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mbbeagle.c
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/*
* MrBayes 3
*
* (c) 2002-2013
*
* This file originally contributed by:
*
* Marc A. Suchard
* Department of Biomathematics
* University of California, Los Angeles
* Los Angeles, CA 90095
*
* With important contributions by
*
* Maxim Teslenko ([email protected])
*
* and by many users (run 'acknowledgments' to see more info)
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public License
* as published by the Free Software Foundation; either version 2
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details (www.gnu.org).
*
*/
#include "bayes.h"
#include "mbbeagle.h"
#include "mcmc.h"
#include "model.h"
#include "utils.h"
// #define DEBUG_MB_BEAGLE_MULTIPART
// #define DEBUG_MB_BEAGLE_MULTIPART_SITELNL
#undef BEAGLE_CTIPS_ENABLED /* define to use compact representation tips with BEAGLE3 on GPU */
const char* const svnRevisionMbbeagleC = "$Rev$"; /* Revision keyword which is expended/updated by svn on each commit/update */
/* Functions and variables defined in mcmc.c that are not exported in mcmc.h */
void LaunchLogLikeForDivision(int chain, int d, MrBFlt* lnL);
void FlipCondLikeSpace (ModelInfo *m, int chain, int nodeIndex);
void FlipNodeScalerSpace (ModelInfo *m, int chain, int nodeIndex);
void FlipSiteScalerSpace (ModelInfo *m, int chain);
void FlipTiProbsSpace (ModelInfo *m, int chain, int nodeIndex);
void ResetSiteScalers (ModelInfo *m, int chain);
void CopySiteScalers (ModelInfo *m, int chain);
extern int *chainId;
extern int numLocalChains;
#if defined (BEAGLE_ENABLED)
/*------------------------------------------------------------------------
|
| InitBeagleInstance: create and initialize a beagle instance
|
-------------------------------------------------------------------------*/
int InitBeagleInstance (ModelInfo *m, int division)
{
int i, j, k, c, s, *inStates, numPartAmbigTips;
double *inPartials;
BitsLong *charBits;
if (m->beagleInstance == -99) {
MrBayesPrint ("\n%s Error: Consecutive MCMC runs not currently supported with BEAGLE. Please restart MrBayes.\n\n", spacer);
exit(1);
}
if (m->useBeagle == NO)
return ERROR;
/* at least one eigen buffer needed */
if (m->nCijkParts == 0)
m->nCijkParts = 1;
/* allocate memory used by beagle */
m->logLikelihoods = (MrBFlt *) SafeCalloc ((numLocalChains)*m->numChars, sizeof(MrBFlt));
m->inRates = (MrBFlt *) SafeCalloc (m->numGammaCats, sizeof(MrBFlt));
m->branchLengths = (MrBFlt *) SafeCalloc (2*numLocalTaxa, sizeof(MrBFlt));
m->tiProbIndices = (int *) SafeCalloc (2*numLocalTaxa, sizeof(int));
m->inWeights = (MrBFlt *) SafeCalloc (m->numGammaCats*m->nCijkParts, sizeof(MrBFlt));
m->bufferIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int));
m->eigenIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int));
m->childBufferIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int));
m->childTiProbIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int));
m->cumulativeScaleIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int));
m->operations = (BeagleOperation *) SafeCalloc(m->numCondLikes*m->nCijkParts, sizeof(BeagleOperation));
numPartAmbigTips = 0;
if (m->numStates != m->numModelStates)
numPartAmbigTips = numLocalTaxa;
else
{
for (i=0; i<numLocalTaxa; i++)
{
#if defined(BEAGLE_MULTIPART_ENABLED) && !defined(BEAGLE_CTIPS_ENABLED)
if (m->isPartAmbig[i] == YES || (beagleFlags & BEAGLE_FLAG_PROCESSOR_GPU))
#else
if (m->isPartAmbig[i] == YES)
#endif
{
numPartAmbigTips++;
}
}
}
m->beagleInstance = createBeagleInstance(m, m->nCijkParts, m->numGammaCats, m->numModelStates, m->numCondLikes, m->numScalers, m->numChars, m->numTiProbs, numPartAmbigTips, division);
if (m->beagleInstance < 0)
return ERROR;
/* initialize tip data */
inStates = (int *) SafeMalloc (m->numChars * sizeof(int));
if (!inStates)
return ERROR;
inPartials = (double *) SafeMalloc (m->numChars * m->numModelStates * sizeof(double));
if (!inPartials)
return ERROR;
for (i=0; i<numLocalTaxa; i++)
{
#if defined(BEAGLE_MULTIPART_ENABLED) && !defined(BEAGLE_CTIPS_ENABLED)
if (m->isPartAmbig[i] == NO && !(beagleFlags & BEAGLE_FLAG_PROCESSOR_GPU))
#else
if (m->isPartAmbig[i] == NO)
#endif
{
charBits = m->parsSets[i];
for (c=0; c<m->numChars; c++)
{
for (s=j=0; s<m->numModelStates; s++)
{
if (IsBitSet(s, charBits))
{
inStates[c] = s;
j++;
}
}
if (j == m->numModelStates)
inStates[c] = j;
else
assert (j==1);
charBits += m->nParsIntsPerSite;
}
beagleSetTipStates(m->beagleInstance, i, inStates);
}
else /* if (m->isPartAmbig == YES) */
{
k = 0;
charBits = m->parsSets[i];
for (c=0; c<m->numChars; c++)
{
for (s=0; s<m->numModelStates; s++)
{
if (IsBitSet(s%m->numStates, charBits))
inPartials[k++] = 1.0;
else
inPartials[k++] = 0.0;
}
charBits += m->nParsIntsPerSite;
}
beagleSetTipPartials(m->beagleInstance, i, inPartials);
}
}
free (inStates);
free (inPartials);
return NO_ERROR;
}
int createBeagleInstance(ModelInfo *m, int nCijkParts, int numGammaCats, int numModelStates, int numCondLikes, int numScalers, int numChars, int numTiProbs, int numPartAmbigTips, int division)
{
int resource, resourceCount, beagleInstance;
BeagleInstanceDetails details;
long preferredFlags, requiredFlags;
resourceCount = beagleResourceCount;
preferredFlags = beagleFlags;
requiredFlags = 0L;
if (beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS) {
requiredFlags |= BEAGLE_FLAG_SCALERS_LOG; //BEAGLE_FLAG_SCALERS_RAW;
}
if (beagleResourceNumber >= 0 && beagleResourceNumber != 99)
{
resource = beagleResourceNumber;
resourceCount = 1;
}
else if (beagleResourceCount != 0)
{
# if defined (MPI_ENABLED)
resource = beagleResource[(beagleInstanceCount + proc_id) % beagleResourceCount];
# else
resource = beagleResource[beagleInstanceCount % beagleResourceCount];
# endif
}
#if defined (BEAGLE_MULTIPART_ENABLED)
else if (beagleResourceNumber == 99) {
int numInstancePartitions = 1;
if (division == -1) {
numInstancePartitions = numCurrentDivisions;
}
MrBayesPrint ("\n%s Running benchmarks to automatically select fastest BEAGLE resource... ", spacer);
long benchmarkFlags = BEAGLE_BENCHFLAG_SCALING_NONE;
if (beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS) {
benchmarkFlags = BEAGLE_BENCHFLAG_SCALING_ALWAYS;
} else if (beagleScalingScheme == MB_BEAGLE_SCALE_DYNAMIC) {
benchmarkFlags = BEAGLE_BENCHFLAG_SCALING_DYNAMIC;
}
// select fastest resource
BeagleBenchmarkedResourceList* rBList;
rBList = beagleGetBenchmarkedResourceList(
numLocalTaxa,
numLocalTaxa - numPartAmbigTips,
numModelStates,
numChars,
numGammaCats,
NULL,
0,
preferredFlags,
requiredFlags,
nCijkParts,
numInstancePartitions,
0,
benchmarkFlags);
# if defined (DEBUG_MB_BEAGLE_FLOW)
fprintf(stdout, "Resource benchmarks:\n");
fprintf(stdout, "\ttipCount : %d\n", numLocalTaxa);
fprintf(stdout, "\tcompactBufferCount : %d\n", numLocalTaxa - numPartAmbigTips);
fprintf(stdout, "\tstateCount : %d\n", numModelStates);
fprintf(stdout, "\tpatternCount : %d\n", numChars);
fprintf(stdout, "\tcategoryCount : %d\n", numGammaCats);
fprintf(stdout, "\teigenModelCount : %d\n", nCijkParts);
fprintf(stdout, "\tpartitionCount : %d\n", numInstancePartitions);
fprintf(stdout, "\tPreferred Flags:");
BeaglePrintFlags(preferredFlags);
fprintf(stdout, "\n");
fprintf(stdout, "\tRequired Flags:");
BeaglePrintFlags(requiredFlags);
fprintf(stdout, "\n");
fprintf(stdout, "\tBenchmark Flags: ");
(benchmarkFlags & BEAGLE_BENCHFLAG_SCALING_ALWAYS ? fprintf(stdout, "BEAGLE_BENCHFLAG_SCALING_ALWAYS") : fprintf(stdout, "BEAGLE_BENCHFLAG_SCALING_DYNAMIC"));
fprintf(stdout, "\n");
fprintf(stdout, "\n");
if (rBList != NULL) {
for (int i = 0; i < rBList->length; i++) {
fprintf(stdout, "\tResource %i:\n\t\tName : %s\n", i, rBList->list[i].name);
fprintf(stdout, "\t\tDesc : %s\n", rBList->list[i].description);
fprintf(stdout, "\t\tSupport Flags:");
BeaglePrintFlags(rBList->list[i].supportFlags);
fprintf(stdout, "\n");
fprintf(stdout, "\t\tRequired Flags:");
BeaglePrintFlags(rBList->list[i].requiredFlags);
fprintf(stdout, "\n");
fprintf(stdout, "\t\tBenchmark Results:\n");
fprintf(stdout, "\t\t\tNmbr : %d\n", rBList->list[i].number);
fprintf(stdout, "\t\t\tImpl : %s\n", rBList->list[i].implName);
fprintf(stdout, "\t\t\tFlags:");
BeaglePrintFlags(rBList->list[i].benchedFlags);
fprintf(stdout, "\n");
fprintf(stdout, "\t\t\tPerf : %.4f ms (%.2fx CPU)\n", rBList->list[i].benchmarkResult, rBList->list[i].performanceRatio);
}
}
fprintf(stdout, "\n");
# endif
if (rBList != NULL) {
double fastestTime = rBList->list[0].benchmarkResult;
resource = rBList->list[0].number;
resourceCount = 1;
for (int i = 1; i < rBList->length; i++) {
if (rBList->list[i].benchmarkResult < fastestTime) {
fastestTime = rBList->list[i].benchmarkResult;
resource = rBList->list[i].number;
}
}
}
}
#endif /* BEAGLE_MULTIPART_ENABLED */
/* TODO: allocate fewer buffers when nCijkParts > 1 */
/* create beagle instance */
beagleInstance = beagleCreateInstance(numLocalTaxa,
numCondLikes * nCijkParts,
numLocalTaxa - numPartAmbigTips,
numModelStates,
numChars,
((numLocalChains + 1) * nCijkParts) * numCurrentDivisions,
numTiProbs*nCijkParts,
numGammaCats,
numScalers * nCijkParts,
(resourceCount == 0 ? NULL : &resource),
(resourceCount == 0 ? 0 : 1),
preferredFlags,
requiredFlags,
&details);
if (beagleInstance < 0)
{
MrBayesPrint ("%s Failed to start BEAGLE instance\n", spacer);
}
else
{
if (division < 0)
MrBayesPrint ("\n%s Using BEAGLE v%s resource %i for %d division%s:", spacer, beagleGetVersion(), details.resourceNumber, numCurrentDivisions, (numCurrentDivisions > 1 ? "s" : ""));
else
MrBayesPrint ("\n%s Using BEAGLE v%s resource %i for division %d:", spacer, beagleGetVersion(), details.resourceNumber, division+1);
# if defined (THREADS_ENABLED)
MrBayesPrint (" (%s)\n", (tryToUseThreads ? "threaded" : "non-threaded"));
# else
if (division >= 0)
MrBayesPrint (" (non-threaded)\n");
else
MrBayesPrint ("\n");
# endif
MrBayesPrint ("%s Rsrc Name : %s\n", spacer, details.resourceName);
MrBayesPrint ("%s Impl Name : %s\n", spacer, details.implName);
MrBayesPrint ("%s Flags:", spacer);
BeaglePrintFlags(details.flags);
MrBayesPrint ("%s MODEL STATES: %d", spacer, numModelStates);
MrBayesPrint ("\n");
beagleInstanceCount++;
}
return beagleInstance;
}
/*-----------------------------------------------------------------
|
| LaunchBEAGLELogLikeForDivision: calculate the log likelihood
| of the new state of the chain for a single division
|
-----------------------------------------------------------------*/
void LaunchBEAGLELogLikeForDivision(int chain, int d, ModelInfo* m, Tree* tree, MrBFlt* lnL)
{
int i, rescaleFreqNew;
int *isScalerNode;
TreeNode *p;
if (beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS)
{
# if defined (DEBUG_MB_BEAGLE_FLOW)
MrBayesPrint ("ALWAYS RESCALING\n");
# endif
#if defined (DEBUG_MB_BEAGLE_MULTIPART)
printf("m->upDateAll %d\n", m->upDateAll);
#endif
/* Flip and copy or reset site scalers */
FlipSiteScalerSpace(m, chain);
if (m->upDateAll == YES) {
for (i=0; i<m->nCijkParts; i++) {
beagleResetScaleFactors(m->beagleInstance, m->siteScalerIndex[chain] + i);
}
}
else
CopySiteScalers(m, chain);
TreeTiProbs_Beagle(tree, d, chain);
TreeCondLikes_Beagle(tree, d, chain);
TreeLikelihood_Beagle(tree, d, chain, lnL, (chainId[chain] % chainParams.numChains));
}
else
{ /* MB_BEAGLE_SCALE_DYNAMIC */
/* This flag is only valid within this block */
m->rescaleBeagleAll = NO;
TreeTiProbs_Beagle(tree, d, chain);
if (m->successCount[chain] > 1000)
{
m->successCount[chain] = 10;
m->rescaleFreq[chain]++; /* increase rescaleFreq independent of whether we accept or reject new state */
m->rescaleFreqOld = rescaleFreqNew = m->rescaleFreq[chain];
for (i=0; i<tree->nIntNodes; i++)
{
p = tree->intDownPass[i];
if (p->upDateCl == YES) {
/* flip to the new workspace since TreeCondLikes_Beagle_Rescale_All() does not do it for
(p->upDateCl == YES) since it assumes that TreeCondLikes_Beagle_No_Rescale() did it */
FlipCondLikeSpace (m, chain, p->index);
}
}
goto rescale_all;
}
if (beagleScalingFrequency != 0 &&
m->beagleComputeCount[chain] % (beagleScalingFrequency) == 0)
{
m->rescaleFreqOld = rescaleFreqNew = m->rescaleFreq[chain];
for (i=0; i<tree->nIntNodes; i++)
{
p = tree->intDownPass[i];
if (p->upDateCl == YES) {
/* flip to the new workspace since TreeCondLikes_Beagle_Rescale_All() does not do it for
(p->upDateCl == YES) since it assumes that TreeCondLikes_Beagle_No_Rescale() did it */
FlipCondLikeSpace (m, chain, p->index);
}
}
goto rescale_all;
}
TreeCondLikes_Beagle_No_Rescale(tree, d, chain);
/* Check if likelihood is valid */
if (TreeLikelihood_Beagle(tree, d, chain, lnL, (chainId[chain] % chainParams.numChains)) == BEAGLE_ERROR_FLOATING_POINT)
{
m->rescaleFreqOld = rescaleFreqNew = m->rescaleFreq[chain];
if (rescaleFreqNew > 1 && m->successCount[chain] < 40)
{
if (m->successCount[chain] < 10)
{
if (m->successCount[chain] < 4)
{
rescaleFreqNew -= rescaleFreqNew >> 3; /* <== we cut up to 12,5% of rescaleFreq */
if (m->successCount[chain] < 2)
{
rescaleFreqNew -= rescaleFreqNew >> 3;
/* to avoid situation when we may stack at high rescaleFreq when new states do not get accepted because of low liklihood but there proposed frequency is high we reduce rescaleFreq even if we reject the last move*/
/* basically the higher probability of proposing of low liklihood state which needs smaller rescaleFreq would lead to higher probability of hitting this code which should reduce rescaleFreqOld thus reduce further probability of hitting this code */
/* at some point this negative feedback mechanism should get in balance with the mechanism of periodically increasing rescaleFreq when long sequence of successes is achieved*/
m->rescaleFreqOld -= m->rescaleFreqOld >> 3;
}
m->rescaleFreqOld -= m->rescaleFreqOld >> 3;
m->rescaleFreqOld--;
m->rescaleFreqOld = (m->rescaleFreqOld ? m->rescaleFreqOld:1);
m->recalculateScalers = YES;
recalcScalers = YES;
}
}
rescaleFreqNew--;
rescaleFreqNew = (rescaleFreqNew ? rescaleFreqNew : 1);
}
m->successCount[chain] = 0;
rescale_all:
# if defined (DEBUG_MB_BEAGLE_FLOW)
MrBayesPrint ("NUMERICAL RESCALING\n");
# endif
m->rescaleBeagleAll = YES;
FlipSiteScalerSpace(m, chain);
isScalerNode = m->isScalerNode[chain];
while_loop:
ResetScalersPartition (isScalerNode, tree, rescaleFreqNew);
for (i=0; i<m->nCijkParts; i++)
{
beagleResetScaleFactors(m->beagleInstance, m->siteScalerIndex[chain] + i);
}
TreeCondLikes_Beagle_Rescale_All (tree, d, chain);
if (TreeLikelihood_Beagle(tree, d, chain, lnL, (chainId[chain] % chainParams.numChains)) == BEAGLE_ERROR_FLOATING_POINT)
{
if (rescaleFreqNew > 1)
{
/* Swap back scalers which were swapped in TreeCondLikes_Beagle_Rescale_All() */
for (i=0; i<tree->nIntNodes; i++)
{
p = tree->intDownPass[i];
if (isScalerNode[p->index] == YES)
FlipNodeScalerSpace (m, chain, p->index);
}
rescaleFreqNew -= rescaleFreqNew >> 3; /* <== we cut up to 12,5% of rescaleFreq */
rescaleFreqNew--; /* we cut extra 1 of rescaleFreq */
goto while_loop;
}
}
m->rescaleFreq[chain] = rescaleFreqNew;
}
}
/* Count number of evaluations */
m->beagleComputeCount[chain]++;
}
void recalculateScalers(int chain)
{
int i, d, rescaleFreqNew;
int *isScalerNode;
ModelInfo* m;
Tree *tree;
#if defined (BEAGLE_MULTIPART_ENABLED)
int divisionCount;
int *divisions;
#endif
if (modelSettings[0].useBeagleMultiPartitions == NO)
{
for (d=0; d<numCurrentDivisions; d++)
{
m = &modelSettings[d];
if (m->recalculateScalers == YES)
{
m->recalculateScalers = NO;
tree = GetTree(m->brlens, chain, state[chain]);
rescaleFreqNew = m->rescaleFreq[chain];
isScalerNode = m->isScalerNode[chain];
ResetScalersPartition (isScalerNode, tree, rescaleFreqNew);
for (i=0; i<m->nCijkParts; i++) {
beagleResetScaleFactors(m->beagleInstance, m->siteScalerIndex[chain] + i);
}
/* here it does not matter if we flip CL space or not */
TreeCondLikes_Beagle_Rescale_All (tree, d, chain);
}
}
}
#if defined (BEAGLE_MULTIPART_ENABLED)
else
{
divisions = (int *) SafeCalloc (numCurrentDivisions, sizeof(int));
divisionCount = 0;
for (d=0; d<numCurrentDivisions; d++)
{
m = &modelSettings[d];
if (m->recalculateScalers == YES)
{
m->recalculateScalers = NO;
tree = GetTree(m->brlens, chain, state[chain]);
rescaleFreqNew = m->rescaleFreq[chain];
isScalerNode = m->isScalerNode[chain];
ResetScalersPartition (isScalerNode, tree, rescaleFreqNew);
for (i=0; i<m->nCijkParts; i++) {
beagleResetScaleFactorsByPartition(m->beagleInstance, m->siteScalerIndex[chain] + i, m->divisionIndex);
}
divisions[divisionCount++] = d;
}
}
/* here it does not matter if we flip CL space or not */
TreeCondLikes_BeagleMultiPartition_Rescale_All(divisions, divisionCount, chain);
free(divisions);
}
#endif
}
void BeagleAddGPUDevicesToList(int **newResourceList, int *beagleResourceCount)
{
BeagleResourceList* beagleResources;
int i, gpuCount;
beagleResources = beagleGetResourceList();
if (*newResourceList == NULL) {
*newResourceList = (int*) SafeCalloc(sizeof(int), beagleResources->length);
}
gpuCount = 0;
for (i = 0; i < beagleResources->length; i++) {
if (beagleResources->list[i].supportFlags & BEAGLE_FLAG_PROCESSOR_GPU) {
(*newResourceList)[gpuCount] = i;
gpuCount++;
}
}
*beagleResourceCount = gpuCount;
}
void BeagleRemoveGPUDevicesFromList(int **beagleResource, int *beagleResourceCount)
{
*beagleResourceCount = 0;
}
/*-----
|
| BeaglePrintResources: outputs the available BEAGLE resources
|
----------*/
void BeaglePrintResources()
{
int i;
BeagleResourceList* beagleResources;
beagleResources = beagleGetResourceList();
MrBayesPrint ("%s Available resources reported by beagle library:\n", spacer);
for (i=0; i<beagleResources->length; i++)
{
MrBayesPrint ("\tResource %i:\n", i);
MrBayesPrint ("\tName: %s\n", beagleResources->list[i].name);
if (i > 0)
{
MrBayesPrint ("\tDesc: %s\n", beagleResources->list[i].description);
}
MrBayesPrint ("\tFlags:");
BeaglePrintFlags(beagleResources->list[i].supportFlags);
MrBayesPrint ("\n\n");
}
MrBayesPrint ("%s BEAGLE version: %s\n", spacer, beagleGetVersion());
}
int BeagleCheckFlagCompatability(long inFlags)
{
if (inFlags & BEAGLE_FLAG_PROCESSOR_GPU) {
if (inFlags & BEAGLE_FLAG_VECTOR_SSE) {
MrBayesPrint ("%s Simultaneous use of GPU and SSE not available.\n", spacer);
return NO;
}
if (inFlags & BEAGLE_FLAG_THREADING_OPENMP) {
MrBayesPrint ("%s Simultaneous use of GPU and OpenMP not available.\n", spacer);
return NO;
}
}
return YES;
}
/*-------------------
|
| BeaglePrintFlags: outputs beagle instance details
|
______________________*/
void BeaglePrintFlags(long inFlags)
{
int i, k;
char *names[] = { "PROCESSOR_CPU",
"PROCESSOR_GPU",
"PROCESSOR_FPGA",
"PROCESSOR_CELL",
"PRECISION_DOUBLE",
"PRECISION_SINGLE",
"COMPUTATION_ASYNCH",
"COMPUTATION_SYNCH",
"EIGEN_REAL",
"EIGEN_COMPLEX",
"SCALING_MANUAL",
"SCALING_AUTO",
"SCALING_ALWAYS",
"SCALING_DYNAMIC",
"SCALERS_RAW",
"SCALERS_LOG",
"VECTOR_NONE",
"VECTOR_SSE",
"THREADING_NONE",
#if defined (BEAGLE_MULTIPART_ENABLED)
"THREADING_CPP",
#endif
"THREADING_OPENMP"
};
long flags[] = { BEAGLE_FLAG_PROCESSOR_CPU,
BEAGLE_FLAG_PROCESSOR_GPU,
BEAGLE_FLAG_PROCESSOR_FPGA,
BEAGLE_FLAG_PROCESSOR_CELL,
BEAGLE_FLAG_PRECISION_DOUBLE,
BEAGLE_FLAG_PRECISION_SINGLE,
BEAGLE_FLAG_COMPUTATION_ASYNCH,
BEAGLE_FLAG_COMPUTATION_SYNCH,
BEAGLE_FLAG_EIGEN_REAL,
BEAGLE_FLAG_EIGEN_COMPLEX,
BEAGLE_FLAG_SCALING_MANUAL,
BEAGLE_FLAG_SCALING_AUTO,
BEAGLE_FLAG_SCALING_ALWAYS,
BEAGLE_FLAG_SCALING_DYNAMIC,
BEAGLE_FLAG_SCALERS_RAW,
BEAGLE_FLAG_SCALERS_LOG,
BEAGLE_FLAG_VECTOR_NONE,
BEAGLE_FLAG_VECTOR_SSE,
BEAGLE_FLAG_THREADING_NONE,
#if defined (BEAGLE_MULTIPART_ENABLED)
BEAGLE_FLAG_THREADING_CPP,
#endif
BEAGLE_FLAG_THREADING_OPENMP
};
int flagCount = 20;
#if defined (BEAGLE_MULTIPART_ENABLED)
flagCount = 21;
#endif
for (i=k=0; i<flagCount; i++)
{
if (inFlags & flags[i])
{
if (k%4 == 0 && k > 0)
MrBayesPrint ("\n%s ", spacer);
MrBayesPrint (" %s", names[i]);
k++;
}
}
}
#if defined(THREADS_ENABLED)
void *LaunchThreadLogLikeForDivision(void *arguments)
{
int d, chain;
MrBFlt *lnL;
LaunchStruct* launchStruct;
launchStruct = (LaunchStruct*) arguments;
chain = launchStruct->chain;
d = launchStruct->division;
lnL = launchStruct->lnL;
LaunchLogLikeForDivision(chain, d, lnL);
return 0;
}
MrBFlt LaunchLogLikeForAllDivisionsInParallel(int chain)
{
int d;
int threadError;
pthread_t* threads;
LaunchStruct* launchValues;
int* wait;
ModelInfo* m;
MrBFlt chainLnLike;
chainLnLike = 0.0;
/* TODO Initialize only once */
threads = (pthread_t*) SafeMalloc(sizeof(pthread_t) * numCurrentDivisions);
launchValues = (LaunchStruct*) SafeMalloc(sizeof(LaunchStruct) * numCurrentDivisions);
wait = (int*) SafeMalloc(sizeof(int) * numCurrentDivisions);
/* Cycle through divisions and recalculate tis and cond likes as necessary. */
/* Code below does not try to avoid recalculating ti probs for divisions */
/* that could share ti probs with other divisions. */
for (d=0; d<numCurrentDivisions; d++)
{
# if defined (BEST_MPI_ENABLED)
if (isDivisionActive[d] == NO)
continue;
# endif
m = &modelSettings[d];
if (m->upDateCl == YES)
{
launchValues[d].chain = chain;
launchValues[d].division = d;
launchValues[d].lnL = &(m->lnLike[2*chain + state[chain]]);
/* Fork */
threadError = pthread_create(&threads[d], NULL,
LaunchThreadLogLikeForDivision,
(void*) &launchValues[d]);
assert (0 == threadError);
wait[d] = 1;
}
else
{
wait[d] = 0;
}
}
for (d = 0; d < numCurrentDivisions; d++)
{
/* Join */
if (wait[d])
{
threadError = pthread_join(threads[d], NULL);
assert (0 == threadError);
}
m = &modelSettings[d];
chainLnLike += m->lnLike[2*chain + state[chain]];
}
/* TODO Free these once */
free(threads);
free(launchValues);
free(wait);
return chainLnLike;
}
#endif
int ScheduleLogLikeForAllDivisions()
{
int d;
int divisionsToLaunch = 0;
ModelInfo *m;
if (numCurrentDivisions < 2) {
return 0;
}
for (d=0; d<numCurrentDivisions; d++) {
m = &modelSettings[d];
if (m->upDateCl == YES) {
divisionsToLaunch++;
}
}
return (divisionsToLaunch > 1);
}
/*----------------------------------------------------------------
|
| TreeCondLikes_Beagle: This routine updates all conditional
| (partial) likelihoods of a beagle instance while doing no rescaling.
| That potentialy can make final liklihood bad then calculation with rescaling needs to be done.
|
-----------------------------------------------------------------*/
int TreeCondLikes_Beagle_No_Rescale (Tree *t, int division, int chain)
{
int i, j, cumulativeScaleIndex, op;
TreeNode *p;
ModelInfo *m;
unsigned chil1Step, chil2Step;
int *isScalerNode;
m = &modelSettings[division];
isScalerNode = m->isScalerNode[chain];
op = 0;
for (i=0; i<t->nIntNodes; i++)
{
#if defined (BEAGLE_LEVELPASS_ENABLED)
p = t->intDownPassLevel[i];
#else
p = t->intDownPass[i];
#endif
/* check if conditional likelihoods need updating */
if (p->upDateCl == YES)
{
/* flip to the new workspace */
FlipCondLikeSpace (m, chain, p->index);
/* update conditional likelihoods */
m->operations[op].destinationPartials = m->condLikeIndex[chain][p->index ];
m->operations[op].child1Partials = m->condLikeIndex[chain][p->left->index ];
m->operations[op].child1TransitionMatrix = m->tiProbsIndex [chain][p->left->index ];
m->operations[op].child2Partials = m->condLikeIndex[chain][p->right->index];
m->operations[op].child2TransitionMatrix = m->tiProbsIndex [chain][p->right->index];
/* All partials for tips are the same across omega categories, thus we are doing the following two if statments.*/
if (p->left->left== NULL)
chil1Step=0;
else
chil1Step=1;
if (p->right->left== NULL)
chil2Step=0;
else
chil2Step=1;
m->operations[op].destinationScaleWrite = BEAGLE_OP_NONE;
cumulativeScaleIndex = BEAGLE_OP_NONE;
if (isScalerNode[p->index] == YES)
{
m->operations[op].destinationScaleRead = m->nodeScalerIndex[chain][p->index];
}
else
{
m->operations[op].destinationScaleRead = BEAGLE_OP_NONE;
}
for (j=1; j<m->nCijkParts; j++)
{
m->operations[op+1].destinationPartials = m->operations[op].destinationPartials + 1;
m->operations[op+1].child1Partials = m->operations[op].child1Partials + chil1Step;
m->operations[op+1].child1TransitionMatrix = m->operations[op].child1TransitionMatrix + 1;
m->operations[op+1].child2Partials = m->operations[op].child2Partials + chil2Step;
m->operations[op+1].child2TransitionMatrix = m->operations[op].child2TransitionMatrix + 1;
m->operations[op+1].destinationScaleWrite = BEAGLE_OP_NONE;
if ( isScalerNode[p->index] == YES )
{
m->operations[op+1].destinationScaleRead = m->operations[op].destinationScaleRead + 1;
}
else
{
m->operations[op+1].destinationScaleRead = BEAGLE_OP_NONE;
}
op++;
}
op++;
}
}
beagleUpdatePartials(m->beagleInstance,
m->operations,
op,
cumulativeScaleIndex);
return NO_ERROR;
}
/*----------------------------------------------------------------
|
| TreeCondLikes_Beagle: This routine updates all conditional
| (partial) likelihoods of a beagle instance while rescaling at every node.
| Note: all nodes get recalculated, not only tached by move.
|
-----------------------------------------------------------------*/
int TreeCondLikes_Beagle_Rescale_All (Tree *t, int division, int chain)
{
int i, j, cumulativeScaleIndex, op, opJ, opPrev;
TreeNode *p;
ModelInfo *m;
unsigned chil1Step, chil2Step;
int *isScalerNode;
m = &modelSettings[division];
isScalerNode = m->isScalerNode[chain];
op = 0;
for (i=0; i<t->nIntNodes; i++)
{
#if defined (BEAGLE_LEVELPASS_ENABLED)
p = t->intDownPassLevel[i];
#else
p = t->intDownPass[i];
#endif
if (p->upDateCl == NO)
{
//p->upDateCl = YES;
/* flip to the new workspace */
FlipCondLikeSpace (m, chain, p->index);
}
/* update conditional likelihoods */
m->operations[op].destinationPartials = m->condLikeIndex[chain][p->index ];
m->operations[op].child1Partials = m->condLikeIndex[chain][p->left->index ];
m->operations[op].child1TransitionMatrix = m->tiProbsIndex [chain][p->left->index ];
m->operations[op].child2Partials = m->condLikeIndex[chain][p->right->index];
m->operations[op].child2TransitionMatrix = m->tiProbsIndex [chain][p->right->index];
/* All partials for tips are the same across omega catigoris, thus we are doing the following two if statments.*/
if (p->left->left== NULL)
chil1Step=0;
else
chil1Step=1;
if (p->right->left== NULL)
chil2Step=0;
else
chil2Step=1;
m->operations[op].destinationScaleRead = BEAGLE_OP_NONE;
if (isScalerNode[p->index] == YES)
{
FlipNodeScalerSpace (m, chain, p->index);
m->operations[op].destinationScaleWrite = m->nodeScalerIndex[chain][p->index];
cumulativeScaleIndex = m->siteScalerIndex[chain];
}
else
{
m->operations[op].destinationScaleWrite = BEAGLE_OP_NONE;
cumulativeScaleIndex = BEAGLE_OP_NONE;
}
for (j=1; j<m->nCijkParts; j++)
{
opJ = op + j * t->nIntNodes;
opPrev = op + (j-1) * t->nIntNodes;
m->operations[opJ].destinationPartials = m->operations[opPrev].destinationPartials + 1;
m->operations[opJ].child1Partials = m->operations[opPrev].child1Partials + chil1Step;
m->operations[opJ].child1TransitionMatrix = m->operations[opPrev].child1TransitionMatrix + 1;
m->operations[opJ].child2Partials = m->operations[opPrev].child2Partials + chil2Step;
m->operations[opJ].child2TransitionMatrix = m->operations[opPrev].child2TransitionMatrix + 1;
m->operations[opJ].destinationScaleRead = BEAGLE_OP_NONE;
if ( isScalerNode[p->index] == YES )
{
m->operations[opJ].destinationScaleWrite = m->operations[opPrev].destinationScaleWrite + 1;
}
else
{
m->operations[opJ].destinationScaleWrite = BEAGLE_OP_NONE;
}