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More convenient use of specific runtime tuning

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This commit is contained in:
Philippe Tillet
2015-02-08 14:23:38 -05:00
parent 9c68704f09
commit 85fb438806
4 changed files with 27 additions and 38 deletions
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2
bench/blas.cpp
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@@ -96,7 +96,7 @@ void bench(ad::numeric_type dtype)
ad::array x(N, dtype), y(N, dtype);
/* ATIDLAS */
y = x + y; queue.flush(); queue.finish();
BENCHMARK_ATIDLAS(y = ad::controller<atidlas::array_expression>(x + y, ad::execution_options_type(0, &events)), 3*N*dtsize/t)
BENCHMARK_ATIDLAS(y = ad::control(x + y, ad::execution_options_type(0, &events), ad::dispatcher_options_type(true)), 3*N*dtsize/t)
/* clAmdBlas */
#ifdef BENCH_CLAMDBLAS
BENCHMARK_CLAMDBLAS(clAmdBlasSaxpy(N, 1, x.data()(), 0, 1, y.data()(), 0, 1, 1, &queue(), 0, NULL, &event()), 3*N*dtsize/t)

2
include/atidlas/model/model.h
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@@ -29,8 +29,6 @@ namespace atidlas
model(base const &, cl::CommandQueue &);
void execute(controller<expressions_tuple> const &);
void tune(controller<expressions_tuple> const &);
templates_container const & templates() const;
private:
templates_container templates_;

3
include/atidlas/symbolic/expression.h
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@@ -260,7 +260,8 @@ struct execution_options_type
struct dispatcher_options_type
{
dispatcher_options_type(int _label = -1) : label(_label){}
dispatcher_options_type(bool _tune = false, int _label = -1) : tune(_tune), label(_label){}
bool tune;
int label;
};

48
lib/model/model.cpp
View File

@@ -95,36 +95,11 @@ model::model(base const & tp, cl::CommandQueue & queue) : templates_(1,tp.clone(
void model::execute(controller<expressions_tuple> const & expressions)
{
std::vector<cl_ext::lazy_compiler> & compilers = init(expressions);
//Prediction
int label = 0;
if(expressions.dispatcher_options().label>=0)
{
label = expressions.dispatcher_options().label;
}
else
{
std::vector<int_t> x = templates_[0]->input_sizes(expressions.x());
//The user tuned the model specifically for this input size
if(hardcoded_.find(x)!=hardcoded_.end())
label = hardcoded_.at(x);
//The user bypasses the random forest
else if(predictor_.get())
//Specific tuning if requested
if(expressions.dispatcher_options().tune && hardcoded_.find(x)==hardcoded_.end())
{
std::vector<float> predictions = predictor_->predict(x);
label = std::distance(predictions.begin(),std::min_element(predictions.begin(), predictions.end()));
}
}
//Execution
return templates_[label]->enqueue(queue_, compilers, label, expressions);
}
void model::tune(controller<expressions_tuple> const & expressions)
{
std::vector<cl_ext::lazy_compiler> & compilers = init(expressions);
//Collect the timings
std::vector<float> timings(templates_.size());
tools::timer timer;
for(size_t i = 0 ; i < templates_.size() ; ++i)
@@ -134,12 +109,27 @@ void model::tune(controller<expressions_tuple> const & expressions)
queue_.finish();
timings[i] = timer.get();
}
//Fill the override
std::vector<int_t> x = templates_[0]->input_sizes(expressions.x());
hardcoded_[x] = std::distance(timings.begin(),std::min_element(timings.begin(), timings.end()));
}
//Prediction
int label = 0;
if(expressions.dispatcher_options().label>=0)
label = expressions.dispatcher_options().label;
else if(hardcoded_.find(x)!=hardcoded_.end())
label = hardcoded_.at(x);
else if(predictor_.get())
{
std::vector<float> predictions = predictor_->predict(x);
label = std::distance(predictions.begin(),std::min_element(predictions.begin(), predictions.end()));
}
//Execution
return templates_[label]->enqueue(queue_, compilers, label, expressions);
}
model::templates_container const & model::templates() const
{ return templates_; }