1 #include "searchtuner.h"
2 #include "tunabledependent.h"
8 HashsetTunableDep initializeTunableDependencies()
10 HashsetTunableDep dep;
11 dep.add(new TunableDependent(MUSTREACHGLOBAL, DECOMPOSEORDER));
12 dep.add(new TunableDependent(MUSTREACHLOCAL, DECOMPOSEORDER));
13 dep.add(new TunableDependent(MUSTREACHPRUNE, DECOMPOSEORDER));
14 dep.add(new TunableDependent(MUSTEDGEPRUNE, DECOMPOSEORDER));
15 dep.add(new TunableDependent(NODEENCODING, ENCODINGGRAPHOPT));
16 dep.add(new TunableDependent(EDGEENCODING, ENCODINGGRAPHOPT));
17 dep.add(new TunableDependent(ELEMENTOPTSETS, ELEMENTOPT));
22 HashsetTunableDep SearchTuner::tunableDependency = initializeTunableDependencies();
24 TunableSetting::TunableSetting(VarType _type, TunableParam _param) :
31 TunableSetting::TunableSetting(VarType _type1, VarType _type2, TunableParam _param) :
38 TunableSetting::TunableSetting(TunableParam _param) :
45 TunableSetting::TunableSetting(TunableSetting *ts) :
50 lowValue(ts->lowValue),
51 highValue(ts->highValue),
52 defaultValue(ts->defaultValue),
53 selectedValue(ts->selectedValue)
57 void TunableSetting::setDecision(int _low, int _high, int _default, int _selection) {
60 defaultValue = _default;
61 selectedValue = _selection;
64 void TunableSetting::print() {
65 model_print("Param %s = %u \t range=[%u,%u]", tunableParameterToString( (Tunables)param), selectedValue, lowValue, highValue);
67 model_print("\tVarType1 %" PRIu64 ", ", type1);
68 model_print("VarType2 %" PRIu64 ", ", type2);
73 unsigned int tunableSettingHash(TunableSetting *setting) {
74 return setting->hasVar ^ setting->type1 ^ setting->type2 ^ setting->param;
77 bool tunableSettingEquals(TunableSetting *setting1, TunableSetting *setting2) {
78 return setting1->hasVar == setting2->hasVar &&
79 setting1->type1 == setting2->type1 &&
80 setting1->type2 == setting2->type2 &&
81 setting1->param == setting2->param;
84 ostream &operator<<(ostream &os, const TunableSetting &ts)
86 os << ts.hasVar << " " << ts.type1 << " " << ts.type2 << " " << ts.param << " " << ts.lowValue << " "
87 << ts.highValue << " " << ts.defaultValue << " " << ts.selectedValue;
92 SearchTuner::SearchTuner() {
95 naiveEncoding = ELEM_UNASSIGNED;
98 myfile.open (TUNEFILE, ios::in);
99 if (myfile.is_open()) {
108 while (myfile >> hasVar >> type1 >> type2 >> param >> lowValue >> highValue >> defaultValue >> selectedValue) {
109 TunableSetting *setting;
112 setting = new TunableSetting(type1, type2, param);
114 setting = new TunableSetting(param);
116 setting->setDecision(lowValue, highValue, defaultValue, selectedValue);
117 usedSettings.add(setting);
123 SearchTuner *SearchTuner::copyUsed() {
124 SearchTuner *tuner = new SearchTuner();
125 SetIteratorTunableSetting *iterator = usedSettings.iterator();
126 while (iterator->hasNext()) {
127 TunableSetting *setting = iterator->next();
128 TunableSetting *copy = new TunableSetting(setting);
129 tuner->settings.add(copy);
132 if(naiveEncoding != ELEM_UNASSIGNED){
133 tuner->graphEncoding = graphEncoding;
134 tuner->naiveEncoding = naiveEncoding;
141 SearchTuner::~SearchTuner() {
142 SetIteratorTunableSetting *iterator = settings.iterator();
143 while (iterator->hasNext()) {
144 TunableSetting *setting = iterator->next();
150 int SearchTuner::getTunable(TunableParam param, TunableDesc *descriptor) {
151 TunableSetting setting(param);
152 TunableSetting *result = usedSettings.get(&setting);
153 if (result == NULL) {
154 result = settings.get(&setting);
155 if ( result == NULL) {
156 result = new TunableSetting(param);
157 uint value = descriptor->lowValue + (random() % (1 + descriptor->highValue - descriptor->lowValue));
158 result->setDecision(descriptor->lowValue, descriptor->highValue, descriptor->defaultValue, value);
159 settings.add(result);
161 usedSettings.add(result);
163 return result->selectedValue;
166 int SearchTuner::getVarTunable(VarType vartype, TunableParam param, TunableDesc *descriptor) {
167 return getVarTunable(vartype, 0, param, descriptor);
170 int SearchTuner::getVarTunable(VarType vartype1, VarType vartype2, TunableParam param, TunableDesc *descriptor) {
171 TunableSetting setting(vartype1, vartype2, param);
172 TunableSetting *result = usedSettings.get(&setting);
173 if (result == NULL) {
174 result = settings.get(&setting);
175 if ( result == NULL) {
177 TunableSetting(vartype1, vartype2, param);
178 uint value = descriptor->lowValue + (random() % (1 + descriptor->highValue - descriptor->lowValue));
179 result->setDecision(descriptor->lowValue, descriptor->highValue, descriptor->defaultValue, value);
180 settings.add(result);
182 usedSettings.add(result);
184 return result->selectedValue;
187 bool SearchTuner::validTunableSetting(TunableSetting* setting){
188 TunableDependent tuneDep((Tunables)setting->param);
190 while(tunableDependency.contains(&tuneDep)){
191 TunableDependent *dependent = tunableDependency.get(&tuneDep);
192 TunableSetting p(dependent->parent);
193 if(!settings.contains(&p)){
194 SetIteratorTunableSetting *iter = settings.iterator();
195 while(iter->hasNext()){
196 model_print("*******************\n");
197 iter->next()->print();
201 ASSERT(settings.contains(&p));
202 TunableSetting *parent = settings.get(&p);
203 if(!(bool)parent->selectedValue){ //Check parent config is already off
206 tuneDep.dependent = dependent->parent;
211 void SearchTuner::randomMutate() {
212 TunableSetting *randomSetting;
214 randomSetting= settings.getRandomElement();
215 }while(!validTunableSetting(randomSetting));
216 int range = randomSetting->highValue - randomSetting->lowValue;
217 int randomchoice = (random() % range) + randomSetting->lowValue;
218 if (randomchoice < randomSetting->selectedValue)
219 randomSetting->selectedValue = randomchoice;
221 randomSetting->selectedValue = randomchoice + 1;
222 model_print("&&&&&&&&Mutating&&&&&&&\n");
223 randomSetting->print();
224 model_print("&&&&&&&&&&&&&&&&&&&&&&&\n");
228 int SearchTuner::nextStaticTuner() {
229 if(naiveEncoding == ELEM_UNASSIGNED){
230 naiveEncoding = ONEHOT;
231 SetIteratorTunableSetting *iter = settings.iterator();
232 while(iter->hasNext()){
233 TunableSetting *setting = iter->next();
234 if (setting->param == NAIVEENCODER){
235 setting->selectedValue = ONEHOT;
236 } else if(setting->param == ENCODINGGRAPHOPT){
237 setting->selectedValue = false;
243 int result=EXIT_FAILURE;
244 if(naiveEncoding == BINARYINDEX && graphEncoding){
245 model_print("Best tuner\n");
247 }else if (naiveEncoding == BINARYINDEX && !graphEncoding){
248 naiveEncoding = ONEHOT;
249 graphEncoding = true;
251 naiveEncoding = (ElementEncodingType)((int)naiveEncoding + 1);
253 SetIteratorTunableSetting *iter = settings.iterator();
255 while(iter->hasNext()){
256 TunableSetting * setting = iter->next();
257 if (setting->param == NAIVEENCODER){
258 setting->selectedValue = naiveEncoding;
260 } else if(setting->param == ENCODINGGRAPHOPT){
261 setting->selectedValue = graphEncoding;
265 model_print("Mutating %u settings\n", count);
271 void SearchTuner::print() {
272 SetIteratorTunableSetting *iterator = settings.iterator();
273 while (iterator->hasNext()) {
274 TunableSetting *setting = iterator->next();
281 void SearchTuner::serialize() {
283 myfile.open (TUNEFILE, ios::out | ios::trunc);
284 SetIteratorTunableSetting *iterator = settings.iterator();
285 while (iterator->hasNext()) {
286 TunableSetting *setting = iterator->next();
287 myfile << *setting << endl;
293 void SearchTuner::printUsed() {
294 SetIteratorTunableSetting *iterator = usedSettings.iterator();
295 while (iterator->hasNext()) {
296 TunableSetting *setting = iterator->next();