void
Master::distributeSynapses(const Mapper& mapper, const network::Generator& net)
{
typedef std::vector<Synapse> svector;
svector input; // dummy
std::vector<svector> output(m_world.size());
unsigned queued = 0;
//! \todo pass this in
const unsigned bufferSize = 2 << 11;
for(network::synapse_iterator s = net.synapse_begin(); s != net.synapse_end(); ++s, ++queued) {
int sourceRank = mapper.rankOf(s->source);
int targetRank = mapper.rankOf(s->target());
output.at(sourceRank).push_back(*s);
if(sourceRank != targetRank) {
output.at(targetRank).push_back(*s);
}
if(queued == bufferSize) {
flushBuffer(SYNAPSE_VECTOR, input, output, m_world);
queued = 0;
}
}
flushBuffer(SYNAPSE_VECTOR, input, output, m_world);
int tag = SYNAPSES_END;
broadcast(m_world, tag, MASTER);
}
ConnectivityMatrix::ConnectivityMatrix(
const network::Generator& net,
const ConfigurationImpl& conf,
const mapper_t& mapper,
const bool verifySources) :
m_mapper(mapper),
m_fractionalBits(conf.fractionalBits()),
m_maxDelay(0),
m_writeOnlySynapses(conf.writeOnlySynapses())
{
if(conf.stdpFunction()) {
m_stdp = StdpProcess(conf.stdpFunction().get(), m_fractionalBits);
}
construction::RCM<nidx_t, RSynapse, 32> m_racc(conf, net, RSynapse(~0U,0));
network::synapse_iterator i = net.synapse_begin();
network::synapse_iterator i_end = net.synapse_end();
for( ; i != i_end; ++i) {
nidx_t source = mapper.localIdx(i->source);
nidx_t target = mapper.localIdx(i->target());
sidx_t sidx = addSynapse(source, target, *i);
m_racc.addSynapse(target, RSynapse(source, i->delay), *i, sidx);
}
//! \todo avoid two passes here
finalizeForward(mapper, verifySources);
m_rcm.reset(new runtime::RCM(m_racc));
}
void
Master::distributeNeurons(const Mapper& mapper, const network::Generator& net)
{
typedef std::vector< std::pair<nidx_t, Neuron<float> > > nvector;
nvector input; // dummy
std::vector<nvector> output(m_world.size());
unsigned queued = 0;
//! \todo pass this in
const unsigned bufferSize = 2 << 11;
for(network::neuron_iterator n = net.neuron_begin(); n != net.neuron_end(); ++n, ++queued) {
output.at(mapper.rankOf(n->first)).push_back(*n);
if(queued == bufferSize) {
flushBuffer(NEURON_VECTOR, input, output, m_world);
queued = 0;
}
}
flushBuffer(NEURON_VECTOR, input, output, m_world);
int tag = NEURONS_END;
broadcast(m_world, tag, MASTER);
}
