statistics::DistData& expected_data)
349 {
350  statistics::DistStor stor(&params);
351 
352  statistics::Counter val;
353  statistics::DistData data;
354 
355  expected_data.min = params.min;
356  expected_data.max = params.max;
357  expected_data.sum = 0;
358  expected_data.squares = 0;
359  expected_data.logs = 0;
360  expected_data.samples = 0;
361 
362  // Populate storage with more data
363  for (int i = 0; i < num_values; i++) {
364  stor.sample(values[i].value, values[i].numSamples);
365 
366  val = values[i].value * values[i].numSamples;
367  expected_data.sum += val;
368  expected_data.squares += values[i].value * val;
369  expected_data.samples += values[i].numSamples;
370  }
371  stor.prepare(&params, data);
372 
373  // DistStor does not use log
374  checkExpectedDistData(data, expected_data, true);
375 }
376 
378 TEST(StatsDistStorTest, SamplePrepareSingle)
379 {
380  statistics::DistStor::Params params(0, 99, 5);
381 
382  ValueSamples values[] = {{10, 5}};
383  int num_values = sizeof(values) / sizeof(ValueSamples);
384 
385  // Setup expected data
386  statistics::DistData expected_data;
387  expected_data.type = statistics::Dist;
388  expected_data.bucket_size = params.bucket_size;
389  expected_data.underflow = 0;
390  expected_data.overflow = 0;
391  expected_data.min_val = 10;
392  expected_data.max_val = 10;
393  expected_data.cvec.clear();
394  expected_data.cvec.resize(params.buckets);
395  expected_data.cvec[2] = 5;
396 
397  prepareCheckDistStor(params, values, num_values, expected_data);
398 }
399 
401 TEST(StatsDistStorTest, SamplePrepareMultiple)
402 {
403  statistics::DistStor::Params params(0, 99, 5);
404 
405  // There are 20 buckets: [0,5[, [5,10[, [10,15[, ..., [95,100[.
406  // We test that values that pass the maximum bucket value (1234, 12345678,
407  // 100) are added to the overflow counter, and that the ones below the
408  // minimum bucket value (-10, -1) are added to the underflow counter.
409  // The extremes (0 and 99) are added to check if they go to the first and
410  // last buckets.
411  ValueSamples values[] = {{10, 5}, {1234, 2}, {12345678, 99}, {-10, 4},
412  {17, 17}, {52, 63}, {18, 11}, {0, 1}, {99, 15}, {-1, 200}, {100, 50}};
413  int num_values = sizeof(values) / sizeof(ValueSamples);
414 
415  // Setup variables that should always match params' values
416  statistics::DistData expected_data;
417  expected_data.type = statistics::Dist;
418  expected_data.min_val = -10;
419  expected_data.max_val = 12345678;
420  expected_data.bucket_size = params.bucket_size;
421  expected_data.underflow = 204;
422  expected_data.overflow = 151;
423  expected_data.sum = 0;
424  expected_data.squares = 0;
425  expected_data.samples = 0;
426  expected_data.cvec.clear();
427  expected_data.cvec.resize(params.buckets);
428  expected_data.cvec[0] = 1;
429  expected_data.cvec[2] = 5;
430  expected_data.cvec[3] = 17+11;
431  expected_data.cvec[10] = 63;
432  expected_data.cvec[19] = 15;
433 
434  prepareCheckDistStor(params, values, num_values, expected_data);
435 }
436 
438 TEST(StatsDistStorTest, Reset)
439 {
440  statistics::DistStor::Params params(0, 99, 5);
441  statistics::DistStor stor(&params);
442 
443  // Populate storage with random samples
444  ValueSamples values[] = {{10, 5}, {1234, 2}, {12345678, 99}, {-10, 4},
445  {17, 17}, {52, 63}, {18, 11}, {0, 1}, {99, 15}, {-1, 200}, {100, 50}};
446  int num_values = sizeof(values) / sizeof(ValueSamples);
447  for (int i = 0; i < num_values; i++) {
448  stor.sample(values[i].value, values[i].numSamples);
449  }
450 
451  // Reset storage, and make sure all data has been cleared
452  stor.reset(&params);
453  statistics::DistData data;
454  stor.prepare(&params, data);
455 
456  statistics::DistData expected_data;
457  expected_data.type = statistics::Dist;
458  expected_data.bucket_size = params.bucket_size;
459  expected_data.underflow = 0;
460  expected_data.overflow = 0;
461  expected_data.min = params.min;
462  expected_data.max = params.max;
463  expected_data.min_val = 0;
464  expected_data.max_val = 0;
465  expected_data.sum = 0;
466  expected_data.squares = 0;
467  expected_data.samples = 0;
468  expected_data.cvec.clear();
469  expected_data.cvec.resize(params.buckets);
470 
471  checkExpectedDistData(data, expected_data, true);
472 }
473 
474 #if TRACING_ON
476 TEST(StatsHistStorDeathTest, NotEnoughBuckets0)
477 {
478  EXPECT_ANY_THROW(statistics::HistStor::Params params(0));
479 }
480 
482 TEST(StatsHistStorDeathTest, NotEnoughBuckets1)
483 {
484  EXPECT_ANY_THROW(statistics::HistStor::Params params(1));
485 }
486 #endif
487 
493 TEST(StatsHistStorTest, ZeroReset)
494 {
495  statistics::HistStor::Params params(10);
496  statistics::HistStor stor(&params);
497  statistics::Counter val = 10;
498  statistics::Counter num_samples = 5;
499 
500  ASSERT_TRUE(stor.zero());
501 
502  stor.reset(&params);
503  stor.sample(val, num_samples);
504  ASSERT_FALSE(stor.zero());
505 
506  stor.reset(&params);
507  ASSERT_TRUE(stor.zero());
508 }
509 
514 TEST(StatsHistStorTest, Size)
515 {
516  statistics::Counter val = 10;
517  statistics::Counter num_samples = 5;
518  statistics::DistData data;
519  statistics::size_type sizes[] = {2, 10, 1234};
520 
521  for (int i = 0; i < (sizeof(sizes) / sizeof(statistics::size_type)); i++) {
522  statistics::HistStor::Params params(sizes[i]);
523  statistics::HistStor stor(&params);
524 
525  ASSERT_EQ(stor.size(), sizes[i]);
526  stor.sample(val, num_samples);
527  ASSERT_EQ(stor.size(), sizes[i]);
528  stor.prepare(&params, data);
529  ASSERT_EQ(stor.size(), sizes[i]);
530  stor.reset(&params);
531  ASSERT_EQ(stor.size(), sizes[i]);
532  stor.zero();
533  ASSERT_EQ(stor.size(), sizes[i]);
534  }
535 }
536 
547 void
548 prepareCheckHistStor(statistics::HistStor::Params& params,
549  ValueSamples* values, int num_values, statistics::DistData& expected_data)
550 {
551  statistics::HistStor stor(&params);
552 
553  statistics::Counter val;
554  statistics::DistData data;
555  bool no_log = false;
556 
557  expected_data.min_val = expected_data.min;
558  expected_data.max = expected_data.max_val + expected_data.bucket_size - 1;
559  expected_data.sum = 0;
560  expected_data.squares = 0;
561  expected_data.logs = 0;
562  expected_data.samples = 0;
563 
564  // Populate storage with more data
565  for (int i = 0; i < num_values; i++) {
566  stor.sample(values[i].value, values[i].numSamples);
567 
568  val = values[i].value * values[i].numSamples;
569  expected_data.sum += val;
570  expected_data.squares += values[i].value * val;
571  if (values[i].value < 0) {
572  // Negative values don't have log, so mark log check to be skipped
573  no_log = true;
574  } else {
575  expected_data.logs +=
576  std::log(values[i].value) * values[i].numSamples;
577  }
578  expected_data.samples += values[i].numSamples;
579  }
580  stor.prepare(&params, data);
581  checkExpectedDistData(data, expected_data, no_log);
582 }
583 
588 TEST(StatsHistStorTest, SamplePrepareFit)
589 {
590  statistics::HistStor::Params params(4);
591 
592  // Setup expected data for the hand-carved values given. The final buckets
593  // will be divided at:
594  // Bkt0=[0,1[ , Bkt1=[1,2[, Bkt2=[2,3[, Bkt3=[3,4[
595  ValueSamples values[] = {{0, 5}, {1, 2}, {2, 99}, {3, 4}};
596  const int num_values = sizeof(values) / sizeof(ValueSamples);
597  statistics::DistData expected_data;
598  expected_data.type = statistics::Hist;
599  expected_data.bucket_size = 1;
600  expected_data.min = 0;
601  expected_data.max_val = 3;
602  expected_data.cvec.clear();
603  expected_data.cvec.resize(params.buckets);
604  expected_data.cvec[0] = 5;
605  expected_data.cvec[1] = 2;
606  expected_data.cvec[2] = 99;
607  expected_data.cvec[3] = 4;
608 
609  prepareCheckHistStor(params, values, num_values, expected_data);
610 }
611 
616 TEST(StatsHistStorTest, SamplePrepareSingleGrowUp)
617 {
618  statistics::HistStor::Params params(4);
619 
620  // Setup expected data for the hand-carved values given. Since there
621  // are four buckets, and the highest value is 4, the bucket size will
622  // grow to be 2. The final buckets will be divided at:
623  // Bkt0=[0,2[ , Bkt1=[2,4[, Bkt2=[4,6[, Bkt3=[6,8[
624  ValueSamples values[] = {{0, 5}, {1, 2}, {2, 99}, {4, 4}};
625  const int num_values = sizeof(values) / sizeof(ValueSamples);
626  statistics::DistData expected_data;
627  expected_data.type = statistics::Hist;
628  expected_data.bucket_size = 2;
629  expected_data.min = 0;
630  expected_data.max_val = 6;
631  expected_data.cvec.clear();
632  expected_data.cvec.resize(params.buckets);
633  expected_data.cvec[0] = 5+2;
634  expected_data.cvec[1] = 99;
635  expected_data.cvec[2] = 4;
636  expected_data.cvec[3] = 0;
637 
638  prepareCheckHistStor(params, values, num_values, expected_data);
639 }
640 
645 TEST(StatsHistStorTest, SamplePrepareMultipleGrowUp)
646 {
647  statistics::HistStor::Params params(4);
648 
649  // Setup expected data for the hand-carved values given. Since there
650  // are four buckets, and the highest value is 4, the bucket size will
651  // grow thrice to become 8. The final buckets will be divided at:
652  // Bkt0=[0,8[ , Bkt1=[8,16[, Bkt2=[16,24[, Bkt3=[24,32[
653  ValueSamples values[] = {{0, 5}, {1, 2}, {2, 99}, {16, 4}};
654  const int num_values = sizeof(values) / sizeof(ValueSamples);
655  statistics::DistData expected_data;
656  expected_data.type = statistics::Hist;
657  expected_data.bucket_size = 8;
658  expected_data.min = 0;
659  expected_data.max_val = 24;
660  expected_data.cvec.clear();
661  expected_data.cvec.resize(params.buckets);
662  expected_data.cvec[0] = 5+2+99;
663  expected_data.cvec[1] = 0;
664  expected_data.cvec[2] = 4;
665  expected_data.cvec[3] = 0;
666 
667  prepareCheckHistStor(params, values, num_values, expected_data);
668 }
669 
675 TEST(StatsHistStorTest, SamplePrepareGrowDownOddBuckets)
676 {
677  statistics::HistStor::Params params(5);
678 
679  // Setup expected data for the hand-carved values given. Since there
680  // is a negative value, the min bucket will change, and the bucket size
681  // will grow to be 2. The final buckets will be divided at:
682  // Bkt0=[-4,-2[ , Bkt1=[-2,-0[, Bkt2=[0,2[, Bkt3=[2,4[, Bkt4=[4,6[
683  ValueSamples values[] =
684  {{0, 5}, {1, 2}, {2, 99}, {3, 12}, {4, 33}, {-1, 4}};
685  const int num_values = sizeof(values) / sizeof(ValueSamples);
686  statistics::DistData expected_data;
687  expected_data.type = statistics::Hist;
688  expected_data.bucket_size = 2;
689  expected_data.min = -4;
690  expected_data.max_val = 4;
691  expected_data.cvec.clear();
692  expected_data.cvec.resize(params.buckets);
693  expected_data.cvec[0] = 0;
694  expected_data.cvec[1] = 4;
695  expected_data.cvec[2] = 5+2;
696  expected_data.cvec[3] = 99+12;
697  expected_data.cvec[4] = 33;
698 
699  prepareCheckHistStor(params, values, num_values, expected_data);
700 }
701 
707 TEST(StatsHistStorTest, SamplePrepareGrowDownEvenBuckets)
708 {
709  statistics::HistStor::Params params(4);
710 
711  // Setup expected data for the hand-carved values given. Since there
712  // is a negative value, the min bucket will change, and the bucket size
713  // will grow to be 2. The final buckets will be divided at:
714  // Bkt0=[-4,-2[ , Bkt1=[-2,0[, Bkt2=[0,2[, Bkt3=[2,4[
715  ValueSamples values[] = {{0, 5}, {1, 2}, {2, 99}, {-1, 4}};
716  const int num_values = sizeof(values) / sizeof(ValueSamples);
717  statistics::DistData expected_data;
718  expected_data.type = statistics::Hist;
719  expected_data.bucket_size = 2;
720  expected_data.min = -4;
721  expected_data.max_val = 2;
722  expected_data.cvec.clear();
723  expected_data.cvec.resize(params.buckets);
724  expected_data.cvec[0] = 0;
725  expected_data.cvec[1] = 4;
726  expected_data.cvec[2] = 5+2;
727  expected_data.cvec[3] = 99;
728 
729  prepareCheckHistStor(params, values, num_values, expected_data);
730 }
731 
737 TEST(StatsHistStorTest, SamplePrepareGrowDownGrowOutOddBuckets)
738 {
739  statistics::HistStor::Params params(5);
740 
741  // Setup expected data for the hand-carved values given. Since there
742  // is a negative value, the min bucket will change, and the bucket size
743  // will grow to be 8. The final buckets will be divided at:
744  // Bkt0=[-16,-8[ , Bkt1=[-8,0[, Bkt2=[0,8[, Bkt3=[8,16[, Bkt4=[16,24[
745  ValueSamples values[] =
746  {{0, 5}, {1, 2}, {2, 99}, {3, 12}, {4, 33}, {-12, 4}};
747  const int num_values = sizeof(values) / sizeof(ValueSamples);
748  statistics::DistData expected_data;
749  expected_data.type = statistics::Hist;
750  expected_data.bucket_size = 8;
751  expected_data.min = -16;
752  expected_data.max_val = 16;
753  expected_data.cvec.clear();
754  expected_data.cvec.resize(params.buckets);
755  expected_data.cvec[0] = 4;
756  expected_data.cvec[1] = 0;
757  expected_data.cvec[2] = 5+2+99+12+33;
758  expected_data.cvec[3] = 0;
759  expected_data.cvec[4] = 0;
760 
761  prepareCheckHistStor(params, values, num_values, expected_data);
762 }
763 
769 TEST(StatsHistStorTest, SamplePrepareGrowDownGrowOutEvenBuckets)
770 {
771  statistics::HistStor::Params params(4);
772 
773  // Setup expected data for the hand-carved values given. Since there
774  // is a negative value, the min bucket will change, and the bucket size
775  // will grow to be 8. The final buckets will be divided at:
776  // Bkt0=[-16,-8[ , Bkt1=[-8,0[, Bkt2=[0,8[, Bkt3=[8,16[
777  ValueSamples values[] =
778  {{0, 5}, {1, 2}, {2, 99}, {3, 12}, {-12, 4}};
779  const int num_values = sizeof(values) / sizeof(ValueSamples);
780  statistics::DistData expected_data;
781  expected_data.type = statistics::Hist;
782  expected_data.bucket_size = 8;
783  expected_data.min = -16;
784  expected_data.max_val = 8;
785  expected_data.cvec.clear();
786  expected_data.cvec.resize(params.buckets);
787  expected_data.cvec[0] = 4;
788  expected_data.cvec[1] = 0;
789  expected_data.cvec[2] = 5+2+99+12;
790  expected_data.cvec[3] = 0;
791 
792  prepareCheckHistStor(params, values, num_values, expected_data);
793 }
794 
800 TEST(StatsHistStorTest, SamplePrepareMultipleGrowOddBuckets)
801 {
802  statistics::HistStor::Params params(5);
803 
804  // Setup expected data for the hand-carved values given. This adds quite
805  // a few positive and negative samples, and the bucket size will grow to
806  // be 64. The final buckets will be divided at:
807  // Bkt0=[-128,-64[ , Bkt1=[-64,0[, Bkt2=[0,64[, Bkt3=[64,128[,
808  // Bkt4=[128,192[
809  ValueSamples values[] =
810  {{0, 5}, {7, 2}, {31, 99}, {-8, 12}, {127, 4}, {-120, 53}, {-50, 1}};
811  const int num_values = sizeof(values) / sizeof(ValueSamples);
812  statistics::DistData expected_data;
813  expected_data.type = statistics::Hist;
814  expected_data.bucket_size = 64;
815  expected_data.min = -128;
816  expected_data.max_val = 128;
817  expected_data.cvec.clear();
818  expected_data.cvec.resize(params.buckets);
819  expected_data.cvec[0] = 53;
820  expected_data.cvec[1] = 12+1;
821  expected_data.cvec[2] = 5+2+99;
822  expected_data.cvec[3] = 4;
823  expected_data.cvec[4] = 0;
824 
825  prepareCheckHistStor(params, values, num_values, expected_data);
826 }
827 
833 TEST(StatsHistStorTest, SamplePrepareMultipleGrowEvenBuckets)
834 {
835  statistics::HistStor::Params params(4);
836 
837  // Setup expected data for the hand-carved values given. This adds quite
838  // a few positive and negative samples, and the bucket size will grow to
839  // be 64. The final buckets will be divided at:
840  // Bkt0=[-128,-64[ , Bkt1=[-64,0[, Bkt2=[0,64[, Bkt3=[64,128[
841  ValueSamples values[] =
842  {{0, 5}, {7, 2}, {31, 99}, {-8, 12}, {127, 4}, {-120, 53}, {-50, 1}};
843  const int num_values = sizeof(values) / sizeof(ValueSamples);
844  statistics::DistData expected_data;
845  expected_data.type = statistics::Hist;
846  expected_data.bucket_size = 64;
847  expected_data.min = -128;
848  expected_data.max_val = 64;
849  expected_data.cvec.clear();
850  expected_data.cvec.resize(params.buckets);
851  expected_data.cvec[0] = 53;
852  expected_data.cvec[1] = 12+1;
853  expected_data.cvec[2] = 5+2+99;
854  expected_data.cvec[3] = 4;
855 
856  prepareCheckHistStor(params, values, num_values, expected_data);
857 }
858 
860 TEST(StatsHistStorTest, Reset)
861 {
862  statistics::HistStor::Params params(4);
863  statistics::HistStor stor(&params);
864 
865  // Setup expected data for the hand-carved values given. This adds quite
866  // a few positive and negative samples, and the bucket size will grow to
867  // be 64. The final buckets will be divided at:
868  // Bkt0=[-128,-64[ , Bkt1=[-64,0[, Bkt2=[0,64[, Bkt3=[64,128[
869  ValueSamples values[] =
870  {{0, 5}, {7, 2}, {31, 99}, {-8, 12}, {127, 4}, {-120, 53}, {-50, 1}};
871  const int num_values = sizeof(values) / sizeof(ValueSamples);
872  for (int i = 0; i < num_values; i++) {
873  stor.sample(values[i].value, values[i].numSamples);
874  }
875 
876  // Reset storage, and make sure all data has been cleared:
877  // Bkt0=[0,1[ , Bkt1=[1,2[, Bkt2=[2,3[, Bkt3=[3,4[
878  stor.reset(&params);
879  statistics::DistData expected_data;
880  expected_data.type = statistics::Hist;
881  expected_data.bucket_size = 1;
882  expected_data.min = 0;
883  expected_data.max_val = 3;
884  expected_data.cvec.clear();
885  expected_data.cvec.resize(params.buckets);
886  prepareCheckHistStor(params, values, 0, expected_data);
887 }
888 
889 #if TRACING_ON
891 TEST(StatsHistStorDeathTest, AddDifferentSize)
892 {
893  statistics::HistStor::Params params(4);
894  statistics::HistStor stor(&params);
895 
896  statistics::HistStor::Params params2(5);
897  statistics::HistStor stor2(&params2);
898 
899  ASSERT_DEATH(stor.add(&stor2), ".+");
900 }
901 
903 TEST(StatsHistStorDeathTest, AddDifferentMin)
904 {
905  statistics::HistStor::Params params(4);
906  statistics::HistStor stor(&params);
907  stor.sample(-1, 3);
908 
909  // On creation, the storage's min is zero
910  statistics::HistStor::Params params2(4);
911  statistics::HistStor stor2(&params2);
912 
913  ASSERT_DEATH(stor.add(&stor2), ".+");
914 }
915 #endif
916 
918 TEST(StatsHistStorTest, Add)
919 {
920  statistics::HistStor::Params params(4);
921 
922  // Setup first storage. Buckets are:
923  // Bkt0=[0,16[, Bkt1=[16,32[, Bkt2=[32,48[, Bkt3=[58,64[
924  statistics::HistStor stor(&params);
925  ValueSamples values[] = {{0, 5}, {3, 2}, {20, 37}, {32, 18}};
926  int num_values = sizeof(values) / sizeof(ValueSamples);
927  for (int i = 0; i < num_values; i++) {
928  stor.sample(values[i].value, values[i].numSamples);
929  }
930  statistics::DistData data;
931  stor.prepare(&params, data);
932 
933  // Setup second storage. Buckets are:
934  // Bkt0=[0,32[, Bkt1=[32,64[, Bkt2=[64,96[, Bkt3=[96,128[
935  statistics::HistStor stor2(&params);
936  ValueSamples values2[] = {{10, 10}, {0, 1}, {80, 4}, {17, 100}, {95, 79}};
937  int num_values2 = sizeof(values2) / sizeof(ValueSamples);
938  for (int i = 0; i < num_values2; i++) {
939  stor2.sample(values2[i].value, values2[i].numSamples);
940  }
941  statistics::DistData data2;
942  stor2.prepare(&params, data2);
943 
944  // Perform the merge
945  stor.add(&stor2);
946  statistics::DistData merge_data;
947  stor.prepare(&params, merge_data);
948 
949  // Setup expected data. Buckets are:
950  // Bkt0=[0,32[, Bkt1=[32,64[, Bkt2=[64,96[, Bkt3=[96,128[
951  statistics::DistData expected_data;
952  expected_data.type = statistics::Hist;
953  expected_data.bucket_size = 32;
954  expected_data.min = 0;
955  expected_data.max = 127;
956  expected_data.min_val = 0;
957  expected_data.max_val = 96;
958  expected_data.cvec.clear();
959  expected_data.cvec.resize(params.buckets);
960  expected_data.cvec[0] = 5+2+37+10+1+100;
961  expected_data.cvec[1] = 18;
962  expected_data.cvec[2] = 4+79;
963  expected_data.cvec[3] = 0;
964  expected_data.sum = data.sum + data2.sum;
965  expected_data.squares = data.squares + data2.squares;
966  expected_data.logs = data.squares + data2.logs;
967  expected_data.samples = data.samples + data2.samples;
968 
969  // Compare results
970  checkExpectedDistData(merge_data, expected_data, false);
971 }
972 
978 TEST(StatsSampleStorTest, ZeroReset)
979 {
980  statistics::SampleStor stor(nullptr);
981  statistics::Counter val = 10;
982  statistics::Counter num_samples = 5;
983 
984  ASSERT_TRUE(stor.zero());
985 
986  stor.reset(nullptr);
987  stor.sample(val, num_samples);
988  ASSERT_FALSE(stor.zero());
989 
990  stor.reset(nullptr);
991  ASSERT_TRUE(stor.zero());
992 }
993 
995 TEST(StatsSampleStorTest, SamplePrepare)
996 {
997  statistics::SampleStor stor(nullptr);
998  ValueSamples values[] = {{10, 5}, {1234, 2}, {0xFFFFFFFF, 18}};
999  int num_values = sizeof(values) / sizeof(ValueSamples);
1000  statistics::Counter val;
1001  statistics::DistData data;
1002  statistics::DistData expected_data;
1003  statistics::SampleStor::Params params;
1004 
1005  // Simple test with one value being sampled
1006  stor.sample(values[0].value, values[0].numSamples);
1007  stor.prepare(&params, data);
1008  val = values[0].value * values[0].numSamples;
1009  expected_data.type = statistics::Deviation;
1010  expected_data.sum = val;
1011  expected_data.squares = values[0].value * val;
1012  expected_data.samples = values[0].numSamples;
1013  ASSERT_EQ(data.type, expected_data.type);
1014  ASSERT_EQ(data.sum, expected_data.sum);
1015  ASSERT_EQ(data.squares, expected_data.squares);
1016  ASSERT_EQ(data.samples, expected_data.samples);
1017 
1018  // Reset storage, and make sure all data has been cleared
1019  expected_data.sum = 0;
1020  expected_data.squares = 0;
1021  expected_data.samples = 0;
1022  stor.reset(nullptr);
1023  stor.prepare(&params, data);
1024  ASSERT_EQ(data.type, expected_data.type);
1025  ASSERT_EQ(data.sum, expected_data.sum);
1026  ASSERT_EQ(data.squares, expected_data.squares);
1027  ASSERT_EQ(data.samples, expected_data.samples);
1028 
1029  // Populate storage with more data
1030  for (int i = 0; i < num_values; i++) {
1031  stor.sample(values[i].value, values[i].numSamples);
1032 
1033  val = values[i].value * values[i].numSamples;
1034  expected_data.sum += val;
1035  expected_data.squares += values[i].value * val;
1036  expected_data.samples += values[i].numSamples;
1037  }
1038  stor.prepare(&params, data);
1039  ASSERT_EQ(data.type, expected_data.type);
1040  ASSERT_EQ(data.sum, expected_data.sum);
1041  ASSERT_EQ(data.squares, expected_data.squares);
1042  ASSERT_EQ(data.samples, expected_data.samples);
1043 }
1044 
1046 TEST(StatsSampleStorTest, Size)
1047 {
1048  statistics::SampleStor stor(nullptr);
1049  statistics::Counter val = 10;
1050  statistics::Counter num_samples = 5;
1051  statistics::DistData data;
1052  statistics::SampleStor::Params params;
1053 
1054  ASSERT_EQ(stor.size(), 1);
1055  stor.sample(val, num_samples);
1056  ASSERT_EQ(stor.size(), 1);
1057  stor.prepare(&params, data);
1058  ASSERT_EQ(stor.size(), 1);
1059  stor.reset(nullptr);
1060  ASSERT_EQ(stor.size(), 1);
1061  stor.zero();
1062  ASSERT_EQ(stor.size(), 1);
1063 }
1064 
1070 TEST(StatsAvgSampleStorTest, ZeroReset)
1071 {
1072  statistics::AvgSampleStor stor(nullptr);
1073  statistics::Counter val = 10;
1074  statistics::Counter num_samples = 5;
1075 
1076  ASSERT_TRUE(stor.zero());
1077 
1078  stor.reset(nullptr);
1079  stor.sample(val, num_samples);
1080  ASSERT_FALSE(stor.zero());
1081 
1082  stor.reset(nullptr);
1083  ASSERT_TRUE(stor.zero());
1084 }
1085 
1087 TEST(StatsAvgSampleStorTest, SamplePrepare)
1088 {
1089  statistics::AvgSampleStor stor(nullptr);
1090  ValueSamples values[] = {{10, 5}, {1234, 2}, {0xFFFFFFFF, 18}};
1091  int num_values = sizeof(values) / sizeof(ValueSamples);
1092  statistics::Counter val;
1093  statistics::DistData data;
1094  statistics::DistData expected_data;
1095  statistics::AvgSampleStor::Params params;
1096 
1097  // Simple test with one value being sampled
1098  stor.sample(values[0].value, values[0].numSamples);
1099  stor.prepare(&params, data);
1100  val = values[0].value * values[0].numSamples;
1101  expected_data.type = statistics::Deviation;
1102  expected_data.sum = val;
1103  expected_data.squares = values[0].value * val;
1104  ASSERT_EQ(data.type, expected_data.type);
1105  ASSERT_EQ(data.sum, expected_data.sum);
1106  ASSERT_EQ(data.squares, expected_data.squares);
1107  ASSERT_EQ(data.samples, curTick());
1108 
1109  increaseTick();
1110 
1111  // Reset storage, and make sure all data has been cleared
1112  expected_data.sum = 0;
1113  expected_data.squares = 0;
1114  stor.reset(nullptr);
1115  stor.prepare(&params, data);
1116  ASSERT_EQ(data.type, expected_data.type);
1117  ASSERT_EQ(data.sum, expected_data.sum);
1118  ASSERT_EQ(data.squares, expected_data.squares);
1119  ASSERT_EQ(data.samples, curTick());
1120 
1121  increaseTick();
1122 
1123  // Populate storage with more data
1124  for (int i = 0; i < num_values; i++) {
1125  stor.sample(values[i].value, values[i].numSamples);
1126 
1127  val = values[i].value * values[i].numSamples;
1128  expected_data.sum += val;
1129  expected_data.squares += values[i].value * val;
1130  }
1131  stor.prepare(&params, data);
1132  ASSERT_EQ(data.type, expected_data.type);
1133  ASSERT_EQ(data.sum, expected_data.sum);
1134  ASSERT_EQ(data.squares, expected_data.squares);
1135  ASSERT_EQ(data.samples, curTick());
1136 }
1137 
1139 TEST(StatsAvgSampleStorTest, Size)
1140 {
1141  statistics::AvgSampleStor stor(nullptr);
1142  statistics::Counter val = 10;
1143  statistics::Counter num_samples = 5;
1144  statistics::DistData data;
1145  statistics::AvgSampleStor::Params params;
1146 
1147  ASSERT_EQ(stor.size(), 1);
1148  stor.sample(val, num_samples);
1149  ASSERT_EQ(stor.size(), 1);
1150  stor.prepare(&params, data);
1151  ASSERT_EQ(stor.size(), 1);
1152  stor.reset(nullptr);
1153  ASSERT_EQ(stor.size(), 1);
1154  stor.zero();
1155  ASSERT_EQ(stor.size(), 1);
1156 }
1157 
1163 TEST(StatsSparseHistStorTest, ZeroReset)
1164 {
1165  statistics::SparseHistStor stor(nullptr);
1166  statistics::Counter val = 10;
1167  statistics::Counter num_samples = 5;
1168 
1169  ASSERT_TRUE(stor.zero());
1170 
1171  stor.reset(nullptr);
1172  stor.sample(val, num_samples);
1173  ASSERT_FALSE(stor.zero());
1174 
1175  stor.reset(nullptr);
1176  ASSERT_TRUE(stor.zero());
1177 }
1178 
1180 TEST(StatsSparseHistStorTest, SamplePrepare)
1181 {
1182  statistics::SparseHistStor stor(nullptr);
1183  ValueSamples values[] = {{10, 5}, {1234, 2}, {0xFFFFFFFF, 18}};
1184  int num_values = sizeof(values) / sizeof(ValueSamples);
1185  statistics::Counter total_samples;
1186  statistics::SparseHistData data;
1187 
1188  // Simple test with one value being sampled
1189  stor.sample(values[0].value, values[0].numSamples);
1190  stor.prepare(nullptr, data);
1191  ASSERT_EQ(stor.size(), 1);
1192  ASSERT_EQ(data.cmap.size(), 1);
1193  ASSERT_EQ(data.cmap[values[0].value], values[0].numSamples);
1194  ASSERT_EQ(data.samples, values[0].numSamples);
1195 
1196  // Reset storage, and make sure all data has been cleared
1197  stor.reset(nullptr);
1198  stor.prepare(nullptr, data);
1199  ASSERT_EQ(stor.size(), 0);
1200  ASSERT_EQ(data.cmap.size(), 0);
1201  ASSERT_EQ(data.samples, 0);
1202 
1203  // Populate storage with more data
1204  for (int i = 0; i < num_values; i++) {
1205  stor.sample(values[i].value, values[i].numSamples);
1206  }
1207  stor.prepare(nullptr, data);
1208  total_samples = 0;
1209  ASSERT_EQ(stor.size(), num_values);
1210  ASSERT_EQ(data.cmap.size(), num_values);
1211  for (int i = 0; i < num_values; i++) {
1212  ASSERT_EQ(data.cmap[values[i].value], values[i].numSamples);
1213  total_samples += values[i].numSamples;
1214  }
1215  ASSERT_EQ(data.samples, total_samples);
1216 }
data
const char data[]
Definition: circlebuf.test.cc:48
gem5::GTestTickHandler::setCurTick
void setCurTick(Tick tick)
Assign a value to the current simulation tick.
Definition: cur_tick_fake.hh:46
gem5::statistics::AvgSampleStor
Templatized storage for distribution that calculates per tick mean and variance.
Definition: storage.hh:639
gem5::statistics::AvgSampleStor::zero
bool zero() const
Return true if no samples have been added.
Definition: storage.hh:682
gem5::statistics::AvgSampleStor::reset
void reset(const StorageParams *const storage_params)
Reset stat value to default.
Definition: storage.hh:700
gem5::statistics::AvgSampleStor::sample
void sample(Counter val, int number)
Add a value to the distribution for the given number of times.
Definition: storage.hh:666
gem5::statistics::AvgSampleStor::size
size_type size() const
Return the number of entries, in this case 1.
Definition: storage.hh:676
gem5::statistics::AvgSampleStor::prepare
void prepare(const StorageParams *const storage_params, DistData &data)
Definition: storage.hh:685
gem5::statistics::AvgStor
Templatized storage and interface to a per-tick average stat.
Definition: storage.hh:128
gem5::statistics::AvgStor::value
Counter value() const
Return the current count.
Definition: storage.hh:178
gem5::statistics::AvgStor::reset
void reset(const StorageParams *const storage_params)
Reset stat value to default.
Definition: storage.hh:210
gem5::statistics::AvgStor::inc
void inc(Counter val)
Increment the current count by the provided value, calls set.
Definition: storage.hh:166
gem5::statistics::AvgStor::result
Result result() const
Return the current average.
Definition: storage.hh:185
gem5::statistics::AvgStor::dec
void dec(Counter val)
Deccrement the current count by the provided value, calls set.
Definition: storage.hh:172
gem5::statistics::AvgStor::prepare
void prepare(const StorageParams *const storage_params)
Prepare stat data for dumping or serialization.
Definition: storage.hh:200
gem5::statistics::AvgStor::set
void set(Counter val)
Set the current count to the one provided, update the total and last set values.
Definition: storage.hh:155
gem5::statistics::DistStor
Templatized storage and interface for a distribution stat.
Definition: storage.hh:234
gem5::statistics::DistStor::size
size_type size() const
Return the number of buckets in this distribution.
Definition: storage.hh:305
gem5::statistics::DistStor::prepare
void prepare(const StorageParams *const storage_params, DistData &data)
Definition: storage.hh:318
gem5::statistics::DistStor::sample
void sample(Counter val, int number)
Add a value to the distribution for the given number of times.
Definition: storage.cc:54
gem5::statistics::DistStor::zero
bool zero() const
Returns true if any calls to sample have been made.
Definition: storage.hh:312
gem5::statistics::DistStor::reset
void reset(const StorageParams *const storage_params)
Reset stat value to default.
Definition: storage.hh:346
gem5::statistics::HistStor
Templatized storage and interface for a histogram stat.
Definition: storage.hh:400
gem5::statistics::HistStor::reset
void reset(const StorageParams *const storage_params)
Reset stat value to default.
Definition: storage.hh:538
gem5::statistics::HistStor::add
void add(HistStor *other)
Adds the contents of the given storage to this storage.
Definition: storage.cc:210
gem5::statistics::HistStor::size
size_type size() const
Return the number of buckets in this distribution.
Definition: storage.hh:497
gem5::statistics::HistStor::zero
bool zero() const
Returns true if any calls to sample have been made.
Definition: storage.hh:504
gem5::statistics::HistStor::sample
void sample(Counter val, int number)
Add a value to the distribution for the given number of times.
Definition: storage.cc:177
gem5::statistics::HistStor::prepare
void prepare(const StorageParams *const storage_params, DistData &data)
Definition: storage.hh:510
gem5::statistics::SampleStor
Templatized storage and interface for a distribution that calculates mean and variance.
Definition: storage.hh:561
gem5::statistics::SampleStor::size
size_type size() const
Return the number of entries in this stat, 1.
Definition: storage.hh:602
gem5::statistics::SampleStor::zero
bool zero() const
Return true if no samples have been added.
Definition: storage.hh:608
gem5::statistics::SampleStor::reset
void reset(const StorageParams *const storage_params)
Reset stat value to default.
Definition: storage.hh:626
gem5::statistics::SampleStor::prepare
void prepare(const StorageParams *const storage_params, DistData &data)
Definition: storage.hh:611
gem5::statistics::SampleStor::sample
void sample(Counter val, int number)
Add a value the given number of times to this running average.
Definition: storage.hh:591
gem5::statistics::SparseHistStor
Templatized storage and interface for a sparse histogram stat.
Definition: storage.hh:715
gem5::statistics::SparseHistStor::size
size_type size() const
Return the number of buckets in this distribution.
Definition: storage.hh:750
gem5::statistics::SparseHistStor::reset
void reset(const StorageParams *const storage_params)
Reset stat value to default.
Definition: storage.hh:778
gem5::statistics::SparseHistStor::sample
void sample(Counter val, int number)
Add a value to the distribution for the given number of times.
Definition: storage.hh:740
gem5::statistics::SparseHistStor::prepare
void prepare(const StorageParams *const storage_params, SparseHistData &data)
Definition: storage.hh:763
gem5::statistics::SparseHistStor::zero
bool zero() const
Returns true if any calls to sample have been made.
Definition: storage.hh:757
gem5::statistics::StatStor
Templatized storage and interface for a simple scalar stat.
Definition: storage.hh:58
gem5::statistics::StatStor::prepare
void prepare(const StorageParams *const storage_params)
Prepare stat data for dumping or serialization.
Definition: storage.hh:107
gem5::statistics::StatStor::result
Result result() const
Return the value of this stat as a result type.
Definition: storage.hh:102
gem5::statistics::StatStor::dec
void dec(Counter val)
Decrement the stat by the given value.
Definition: storage.hh:90
gem5::statistics::StatStor::value
Counter value() const
Return the value of this stat as its base type.
Definition: storage.hh:96
gem5::statistics::StatStor::set
void set(Counter val)
The the stat to the given value.
Definition: storage.hh:78
gem5::statistics::StatStor::inc
void inc(Counter val)
Increment the stat by the given value.
Definition: storage.hh:84
gem5::statistics::StatStor::reset
void reset(const StorageParams *const storage_params)
Reset stat value to default.
Definition: storage.hh:112
cur_tick_fake.hh
gem5::ArmISA::i
Bitfield< 7 > i
Definition: misc_types.hh:67
gem5::X86ISA::val
Bitfield< 63 > val
Definition: misc.hh:776
gem5::statistics::size_type
unsigned int size_type
Definition: types.hh:60
gem5::statistics::total
const FlagsType total
Print the total.
Definition: info.hh:60
gem5::statistics::Counter
double Counter
All counters are of 64-bit values.
Definition: types.hh:47
gem5::statistics::Result
double Result
All results are doubles.
Definition: types.hh:56
gem5
Reference material can be found at the JEDEC website: UFS standard http://www.jedec....
Definition: gpu_translation_state.hh:38
gem5::curTick
Tick curTick()
The universal simulation clock.
Definition: cur_tick.hh:46
gem5::Tick
uint64_t Tick
Tick count type.
Definition: types.hh:58
increaseTick
void increaseTick()
Increases the current tick by one.
Definition: storage.test.cc:44
prepareCheckHistStor
void prepareCheckHistStor(statistics::HistStor::Params &params, ValueSamples *values, int num_values, statistics::DistData &expected_data)
Auxiliary function that finishes preparing the HistStor's expected values, perform the calls to the s...
Definition: storage.test.cc:548
TEST
TEST(StatsStatStorTest, SetValueResult)
Test setting and getting a value to the storage.
Definition: storage.test.cc:59
checkExpectedDistData
void checkExpectedDistData(const statistics::DistData &data, const statistics::DistData &expected_data, bool no_log=true)
Compare both dist datas to see if their contents match.
Definition: storage.test.cc:315
tickHandler
GTestTickHandler tickHandler
Definition: storage.test.cc:41
prepareCheckDistStor
void prepareCheckDistStor(statistics::DistStor::Params &params, ValueSamples *values, int num_values, statistics::DistData &expected_data)
Auxiliary function that finishes preparing the DistStor's expected values, perform the calls to the s...
Definition: storage.test.cc:347
ValueSamples
A pair of value and its number of samples, used for sampling.
Definition: storage.test.cc:48
ValueSamples::ValueSamples
ValueSamples(statistics::Counter value, statistics::Counter num_samples)
Definition: storage.test.cc:52
ValueSamples::numSamples
statistics::Counter numSamples
Definition: storage.test.cc:50
ValueSamples::value
statistics::Counter value
Definition: storage.test.cc:49
gem5::statistics::DistData
General container for distribution data.
Definition: types.hh:67
gem5::statistics::DistStor::Params
The parameters for a distribution stat.
Definition: storage.hh:263
gem5::statistics::DistStor::Params::buckets
size_type buckets
The number of buckets.
Definition: storage.hh:271
gem5::statistics::DistStor::Params::max
Counter max
The maximum value to track.
Definition: storage.hh:267
gem5::statistics::DistStor::Params::bucket_size
Counter bucket_size
The number of entries in each bucket.
Definition: storage.hh:269
gem5::statistics::DistStor::Params::min
Counter min
The minimum value to track.
Definition: storage.hh:265
gem5::statistics::HistStor::Params
The parameters for a distribution stat.
Definition: storage.hh:461
gem5::statistics::HistStor::Params::buckets
size_type buckets
The number of buckets.
Definition: storage.hh:463
gem5::statistics::SparseHistData
Data structure of sparse histogram.
Definition: types.hh:86
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