StretchAllocatorF_cl *InitVMem(SAllocMod_cl *smod, Mem_Map memmap, Heap_cl *h, MMU_cl *mmu) { StretchAllocatorF_cl *salloc; Mem_VMemDesc allvm[2]; Mem_VMemDesc used[32]; int i; allvm[0].start_addr = 0; allvm[0].npages = VA_SIZE >> PAGE_WIDTH; allvm[0].page_width = PAGE_WIDTH; allvm[0].attr = 0; allvm[1].npages = 0; /* end of array marker */ /* Determine the used vm from the mapping info */ for(i=0; memmap[i].nframes; i++) { used[i].start_addr = memmap[i].vaddr; used[i].npages = memmap[i].nframes; used[i].page_width = memmap[i].frame_width; used[i].attr = 0; } /* now terminate the array */ used[i].npages = 0; salloc = SAllocMod$NewF(smod, h, mmu, allvm, used); return salloc; }
std::vector<Fonc_Num> AllocateurDInconnues::NewVectInc(std::vector<double> & aV) { std::vector<Fonc_Num> aRes; for (int aK=0; aK<int(aV.size()) ; aK++) { aRes.push_back(NewF(&(aV[aK]))); } return aRes; }
StretchAllocatorF_cl *InitVMem(SAllocMod_cl *smod, HeapMod_cl *hmod, Frames_cl *frames, MMU_cl *mmu) { StretchAllocatorF_cl *salloc; Mem_VMemDesc regions[2]; regions[0].start_addr = 0; regions[0].npages = VA_SIZE >> PAGE_WIDTH; regions[0].page_width = PAGE_WIDTH; regions[0].attr = 0; regions[1].npages = 0; /* end of array marker */ salloc = SAllocMod$NewF(smod, hmod, frames, mmu, regions); return salloc; }
int TAGMFast::MLEGradAscentParallel(const double& Thres, const int& MaxIter, const int ChunkNum, const int ChunkSize, const TStr PlotNm, const double StepAlpha, const double StepBeta) { //parallel time_t InitTime = time(NULL); uint64 StartTm = TSecTm::GetCurTm().GetAbsSecs(); TExeTm ExeTm, CheckTm; double PrevL = Likelihood(true); TIntFltPrV IterLV; int PrevIter = 0; int iter = 0; TIntV NIdxV(F.Len(), 0); for (int i = 0; i < F.Len(); i++) { NIdxV.Add(i); } TIntV NIDOPTV(F.Len()); //check if a node needs optimization or not 1: does not require optimization NIDOPTV.PutAll(0); TVec<TIntFltH> NewF(ChunkNum * ChunkSize); TIntV NewNIDV(ChunkNum * ChunkSize); for (iter = 0; iter < MaxIter; iter++) { NIdxV.Clr(false); for (int i = 0; i < F.Len(); i++) { if (NIDOPTV[i] == 0) { NIdxV.Add(i); } } IAssert (NIdxV.Len() <= F.Len()); NIdxV.Shuffle(Rnd); // compute gradient for chunk of nodes #pragma omp parallel for schedule(static, 1) for (int TIdx = 0; TIdx < ChunkNum; TIdx++) { TIntFltH GradV; for (int ui = TIdx * ChunkSize; ui < (TIdx + 1) * ChunkSize; ui++) { NewNIDV[ui] = -1; if (ui > NIdxV.Len()) { continue; } int u = NIdxV[ui]; // //find set of candidate c (we only need to consider c to which a neighbor of u belongs to) TUNGraph::TNodeI UI = G->GetNI(u); TIntSet CIDSet(5 * UI.GetDeg()); TIntFltH CurFU = F[u]; for (int e = 0; e < UI.GetDeg(); e++) { if (HOVIDSV[u].IsKey(UI.GetNbrNId(e))) { continue; } TIntFltH& NbhCIDH = F[UI.GetNbrNId(e)]; for (TIntFltH::TIter CI = NbhCIDH.BegI(); CI < NbhCIDH.EndI(); CI++) { CIDSet.AddKey(CI.GetKey()); } } if (CIDSet.Empty()) { CurFU.Clr(); } else { for (TIntFltH::TIter CI = CurFU.BegI(); CI < CurFU.EndI(); CI++) { //remove the community membership which U does not share with its neighbors if (! CIDSet.IsKey(CI.GetKey())) { CurFU.DelIfKey(CI.GetKey()); } } GradientForRow(u, GradV, CIDSet); if (Norm2(GradV) < 1e-4) { NIDOPTV[u] = 1; continue; } double LearnRate = GetStepSizeByLineSearch(u, GradV, GradV, StepAlpha, StepBeta, 5); if (LearnRate <= 1e-5) { NewNIDV[ui] = -2; continue; } for (int ci = 0; ci < GradV.Len(); ci++) { int CID = GradV.GetKey(ci); double Change = LearnRate * GradV.GetDat(CID); double NewFuc = CurFU.IsKey(CID)? CurFU.GetDat(CID) + Change : Change; if (NewFuc <= 0.0) { CurFU.DelIfKey(CID); } else { CurFU.AddDat(CID) = NewFuc; } } CurFU.Defrag(); } //store changes NewF[ui] = CurFU; NewNIDV[ui] = u; } } int NumNoChangeGrad = 0; int NumNoChangeStepSize = 0; for (int ui = 0; ui < NewNIDV.Len(); ui++) { int NewNID = NewNIDV[ui]; if (NewNID == -1) { NumNoChangeGrad++; continue; } if (NewNID == -2) { NumNoChangeStepSize++; continue; } for (TIntFltH::TIter CI = F[NewNID].BegI(); CI < F[NewNID].EndI(); CI++) { SumFV[CI.GetKey()] -= CI.GetDat(); } } #pragma omp parallel for for (int ui = 0; ui < NewNIDV.Len(); ui++) { int NewNID = NewNIDV[ui]; if (NewNID < 0) { continue; } F[NewNID] = NewF[ui]; } for (int ui = 0; ui < NewNIDV.Len(); ui++) { int NewNID = NewNIDV[ui]; if (NewNID < 0) { continue; } for (TIntFltH::TIter CI = F[NewNID].BegI(); CI < F[NewNID].EndI(); CI++) { SumFV[CI.GetKey()] += CI.GetDat(); } } // update the nodes who are optimal for (int ui = 0; ui < NewNIDV.Len(); ui++) { int NewNID = NewNIDV[ui]; if (NewNID < 0) { continue; } TUNGraph::TNodeI UI = G->GetNI(NewNID); NIDOPTV[NewNID] = 0; for (int e = 0; e < UI.GetDeg(); e++) { NIDOPTV[UI.GetNbrNId(e)] = 0; } } int OPTCnt = 0; for (int i = 0; i < NIDOPTV.Len(); i++) { if (NIDOPTV[i] == 1) { OPTCnt++; } } if (! PlotNm.Empty()) { printf("\r%d iterations [%s] %d secs", iter * ChunkSize * ChunkNum, ExeTm.GetTmStr(), int(TSecTm::GetCurTm().GetAbsSecs() - StartTm)); if (PrevL > TFlt::Mn) { printf(" (%f) %d g %d s %d OPT", PrevL, NumNoChangeGrad, NumNoChangeStepSize, OPTCnt); } fflush(stdout); } if ((iter - PrevIter) * ChunkSize * ChunkNum >= G->GetNodes()) { PrevIter = iter; double CurL = Likelihood(true); IterLV.Add(TIntFltPr(iter * ChunkSize * ChunkNum, CurL)); printf("\r%d iterations, Likelihood: %f, Diff: %f [%d secs]", iter, CurL, CurL - PrevL, int(time(NULL) - InitTime)); fflush(stdout); if (CurL - PrevL <= Thres * fabs(PrevL)) { break; } else { PrevL = CurL; } } } if (! PlotNm.Empty()) { printf("\nMLE completed with %d iterations(%s secs)\n", iter, int(TSecTm::GetCurTm().GetAbsSecs() - StartTm)); TGnuPlot::PlotValV(IterLV, PlotNm + ".likelihood_Q");[] } else {