46 #define BENCHMARK_VECTOR_SIZE 2
47 #define BENCHMARK_RUNS 1000
50 template<
typename ScalarType>
71 std_vec1[i] = std_vec1[i-1] *
ScalarType(1.000001);
72 std_vec2[i] = std_vec1[i-1] *
ScalarType(0.999999);
80 vcl_vec2 = alpha * vcl_vec1 + beta * vcl_vec2;
85 vcl_vec2 = alpha * vcl_vec1 + beta * vcl_vec2;
88 exec_time = timer.
get();
89 std::cout <<
"Execution time per operation, no scheduler: " << exec_time / BENCHMARK_RUNS <<
" sec" << std::endl;
90 std::cout <<
"Result: " << vcl_vec2[0] << std::endl;
100 exec_time = timer.
get();
101 std::cout <<
"Execution time per operation, with scheduler including statement generation: " << exec_time / BENCHMARK_RUNS <<
" sec" << std::endl;
102 std::cout <<
"Result: " << vcl_vec2[0] << std::endl;
112 exec_time = timer.
get();
113 std::cout <<
"Execution time per operation, only execution: " << exec_time / BENCHMARK_RUNS <<
" sec" << std::endl;
114 std::cout <<
"Result: " << vcl_vec2[0] << std::endl;
121 std::cout << std::endl;
122 std::cout <<
"----------------------------------------------" << std::endl;
123 std::cout <<
" Device Info" << std::endl;
124 std::cout <<
"----------------------------------------------" << std::endl;
126 #ifdef VIENNACL_WITH_OPENCL
130 std::cout << std::endl;
131 std::cout <<
"----------------------------------------------" << std::endl;
132 std::cout <<
"----------------------------------------------" << std::endl;
133 std::cout <<
"## Benchmark :: Vector" << std::endl;
134 std::cout <<
"----------------------------------------------" << std::endl;
135 std::cout << std::endl;
136 std::cout <<
" -------------------------------" << std::endl;
137 std::cout <<
" # benchmarking single-precision" << std::endl;
138 std::cout <<
" -------------------------------" << std::endl;
139 run_benchmark<float>();
140 #ifdef VIENNACL_WITH_OPENCL
144 std::cout << std::endl;
145 std::cout <<
" -------------------------------" << std::endl;
146 std::cout <<
" # benchmarking double-precision" << std::endl;
147 std::cout <<
" -------------------------------" << std::endl;
148 run_benchmark<double>();
Generic interface for the l^2-norm. See viennacl/linalg/vector_operations.hpp for implementations...
Implementation of the dense matrix class.
A tag class representing assignment.
void finish()
Synchronizes the execution. finish() will only return after all compute kernels (CUDA, OpenCL) have completed.
void execute(statement const &s)
#define BENCHMARK_VECTOR_SIZE
viennacl::ocl::device const & current_device()
Convenience function for returning the active device in the current context.
Generic interface for the computation of inner products. See viennacl/linalg/vector_operations.hpp for implementations.
std::string info(vcl_size_t indent=0, char indent_char= ' ') const
Returns an info string with a few properties of the device. Use full_info() to get all details...
bool double_support() const
ViennaCL convenience function: Returns true if the device supports double precision.
A simple, yet (mostly) sufficiently accurate timer for benchmarking and profiling.
The vector type with operator-overloads and proxy classes is defined here. Linear algebra operations ...
void copy(std::vector< NumericT > &cpu_vec, circulant_matrix< NumericT, AlignmentV > &gpu_mat)
Copies a circulant matrix from the std::vector to the OpenCL device (either GPU or multi-core CPU) ...
Provides the datastructures for dealing with a single statement such as 'x = y + z;'.
The main class for representing a statement such as x = inner_prod(y,z); at runtime.
Implementation of the ViennaCL scalar class.
void fast_copy(const const_vector_iterator< SCALARTYPE, ALIGNMENT > &gpu_begin, const const_vector_iterator< SCALARTYPE, ALIGNMENT > &gpu_end, CPU_ITERATOR cpu_begin)