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Added performance-client for AC routines

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CNugteren 2015-06-23 22:31:27 +02:00
parent 9fc38cdf5e
commit a17297937d
2 changed files with 80 additions and 4 deletions
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81
test/performance/client.cc
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@ -115,7 +115,7 @@ void ClientAXY(int argc, char *argv[], Routine3<T> client_routine,
auto a_two = (args.layout == Layout::kRowMajor) ? args.m : args.n;
// Computes the vector sizes in case the matrix is transposed
auto a_transposed = (args.a_transpose == Transpose::kYes);
auto a_transposed = (args.a_transpose != Transpose::kNo);
auto m_real = (a_transposed) ? args.n : args.m;
auto n_real = (a_transposed) ? args.m : args.n;
@ -163,10 +163,83 @@ template void ClientAXY<double2>(int, char **, Routine3<double2>, const std::vec
// =================================================================================================
// This is the matrix-matrix variant of the set-up/tear-down client routine.
template <typename T>
void ClientAC(int argc, char *argv[], Routine2<T> client_routine,
const std::vector<std::string> &options) {
// Function to determine how to find the default value of the leading dimension of matrix A
auto default_ld_a = [](const Arguments<T> args) { return args.k; };
// Simple command line argument parser with defaults
auto args = ParseArguments<T>(argc, argv, options, default_ld_a);
if (args.print_help) { return; }
// Prints the header of the output table
PrintTableHeader(args.silent, options);
// Initializes OpenCL and the libraries
auto platform = Platform(args.platform_id);
auto device = Device(platform, kDeviceType, args.device_id);
auto context = Context(device);
auto queue = CommandQueue(context, device);
if (args.compare_clblas) { clblasSetup(); }
// Computes whether or not the matrices are transposed. Note that we assume a default of
// column-major and no-transpose. If one of them is different (but not both), then rotated
// is considered true.
auto a_rotated = (args.layout == Layout::kColMajor && args.a_transpose != Transpose::kNo) ||
(args.layout == Layout::kRowMajor && args.a_transpose == Transpose::kNo);
// Iterates over all "num_step" values jumping by "step" each time
auto s = size_t{0};
while(true) {
// Computes the data sizes
auto a_two = (a_rotated) ? args.n : args.k;
auto a_size = a_two * args.a_ld + args.a_offset;
auto c_size = args.n * args.c_ld + args.c_offset;
// Populates input host matrices with random data
std::vector<T> a_source(a_size);
std::vector<T> c_source(c_size);
PopulateVector(a_source);
PopulateVector(c_source);
// Creates the matrices on the device
auto a_buffer = Buffer(context, CL_MEM_READ_WRITE, a_size*sizeof(T));
auto c_buffer = Buffer(context, CL_MEM_READ_WRITE, c_size*sizeof(T));
a_buffer.WriteBuffer(queue, a_size*sizeof(T), a_source);
c_buffer.WriteBuffer(queue, c_size*sizeof(T), c_source);
// Runs the routine-specific code
client_routine(args, a_buffer, c_buffer, queue);
// Makes the jump to the next step
++s;
if (s >= args.num_steps) { break; }
args.n += args.step;
args.k += args.step;
args.a_ld += args.step;
args.c_ld += args.step;
}
// Cleans-up and returns
if (args.compare_clblas) { clblasTeardown(); }
}
// Compiles the above function
template void ClientAC<float>(int, char **, Routine2<float>, const std::vector<std::string>&);
template void ClientAC<double>(int, char **, Routine2<double>, const std::vector<std::string>&);
template void ClientAC<float2>(int, char **, Routine2<float2>, const std::vector<std::string>&);
template void ClientAC<double2>(int, char **, Routine2<double2>, const std::vector<std::string>&);
// =================================================================================================
// This is the matrix-matrix-matrix variant of the set-up/tear-down client routine.
template <typename T>
void ClientABC(int argc, char *argv[], Routine3<T> client_routine,
const std::vector<std::string> &options) {
const std::vector<std::string> &options) {
// Function to determine how to find the default value of the leading dimension of matrix A
auto default_ld_a = [](const Arguments<T> args) { return args.m; };
@ -188,9 +261,9 @@ void ClientABC(int argc, char *argv[], Routine3<T> client_routine,
// Computes whether or not the matrices are transposed. Note that we assume a default of
// column-major and no-transpose. If one of them is different (but not both), then rotated
// is considered true.
auto a_rotated = (args.layout == Layout::kColMajor && args.a_transpose == Transpose::kYes) ||
auto a_rotated = (args.layout == Layout::kColMajor && args.a_transpose != Transpose::kNo) ||
(args.layout == Layout::kRowMajor && args.a_transpose == Transpose::kNo);
auto b_rotated = (args.layout == Layout::kColMajor && args.b_transpose == Transpose::kYes) ||
auto b_rotated = (args.layout == Layout::kColMajor && args.b_transpose != Transpose::kNo) ||
(args.layout == Layout::kRowMajor && args.b_transpose == Transpose::kNo);
auto c_rotated = (args.layout == Layout::kRowMajor);

3
test/performance/client.h
View file

@ -52,6 +52,9 @@ template <typename T>
void ClientAXY(int argc, char *argv[], Routine3<T> client_routine,
const std::vector<std::string> &options);
template <typename T>
void ClientAC(int argc, char *argv[], Routine2<T> client_routine,
const std::vector<std::string> &options);
template <typename T>
void ClientABC(int argc, char *argv[], Routine3<T> client_routine,
const std::vector<std::string> &options);