353 lines
15 KiB
C++
353 lines
15 KiB
C++
#include <iostream>
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#include "atidlas/backend/templates/reduction.h"
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#include <CL/cl.hpp>
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#include "atidlas/tools/to_string.hpp"
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#include "atidlas/tools/make_map.hpp"
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#include "atidlas/tools/make_vector.hpp"
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namespace atidlas
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{
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reduction_parameters::reduction_parameters(unsigned int _simd_width,
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unsigned int _group_size, unsigned int _num_groups,
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fetching_policy_type _fetching_policy) : base::parameters_type(_simd_width, _group_size, 1, 2), num_groups(_num_groups), fetching_policy(_fetching_policy)
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{ }
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unsigned int reduction::lmem_usage(expressions_tuple const & expressions) const
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{
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unsigned int res = 0;
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for(expressions_tuple::data_type::const_iterator it = expressions.data().begin() ; it != expressions.data().end() ; ++it)
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{
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numeric_type numeric_t= lhs_most((*it)->tree(), (*it)->root()).lhs.dtype;
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res += p_.local_size_0*size_of(numeric_t);
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}
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return res;
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}
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int reduction::check_invalid_impl(cl::Device const &, expressions_tuple const &) const
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{
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if (p_.fetching_policy==FETCH_FROM_LOCAL)
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return TEMPLATE_INVALID_FETCHING_POLICY_TYPE;
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return TEMPLATE_VALID;
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}
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inline void reduction::reduce_1d_local_memory(kernel_generation_stream & stream, unsigned int size, std::vector<mapped_scalar_reduction*> exprs,
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std::string const & buf_str, std::string const & buf_value_str) const
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{
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stream << "#pragma unroll" << std::endl;
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stream << "for(unsigned int stride = " << size/2 << "; stride >0; stride /=2)" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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stream << "barrier(CLK_LOCAL_MEM_FENCE); " << std::endl;
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stream << "if (lid < stride)" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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for (unsigned int k = 0; k < exprs.size(); k++)
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if (exprs[k]->is_index_reduction())
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compute_index_reduction(stream, exprs[k]->process(buf_str+"[lid]"), exprs[k]->process(buf_str+"[lid+stride]")
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, exprs[k]->process(buf_value_str+"[lid]"), exprs[k]->process(buf_value_str+"[lid+stride]"),
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exprs[k]->root_op());
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else
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compute_reduction(stream, exprs[k]->process(buf_str+"[lid]"), exprs[k]->process(buf_str+"[lid+stride]"), exprs[k]->root_op());
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stream.dec_tab();
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stream << "}" << std::endl;
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stream.dec_tab();
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stream << "}" << std::endl;
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}
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std::string reduction::generate_impl(unsigned int label, const char * type, expressions_tuple const & expressions, std::vector<mapping_type> const & mappings, unsigned int simd_width) const
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{
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kernel_generation_stream stream;
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std::vector<mapped_scalar_reduction*> exprs;
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for (std::vector<mapping_type>::const_iterator it = mappings.begin(); it != mappings.end(); ++it)
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for (mapping_type::const_iterator iit = it->begin(); iit != it->end(); ++iit)
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if (mapped_scalar_reduction * p = dynamic_cast<mapped_scalar_reduction*>(iit->second.get()))
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exprs.push_back(p);
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std::size_t N = exprs.size();
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std::string arguments = "unsigned int N, ";
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for (unsigned int k = 0; k < N; ++k)
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{
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std::string numeric_type = numeric_type_to_string(lhs_most(exprs[k]->array_expression().tree(),
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exprs[k]->array_expression().root()).lhs.dtype);
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if (exprs[k]->is_index_reduction())
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{
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arguments += exprs[k]->process("__global unsigned int* #name_temp, ");
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arguments += exprs[k]->process("__global " + tools::to_string(numeric_type) + "* #name_temp_value, ");
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}
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else
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arguments += exprs[k]->process("__global " + tools::to_string(numeric_type) + "* #name_temp, ");
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}
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/* ------------------------
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* First Kernel
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* -----------------------*/
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char kprefix[10];
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fill_kernel_name(kprefix, label, type);
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stream << " __attribute__((reqd_work_group_size(" << p_.local_size_0 << ",1,1)))" << std::endl;
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stream << "__kernel void " << kprefix << "0" << "(" << arguments << generate_arguments("#scalartype", mappings, expressions) << ")" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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stream << "unsigned int lid = get_local_id(0);" << std::endl;
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process(stream, PARENT_NODE_TYPE, tools::make_map<std::map<std::string, std::string> >("array0", "#scalartype #namereg = #pointer[#start];")
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("array1", "#pointer += #start;"), expressions, mappings);
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for (unsigned int k = 0; k < N; ++k)
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{
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if (exprs[k]->is_index_reduction())
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{
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stream << exprs[k]->process("__local #scalartype #name_buf_value[" + tools::to_string(p_.local_size_0) + "];") << std::endl;
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stream << exprs[k]->process("#scalartype #name_acc_value = " + neutral_element(exprs[k]->root_op()) + ";") << std::endl;
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stream << exprs[k]->process("__local unsigned int #name_buf[" + tools::to_string(p_.local_size_0) + "];") << std::endl;
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stream << exprs[k]->process("unsigned int #name_acc = 0;") << std::endl;
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}
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else
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{
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stream << exprs[k]->process("__local #scalartype #name_buf[" + tools::to_string(p_.local_size_0) + "];") << std::endl;
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stream << exprs[k]->process("#scalartype #name_acc = " + neutral_element(exprs[k]->root_op()) + ";") << std::endl;
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}
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}
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class loop_body : public loop_body_base
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{
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public:
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loop_body(std::vector<mapped_scalar_reduction*> const & _exprs) : exprs(_exprs){ }
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void operator()(kernel_generation_stream & stream, unsigned int simd_width) const
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{
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std::string i = (simd_width==1)?"i*#stride":"i";
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//Fetch vector entry
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for (std::vector<mapped_scalar_reduction*>::const_iterator it = exprs.begin(); it != exprs.end(); ++it)
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(*it)->process_recursive(stream, PARENT_NODE_TYPE, tools::make_map<std::map<std::string, std::string> >("array1", append_width("#scalartype",simd_width) + " #namereg = " + vload(simd_width,i,"#pointer")+";")
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("matrix_row", "#scalartype #namereg = #pointer[$OFFSET{#row*#stride, i*#stride2}];")
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("matrix_column", "#scalartype #namereg = #pointer[$OFFSET{i*#stride,#column*#stride2}];")
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("matrix_diag", "#scalartype #namereg = #pointer[#diag_offset<0?$OFFSET{(i - #diag_offset)*#stride, i*#stride2}:$OFFSET{i*#stride, (i + #diag_offset)*#stride2}];"));
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//Update accumulators
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std::vector<std::string> str(simd_width);
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if (simd_width==1)
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str[0] = "#namereg";
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else
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for (unsigned int a = 0; a < simd_width; ++a)
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str[a] = append_simd_suffix("#namereg.s", a);
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for (unsigned int k = 0; k < exprs.size(); ++k)
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{
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for (unsigned int a = 0; a < simd_width; ++a)
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{
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std::map<std::string, std::string> accessors;
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accessors["array1"] = str[a];
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accessors["matrix_row"] = str[a];
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accessors["matrix_column"] = str[a];
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accessors["matrix_diag"] = str[a];
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accessors["array0"] = "#namereg";
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std::string value = exprs[k]->evaluate_recursive(LHS_NODE_TYPE, accessors);
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if (exprs[k]->is_index_reduction())
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compute_index_reduction(stream, exprs[k]->process("#name_acc"), "i*" + tools::to_string(simd_width) + "+"
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+ tools::to_string(a), exprs[k]->process("#name_acc_value"), value,exprs[k]->root_op());
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else
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compute_reduction(stream, exprs[k]->process("#name_acc"), value,exprs[k]->root_op());
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}
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}
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}
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private:
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std::vector<mapped_scalar_reduction*> exprs;
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};
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element_wise_loop_1D(stream, loop_body(exprs), p_.fetching_policy, simd_width, "i", "N", "get_global_id(0)", "get_global_size(0)");
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//Fills local memory
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for (unsigned int k = 0; k < N; ++k)
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{
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if (exprs[k]->is_index_reduction())
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stream << exprs[k]->process("#name_buf_value[lid] = #name_acc_value;") << std::endl;
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stream << exprs[k]->process("#name_buf[lid] = #name_acc;") << std::endl;
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}
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//Reduce local memory
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reduce_1d_local_memory(stream, p_.local_size_0, exprs, "#name_buf", "#name_buf_value");
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//Write to temporary buffers
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stream << "if (lid==0)" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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for (unsigned int k = 0; k < N; ++k)
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{
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if (exprs[k]->is_index_reduction())
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stream << exprs[k]->process("#name_temp_value[get_group_id(0)] = #name_buf_value[0];") << std::endl;
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stream << exprs[k]->process("#name_temp[get_group_id(0)] = #name_buf[0];") << std::endl;
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}
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stream.dec_tab();
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stream << "}" << std::endl;
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stream.dec_tab();
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stream << "}" << std::endl;
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/* ------------------------
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* Second kernel
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* -----------------------*/
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stream << " __attribute__((reqd_work_group_size(" << p_.local_size_0 << ",1,1)))" << std::endl;
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stream << "__kernel void " << kprefix << "1" << "(" << arguments << generate_arguments("#scalartype", mappings, expressions) << ")" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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stream << "unsigned int lid = get_local_id(0);" << std::endl;
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for (unsigned int k = 0; k < N; ++k)
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{
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if (exprs[k]->is_index_reduction())
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{
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stream << exprs[k]->process("__local unsigned int #name_buf[" + tools::to_string(p_.local_size_0) + "];");
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stream << exprs[k]->process("unsigned int #name_acc = 0;") << std::endl;
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stream << exprs[k]->process("__local #scalartype #name_buf_value[" + tools::to_string(p_.local_size_0) + "];") << std::endl;
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stream << exprs[k]->process("#scalartype #name_acc_value = " + neutral_element(exprs[k]->root_op()) + ";");
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}
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else
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{
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stream << exprs[k]->process("__local #scalartype #name_buf[" + tools::to_string(p_.local_size_0) + "];") << std::endl;
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stream << exprs[k]->process("#scalartype #name_acc = " + neutral_element(exprs[k]->root_op()) + ";");
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}
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}
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stream << "for(unsigned int i = lid; i < " << p_.num_groups << "; i += get_local_size(0))" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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for (unsigned int k = 0; k < N; ++k)
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if (exprs[k]->is_index_reduction())
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compute_index_reduction(stream, exprs[k]->process("#name_acc"), exprs[k]->process("#name_temp[i]"),
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exprs[k]->process("#name_acc_value"),exprs[k]->process("#name_temp_value[i]"),exprs[k]->root_op());
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else
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compute_reduction(stream, exprs[k]->process("#name_acc"), exprs[k]->process("#name_temp[i]"), exprs[k]->root_op());
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stream.dec_tab();
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stream << "}" << std::endl;
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for (unsigned int k = 0; k < N; ++k)
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{
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if (exprs[k]->is_index_reduction())
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stream << exprs[k]->process("#name_buf_value[lid] = #name_acc_value;") << std::endl;
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stream << exprs[k]->process("#name_buf[lid] = #name_acc;") << std::endl;
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}
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//Reduce and write final result
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reduce_1d_local_memory(stream, p_.local_size_0, exprs, "#name_buf", "#name_buf_value");
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stream << "if (lid==0)" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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std::map<std::string, std::string> accessors;
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accessors["scalar_reduction"] = "#name_buf[0]";
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accessors["array0"] = "#pointer[#start]";
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evaluate(stream, PARENT_NODE_TYPE, accessors, expressions, mappings);
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stream.dec_tab();
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stream << "}" << std::endl;
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stream.dec_tab();
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stream << "}" << std::endl;
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return stream.str();
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}
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std::vector<std::string> reduction::generate_impl(unsigned int label, expressions_tuple const & expressions, std::vector<mapping_type> const & mappings) const
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{
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std::vector<std::string> result;
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result.push_back(generate_impl(label, "f", expressions, mappings, 1));
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result.push_back(generate_impl(label, "o", expressions, mappings, p_.simd_width));
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return result;
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}
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reduction::reduction(reduction::parameters_type const & parameters,
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binding_policy_t binding) : base_impl<reduction, reduction_parameters>(parameters, binding)
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{ }
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reduction::reduction(unsigned int simd, unsigned int ls, unsigned int ng,
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fetching_policy_type fetch, binding_policy_t bind):
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base_impl<reduction, reduction_parameters>(reduction_parameters(simd,ls,ng,fetch), bind)
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{}
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std::vector<int_t> reduction::input_sizes(expressions_tuple const & expressions)
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{
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std::vector<size_t> reductions_idx = filter_nodes(&is_reduction, *(expressions.data().front()), false);
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int_t N = vector_size(lhs_most(expressions.data().front()->tree(), reductions_idx[0]));
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return tools::make_vector<int_t>() << N;
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}
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void reduction::enqueue(cl::CommandQueue & queue, std::vector<cl_ext::lazy_compiler> & programs,
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unsigned int label, expressions_tuple const & expressions, operation_cache * cache)
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{
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//Preprocessing
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int_t size = input_sizes(expressions)[0];
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std::vector<array_expression::node const *> reductions;
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for (expressions_tuple::data_type::const_iterator it = expressions.data().begin(); it != expressions.data().end(); ++it)
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{
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std::vector<size_t> reductions_idx = filter_nodes(&is_reduction, **it, false);
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for (std::vector<size_t>::iterator itt = reductions_idx.begin(); itt != reductions_idx.end(); ++itt)
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reductions.push_back(&(*it)->tree()[*itt]);
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}
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//Kernel
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char kfallback[2][10];
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fill_kernel_name(kfallback[0], label, "f0");
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fill_kernel_name(kfallback[1], label, "f1");
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char kopt[2][10];
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fill_kernel_name(kopt[0], label, "o0");
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fill_kernel_name(kopt[1], label, "o1");
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bool fallback = p_.simd_width > 1 && (requires_fallback(expressions) || (size%p_.simd_width>0));
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cl::Program & program = programs[fallback?0:1].program();
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cl::Kernel kernels[2] = { cl::Kernel(program, fallback?kfallback[0]:kopt[0]),
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cl::Kernel(program, fallback?kfallback[1]:kopt[1]) };
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//NDRange
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cl::NDRange grange[2] = { cl::NDRange(p_.local_size_0*p_.num_groups), cl::NDRange(p_.local_size_0) };
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cl::NDRange lrange[2] = { cl::NDRange(p_.local_size_0), cl::NDRange(p_.local_size_0) };
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//Arguments
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cl::Context context = expressions.context();
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array_expression const & s = *(expressions.data().front());
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unsigned int dtype_size = size_of(lhs_most(s.tree(), s.root()).lhs.dtype);
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for (unsigned int k = 0; k < 2; k++)
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{
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unsigned int n_arg = 0;
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kernels[k].setArg(n_arg++, cl_uint(size));
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//Temporary buffers
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unsigned int i = 0;
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unsigned int j = 0;
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for (std::vector<array_expression::node const *>::const_iterator it = reductions.begin(); it != reductions.end(); ++it)
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{
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if (is_index_reduction((*it)->op))
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{
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if (tmpidx_.size() <= j)
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tmpidx_.push_back(cl::Buffer(context, CL_MEM_READ_WRITE, p_.num_groups*4));
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kernels[k].setArg(n_arg++, tmpidx_[j]);
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j++;
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}
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if (tmp_.size() <= i)
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tmp_.push_back(cl::Buffer(context, CL_MEM_READ_WRITE, p_.num_groups*dtype_size));
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kernels[k].setArg(n_arg++, tmp_[i]);
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i++;
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}
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set_arguments(expressions, kernels[k], n_arg);
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}
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for (unsigned int k = 0; k < 2; k++)
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queue.enqueueNDRangeKernel(kernels[k], cl::NullRange, grange[k], lrange[k]);
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if(cache)
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for (unsigned int k = 0; k < 2; k++)
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cache->push_back(queue, kernels[k], cl::NullRange, grange[k], lrange[k]);
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}
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template class base_impl<reduction, reduction_parameters>;
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}
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