int test_main(int argc, char *argv[]) {
//////////////////////////////////////////////////////////////////////////
//[> Parameters <]
//FIXME: use random sizes?
const size_t n = 100;
const size_t m = 50;
const size_t n_s = 60;
const size_t m_s = 30;
//////////////////////////////////////////////////////////////////////////
//[> Setup test <]
namespace mpi = boost::mpi;
El::Initialize(argc, argv);
mpi::environment env(argc, argv);
mpi::communicator world;
MPI_Comm mpi_world(world);
El::Grid grid(mpi_world);
const size_t rank = world.rank();
skylark::base::context_t context (0);
double count = 1.0;
const size_t matrix_full = n * m;
mpi_vector_t colsf(matrix_full);
mpi_vector_t rowsf(matrix_full);
mpi_vector_t valsf(matrix_full);
for(size_t i = 0; i < matrix_full; ++i) {
colsf.SetElement(i, i % m);
rowsf.SetElement(i, i / m);
valsf.SetElement(i, count);
count++;
}
DistMatrixType A(n, m, rowsf, colsf, valsf);
mpi_vector_t zero;
count = 1.0;
El::Matrix<double> local_A(n, m);
for( size_t j = 0; j < local_A.Height(); j++ )
for( size_t i = 0; i < local_A.Width(); i++ )
local_A.Set(j, i, count++);
El::DistMatrix<double, El::STAR, El::STAR> result(grid);
// columnwise application
DistMatrixType expected_A;
DistMatrixType pi_sketch(n_s, n, zero, zero, zero);
// rowwise application
DistMatrixType expected_AR;
DistMatrixType pi_sketch_r(m_s, m, zero, zero, zero);
//////////////////////////////////////////////////////////////////////////
//[> Column wise application DistSparseMatrix -> DistMatrix[MC/MR] <]
typedef El::DistMatrix<double> mcmr_target_t;
//[> 1. Create the sketching matrix <]
Dummy_t<DistMatrixType, mcmr_target_t> Sparse(n, n_s, context);
//[> 2. Create space for the sketched matrix <]
mcmr_target_t sketch_A(n_s, m, grid);
El::Zero(sketch_A);
//[> 3. Apply the transform <]
Sparse.apply(A, sketch_A, skylark::sketch::columnwise_tag());
//[> 4. Build structure to compare <]
// easier to check if all processors own result
result = sketch_A;
compute_sketch_matrix(Sparse, A, pi_sketch);
expected_A = Mult_AnXBn_Synch<PTDD, double, col_t>(
pi_sketch, A, false, false);
compare_result(rank, expected_A, result);
//////////////////////////////////////////////////////////////////////////
//[> Column wise application DistSparseMatrix -> DistMatrix[VC/*] <]
typedef El::DistMatrix<double, El::VC, El::STAR> vcs_target_t;
//[> 1. Create the sketching matrix <]
Dummy_t<DistMatrixType, vcs_target_t> SparseVC(n, n_s, context);
//[> 2. Create space for the sketched matrix <]
vcs_target_t sketch_A_vcs(n_s, m, grid);
El::Zero(sketch_A_vcs);
//[> 3. Apply the transform <]
SparseVC.apply(A, sketch_A_vcs, skylark::sketch::columnwise_tag());
//[> 4. Build structure to compare <]
// easier to check if all processors own result
result = sketch_A_vcs;
compute_sketch_matrix(SparseVC, A, pi_sketch);
expected_A = Mult_AnXBn_Synch<PTDD, double, col_t>(
pi_sketch, A, false, false);
compare_result(rank, expected_A, result);
//////////////////////////////////////////////////////////////////////////
//[> Column wise application DistSparseMatrix -> DistMatrix[*/VR] <]
typedef El::DistMatrix<double, El::STAR, El::VR> svr_target_t;
//[> 1. Create the sketching matrix <]
Dummy_t<DistMatrixType, svr_target_t> SparseVR(n, n_s, context);
//[> 2. Create space for the sketched matrix <]
svr_target_t sketch_A_svr(n_s, m, grid);
El::Zero(sketch_A_svr);
//[> 3. Apply the transform <]
SparseVR.apply(A, sketch_A_svr, skylark::sketch::columnwise_tag());
//[> 4. Build structure to compare <]
// easier to check if all processors own result
result = sketch_A_svr;
compute_sketch_matrix(SparseVR, A, pi_sketch);
expected_A = Mult_AnXBn_Synch<PTDD, double, col_t>(
pi_sketch, A, false, false);
compare_result(rank, expected_A, result);
//////////////////////////////////////////////////////////////////////////
//[> Column wise application DistSparseMatrix -> DistMatrix[*/*] <]
typedef El::DistMatrix<double, El::STAR, El::STAR> st_target_t;
//[> 1. Create the sketching matrix <]
Dummy_t<DistMatrixType, st_target_t> SparseST(n, n_s, context);
//[> 2. Create space for the sketched matrix <]
st_target_t sketch_A_st(n_s, m, grid);
El::Zero(sketch_A_st);
//[> 3. Apply the transform <]
SparseST.apply(A, sketch_A_st, skylark::sketch::columnwise_tag());
//[> 4. Compare <]
compute_sketch_matrix(SparseST, A, pi_sketch);
expected_A = Mult_AnXBn_Synch<PTDD, double, col_t>(
pi_sketch, A, false, false);
compare_result(rank, expected_A, sketch_A_st);
//////////////////////////////////////////////////////////////////////////
//[> Column wise application DistSparseMatrix -> LocalDenseMatrix <]
Dummy_t<DistMatrixType, El::Matrix<double>> LocalSparse(n, n_s, context);
El::Matrix<double> local_sketch_A(n_s, m);
El::Zero(local_sketch_A);
LocalSparse.apply(A, local_sketch_A, skylark::sketch::columnwise_tag());
El::Matrix<double> pi_sketch_l(n_s, n);
El::Zero(pi_sketch_l);
El::Matrix<double> expected_A_l(n_s, m);
El::Zero(expected_A_l);
if(rank == 0) {
// PI generated by random number gen
std::vector<size_t> row_idx = LocalSparse.getRowIdx();
std::vector<double> row_val = LocalSparse.getRowValues();
int sketch_size = row_val.size();
typename LocalMatrixType::coords_t coords;
for(int i = 0; i < sketch_size; ++i)
pi_sketch_l.Set(row_idx[i], i, row_val[i]);
El::Gemm(El::NORMAL, El::NORMAL, 1.0, pi_sketch_l, local_A,
0.0, expected_A_l);
for(int col = 0; col < expected_A_l.Width(); col++) {
for(int row = 0; row < expected_A_l.Height(); row++) {
if(local_sketch_A.Get(row, col) !=
expected_A_l.Get(row, col))
BOOST_FAIL("Result of local colwise application not as expected");
}
}
}
//////////////////////////////////////////////////////////////////////////
//[> Row wise application DistSparseMatrix -> DistMatrix[MC/MR] <]
//[> 1. Create the sketching matrix <]
Dummy_t<DistMatrixType, mcmr_target_t> Sparse_r(m, m_s, context);
//[> 2. Create space for the sketched matrix <]
mcmr_target_t sketch_A_r(n, m_s, grid);
El::Zero(sketch_A_r);
//[> 3. Apply the transform <]
Sparse_r.apply(A, sketch_A_r, skylark::sketch::rowwise_tag());
//[> 4. Build structure to compare <]
// easier to check if all processors own result
result = sketch_A_r;
compute_sketch_matrix(Sparse_r, A, pi_sketch_r);
pi_sketch_r.Transpose();
expected_AR = Mult_AnXBn_Synch<PTDD, double, col_t>(
A, pi_sketch_r, false, false);
compare_result(rank, expected_AR, result);
//////////////////////////////////////////////////////////////////////////
//[> Row wise application DistSparseMatrix -> DistMatrix[VC/*] <]
//[> 1. Create the sketching matrix <]
Dummy_t<DistMatrixType, vcs_target_t> Sparse_r_vcs(m, m_s, context);
//[> 2. Create space for the sketched matrix <]
vcs_target_t sketch_A_r_vcs(n, m_s, grid);
El::Zero(sketch_A_r_vcs);
//[> 3. Apply the transform <]
Sparse_r_vcs.apply(A, sketch_A_r_vcs, skylark::sketch::rowwise_tag());
//[> 4. Build structure to compare <]
// easier to check if all processors own result
result = sketch_A_r_vcs;
pi_sketch_r.Transpose();
compute_sketch_matrix(Sparse_r_vcs, A, pi_sketch_r);
pi_sketch_r.Transpose();
expected_AR = Mult_AnXBn_Synch<PTDD, double, col_t>(
A, pi_sketch_r, false, false);
compare_result(rank, expected_AR, result);
//////////////////////////////////////////////////////////////////////////
//[> Row wise application DistSparseMatrix -> LocalDenseMatrix <]
Dummy_t<DistMatrixType, El::Matrix<double>> LocalSparse_r(m, m_s, context);
El::Matrix<double> local_sketch_A_r(n, m_s);
El::Zero(local_sketch_A_r);
LocalSparse_r.apply(A, local_sketch_A_r, skylark::sketch::rowwise_tag());
El::Matrix<double> local_pi_sketch_r(m_s, m);
El::Zero(local_pi_sketch_r);
El::Matrix<double> expected_A_r(n, m_s);
El::Zero(expected_A_r);
if(rank == 0) {
// PI generated by random number gen
std::vector<size_t> row_idx = LocalSparse_r.getRowIdx();
std::vector<double> row_val = LocalSparse_r.getRowValues();
int sketch_size = row_val.size();
typename LocalMatrixType::coords_t coords;
for(int i = 0; i < sketch_size; ++i)
local_pi_sketch_r.Set(row_idx[i], i, row_val[i]);
El::Gemm(El::NORMAL, El::TRANSPOSE, 1.0, local_A, local_pi_sketch_r,
0.0, expected_A_r);
for(int col = 0; col < expected_A_r.Width(); col++) {
for(int row = 0; row < expected_A_r.Height(); row++) {
if(local_sketch_A_r.Get(row, col) !=
expected_A_r.Get(row, col))
BOOST_FAIL("Result of local rowwise application not as expected");
}
}
}
return 0;
}
void translate(boost::exception const& e) {
BOOST_FAIL(boost::diagnostic_information(e));
//TODO remove prefix ut_translate_boost_exception.cpp(7):
//TODO always FAIL but no ERROR REQUIRE etc - better to override what()
}
int test_main(int argc, char *argv[]) {
//////////////////////////////////////////////////////////////////////////
//[> Parameters <]
//FIXME: use random sizes?
const size_t n = 10;
const size_t m = 5;
const size_t n_s = 6;
const size_t m_s = 3;
typedef skylark::base::sparse_matrix_t<double> Matrix_t;
//////////////////////////////////////////////////////////////////////////
//[> Setup test <]
namespace mpi = boost::mpi;
mpi::environment env(argc, argv);
mpi::communicator world;
const size_t rank = world.rank();
skylark::base::context_t context (0);
double count = 1.0;
const int matrix_full = n * m;
std::vector<int> colsf(m + 1);
std::vector<int> rowsf(matrix_full);
std::vector<double> valsf(matrix_full);
for(size_t i = 0; i < m + 1; ++i)
colsf[i] = i * n;
for(size_t i = 0; i < matrix_full; ++i) {
rowsf[i] = i % n;
valsf[i] = count;
count++;
}
Matrix_t A;
A.attach(&colsf[0], &rowsf[0], &valsf[0], matrix_full, n, m, false);
count = 1;
const int* indptr = A.indptr();
const int* indices = A.indices();
const double* values = A.locked_values();
for(int col = 0; col < A.width(); col++) {
for(int idx = indptr[col]; idx < indptr[col + 1]; idx++) {
BOOST_REQUIRE( values[idx] == count );
count++;
}
}
//////////////////////////////////////////////////////////////////////////
//[> Test COO to CSC <]
typename Matrix_t::coords_t coords_mat;
count = 1;
for(int col = 0; col < A.width(); col++) {
for(int row = 0; row < A.height(); row++) {
typename Matrix_t::coord_tuple_t new_entry(row, col, count);
coords_mat.push_back(new_entry);
count++;
}
}
Matrix_t A_coord;
A_coord.set(coords_mat);
count = 1;
indptr = A_coord.indptr();
indices = A_coord.indices();
values = A_coord.locked_values();
for(int col = 0; col < A_coord.width(); col++) {
for(int idx = indptr[col]; idx < indptr[col + 1]; idx++) {
BOOST_REQUIRE( values[idx] == count );
count++;
}
}
//////////////////////////////////////////////////////////////////////////
//[> Column wise application <]
//[> 1. Create the sketching matrix <]
Dummy_t<Matrix_t, Matrix_t> Sparse(n, n_s, context);
std::vector<size_t> row_idx = Sparse.getRowIdx();
std::vector<double> row_val = Sparse.getRowValues();
// PI generated by random number gen
int sketch_size = row_val.size();
typename Matrix_t::coords_t coords;
for(int i = 0; i < sketch_size; ++i) {
typename Matrix_t::coord_tuple_t new_entry(row_idx[i], i, row_val[i]);
coords.push_back(new_entry);
}
Matrix_t pi_sketch;
pi_sketch.set(coords);
//[> 2. Create sketched matrix <]
Matrix_t sketch_A;
//[> 3. Apply the transform <]
Sparse.apply(A, sketch_A, skylark::sketch::columnwise_tag());
BOOST_CHECK(sketch_A.height() == n_s);
BOOST_CHECK(sketch_A.width() == m);
//[> 4. Build structure to compare: PI * A ?= sketch_A <]
typename Matrix_t::coords_t coords_new;
indptr = pi_sketch.indptr();
indices = pi_sketch.indices();
values = pi_sketch.locked_values();
// multiply with vector where an entry has the value:
// col_idx * n + row_idx + 1.
// See creation of A.
for(int col = 0; col < pi_sketch.width(); col++) {
for(int idx = indptr[col]; idx < indptr[col + 1]; idx++) {
for(int ccol = 0; ccol < m; ++ccol) {
typename Matrix_t::coord_tuple_t new_entry(indices[idx], ccol,
values[idx] * (ccol * n + col + 1));
coords_new.push_back(new_entry);
}
}
}
Matrix_t expected_A;
expected_A.set(coords_new, n_s, m);
if (!static_cast<bool>(expected_A == sketch_A))
BOOST_FAIL("Result of colwise application not as expected");
//////////////////////////////////////////////////////////////////////////
//[> Row wise application <]
//[> 1. Create the sketching matrix <]
Dummy_t<Matrix_t, Matrix_t> Sparse_row(m, m_s, context);
row_idx = Sparse_row.getRowIdx();
row_val = Sparse_row.getRowValues();
// PI generated by random number gen
sketch_size = row_val.size();
coords.clear();
for(int i = 0; i < sketch_size; ++i) {
typename Matrix_t::coord_tuple_t new_entry(i, row_idx[i], row_val[i]);
coords.push_back(new_entry);
}
Matrix_t pi_sketch_row;
pi_sketch_row.set(coords);
//[> 2. Create sketched matrix <]
Matrix_t sketch_A_row;
//[> 3. Apply the transform <]
Sparse_row.apply(A, sketch_A_row, skylark::sketch::rowwise_tag());
BOOST_CHECK(sketch_A_row.height() == n);
BOOST_CHECK(sketch_A_row.width() == m_s);
//[> 4. Build structure to compare: A * PI ?= sketch_A <]
coords_new.clear();
indptr = pi_sketch_row.indptr();
indices = pi_sketch_row.indices();
values = pi_sketch_row.locked_values();
// multiply with vector where an entry has the value:
// col_idx * n + row_idx + 1.
// See creation of A.
for(int col = 0; col < pi_sketch_row.width(); col++) {
for(int idx = indptr[col]; idx < indptr[col + 1]; idx++) {
for(int row = 0; row < n; ++row) {
typename Matrix_t::coord_tuple_t new_entry(row, col,
values[idx] * (indices[idx] * n + row + 1));
coords_new.push_back(new_entry);
}
}
}
Matrix_t expected_A_row;
expected_A_row.set(coords_new, n, m_s);
if (!static_cast<bool>(expected_A_row == sketch_A_row))
BOOST_FAIL("Result of rowwise application not as expected");
return 0;
}
int test_main( int, char* [] )
{
try
{
sc::fifo_scheduler<> scheduler;
Check( scheduler, scheduler.create_processor< FifoSchedulerTest >(), 0 );
Check(
scheduler, scheduler.create_processor< FifoSchedulerTest >( 1 ), 1 );
Check(
scheduler,
scheduler.create_processor< FifoSchedulerTest >(
boost::cref( refArg1 ) ),
6 );
Check(
scheduler,
scheduler.create_processor< FifoSchedulerTest >( 1, 2 ),
12 );
Check(
scheduler,
scheduler.create_processor< FifoSchedulerTest >(
boost::cref( refArg1 ), boost::cref( refArg2 ) ),
65 );
Check(
scheduler,
scheduler.create_processor< FifoSchedulerTest >( 1, 2, 3 ),
123 );
Check(
scheduler,
scheduler.create_processor< FifoSchedulerTest >(
boost::cref( refArg1 ), boost::cref( refArg2 ),
boost::cref( refArg3 ) ),
654 );
Check(
scheduler,
scheduler.create_processor< FifoSchedulerTest >( 1, 2, 3, 4 ),
1234 );
Check(
scheduler,
scheduler.create_processor< FifoSchedulerTest >(
boost::cref( refArg1 ), boost::cref( refArg2 ),
boost::cref( refArg3 ), boost::cref( refArg4 ) ),
6543 );
Check(
scheduler,
scheduler.create_processor< FifoSchedulerTest >( 1, 2, 3, 4, 5 ),
12345 );
Check(
scheduler,
scheduler.create_processor< FifoSchedulerTest >(
boost::cref( refArg1 ), boost::cref( refArg2 ),
boost::cref( refArg3 ), boost::cref( refArg4 ),
boost::cref( refArg5 ) ),
65432 );
Check(
scheduler,
scheduler.create_processor< FifoSchedulerTest >( 1, 2, 3, 4, 5, 6 ),
123456 );
Check(
scheduler,
scheduler.create_processor< FifoSchedulerTest >(
boost::cref( refArg1 ), boost::cref( refArg2 ),
boost::cref( refArg3 ), boost::cref( refArg4 ),
boost::cref( refArg5 ), boost::cref( refArg6 ) ),
654321 );
RunScheduler( scheduler, 0UL );
bool workItem1Processed = false;
scheduler.queue_work_item(
boost::bind( &SetToTrue, boost::ref( workItem1Processed ) ) );
RunScheduler( scheduler, 1UL );
BOOST_REQUIRE( workItem1Processed );
scheduler.terminate();
RunScheduler( scheduler, 1UL );
BOOST_REQUIRE( scheduler.terminated() );
RunScheduler( scheduler, 0UL );
bool workItem2Processed = false;
scheduler.queue_work_item(
boost::bind( &SetToTrue, boost::ref( workItem2Processed ) ) );
// After being terminated, a call to operator() must not process any more
// events
RunScheduler( scheduler, 0UL );
BOOST_REQUIRE( !workItem2Processed );
}
catch ( const UnexpectedEventCount & )
{
BOOST_FAIL( "Unexpected event count." );
}
return 0;
}
std::ostream& operator<<( std::ostream& s, const template_streamable< T >& )
{
BOOST_FAIL( "should not have been called" );
return s;
}
void
ClientHandler::check_not_duplicated_event()
{
Json::Value request;
Json::Value response;
Json::Value params;
std::string sessionId;
std::string subscriptionId, subscriptionId2;
std::string receivedId, responseId;
request["jsonrpc"] = "2.0";
request["id"] = getId();
request["method"] = "subscribe";
params["object"] = "manager_ServerManager";
params["type"] = "ObjectCreated";
request["params"] = params;
response = sendRequest (request);
BOOST_CHECK (response.isMember ("result") );
BOOST_CHECK (response["result"].isObject() );
BOOST_CHECK (response["result"].isMember ("sessionId") );
sessionId = response["result"]["sessionId"].asString();
BOOST_CHECK (response["result"].isMember ("value") );
BOOST_CHECK (response["result"].isMember ("value") );
subscriptionId = response["result"]["value"].asString();
BOOST_CHECK (!subscriptionId.empty() );
params["sessionId"] = sessionId;
request["params"] = params;
request["id"] = getId();
response = sendRequest (request);
BOOST_CHECK (response.isMember ("result") );
BOOST_CHECK (response["result"].isObject() );
BOOST_CHECK (response["result"].isMember ("sessionId") );
BOOST_CHECK (sessionId == response["result"]["sessionId"].asString() );
BOOST_CHECK (response["result"].isMember ("value") );
subscriptionId2 = response["result"]["value"].asString();
BOOST_CHECK (!subscriptionId2.empty() );
BOOST_CHECK (subscriptionId != subscriptionId2);
std::thread listener ([this, &receivedId] () {
try {
Json::Value event = this->waifForEvent (std::chrono::seconds (2) );
receivedId = get_id_from_event (event);
} catch (kurento::KurentoException e) {
BOOST_FAIL ("Expected event not received");
}
try {
this->waifForEvent (std::chrono::seconds (2) );
BOOST_FAIL ("Unexpected event");
} catch (kurento::KurentoException e) {
}
});
request.clear();
request["jsonrpc"] = "2.0";
request["id"] = getId();
request["method"] = "create";
params.clear();
params["type"] = "MediaPipeline";
params["sessionId"] = sessionId;
request["params"] = params;
response = sendRequest (request);
BOOST_CHECK (response.isMember ("result") );
BOOST_CHECK (response["result"].isObject() );
BOOST_CHECK (response["result"].isMember ("sessionId") );
BOOST_CHECK (sessionId == response["result"]["sessionId"].asString() );
BOOST_CHECK (response["result"].isMember ("value") );
responseId = response["result"]["value"].asString();
listener.join();
BOOST_CHECK (receivedId == responseId);
// Unsubscribe first listener and wait for event
request.clear();
request["jsonrpc"] = "2.0";
request["id"] = getId();
request["method"] = "unsubscribe";
params.clear();
params["object"] = "manager_ServerManager";
params["subscription"] = subscriptionId;
params["sessionId"] = sessionId;
request["params"] = params;
response = sendRequest (request);
BOOST_CHECK (response.isMember ("result") );
BOOST_CHECK (response["result"].isObject() );
BOOST_CHECK (response["result"].isMember ("sessionId") );
BOOST_CHECK (sessionId == response["result"]["sessionId"].asString() );
std::thread listener2 ([this, &receivedId] () {
try {
Json::Value event = this->waifForEvent (std::chrono::seconds (2) );
receivedId = get_id_from_event (event);
} catch (kurento::KurentoException e) {
BOOST_FAIL ("Expected event not received");
}
try {
this->waifForEvent (std::chrono::seconds (2) );
BOOST_FAIL ("Unexpected event");
} catch (kurento::KurentoException e) {
}
});
request.clear();
request["jsonrpc"] = "2.0";
request["id"] = getId();
request["method"] = "create";
params.clear();
params["type"] = "MediaPipeline";
params["sessionId"] = sessionId;
request["params"] = params;
response = sendRequest (request);
BOOST_CHECK (response.isMember ("result") );
BOOST_CHECK (response["result"].isObject() );
BOOST_CHECK (response["result"].isMember ("sessionId") );
BOOST_CHECK (sessionId == response["result"]["sessionId"].asString() );
BOOST_CHECK (response["result"].isMember ("value") );
BOOST_CHECK (response["result"].isMember ("value") );
responseId = response["result"]["value"].asString();
listener2.join();
BOOST_CHECK (receivedId == responseId);
request.clear();
request["jsonrpc"] = "2.0";
request["id"] = getId();
request["method"] = "unsubscribe";
params.clear();
params["object"] = "manager_ServerManager";
params["subscription"] = subscriptionId2;
params["sessionId"] = sessionId;
request["params"] = params;
response = sendRequest (request);
BOOST_CHECK (response.isMember ("result") );
BOOST_CHECK (response["result"].isObject() );
BOOST_CHECK (response["result"].isMember ("sessionId") );
BOOST_CHECK (sessionId == response["result"]["sessionId"].asString() );
std::thread listener3 ([this] () {
try {
Json::Value event = this->waifForEvent (std::chrono::seconds (2) );
BOOST_ERROR ("Unexpected event: " + event.toStyledString() );
} catch (kurento::KurentoException e) {
}
});
request.clear();
request["jsonrpc"] = "2.0";
request["id"] = getId();
request["method"] = "create";
params.clear();
params["type"] = "MediaPipeline";
params["sessionId"] = sessionId;
request["params"] = params;
response = sendRequest (request);
BOOST_CHECK (response.isMember ("result") );
BOOST_CHECK (response["result"].isObject() );
BOOST_CHECK (response["result"].isMember ("sessionId") );
BOOST_CHECK (sessionId == response["result"]["sessionId"].asString() );
BOOST_CHECK (response["result"].isMember ("value") );
std::cout << response["result"]["value"].toStyledString() << std::endl;
listener3.join();
// Unsubscribe second listener and no event should be received
}
sc::result react( const EvFail & )
{
BOOST_FAIL( "State machine is unexpectedly still running." );
return discard_event();
}
int test_main(int argc, char *argv[]) {
//////////////////////////////////////////////////////////////////////////
//[> Parameters <]
const size_t n = 10;
const size_t m = 5;
const size_t n_s = 6;
const size_t m_s = 3;
const int seed = static_cast<int>(rand() * 100);
typedef FullyDistVec<size_t, double> mpi_vector_t;
typedef SpDCCols<size_t, double> col_t;
typedef SpParMat<size_t, double, col_t> DistMatrixType;
namespace mpi = boost::mpi;
mpi::environment env(argc, argv);
mpi::communicator world;
const size_t rank = world.rank();
skylark::base::context_t context (seed);
double count = 1.0;
const size_t matrix_full = n * m;
mpi_vector_t colsf(matrix_full);
mpi_vector_t rowsf(matrix_full);
mpi_vector_t valsf(matrix_full);
for(size_t i = 0; i < matrix_full; ++i) {
colsf.SetElement(i, i % m);
rowsf.SetElement(i, i / m);
valsf.SetElement(i, count);
count++;
}
DistMatrixType A(n, m, rowsf, colsf, valsf);
//////////////////////////////////////////////////////////////////////////
//[> Setup test <]
//[> 1. Create the sketching matrix and dump JSON <]
skylark::sketch::CWT_t<DistMatrixType, DistMatrixType>
Sparse(n, n_s, context);
// dump to property tree
boost::property_tree::ptree pt = Sparse.get_data()->to_ptree();
//[> 2. Dump the JSON string to file <]
std::ofstream out("sketch.json");
write_json(out, pt);
out.close();
//[> 3. Create a sketch from the JSON file. <]
std::ifstream file;
std::stringstream json;
file.open("sketch.json", std::ios::in);
boost::property_tree::ptree json_tree;
boost::property_tree::read_json(file, json_tree);
skylark::sketch::CWT_t<DistMatrixType, DistMatrixType> tmp(json_tree);
//[> 4. Both sketches should compute the same result. <]
mpi_vector_t zero;
DistMatrixType sketch_A(n_s, m, zero, zero, zero);
DistMatrixType sketch_Atmp(n_s, m, zero, zero, zero);
Sparse.apply(A, sketch_A, skylark::sketch::columnwise_tag());
tmp.apply(A, sketch_Atmp, skylark::sketch::columnwise_tag());
if (!static_cast<bool>(sketch_A == sketch_Atmp))
BOOST_FAIL("Applied sketch did not result in same result");
return 0;
}
static void
unexpectedFailure(uint32_t status, const std::string& reason)
{
BOOST_FAIL("No error expected, but got: [" << status << ": " << reason << "]");
}
void operator()( const CDirEntry & dirEntry ) {
const static size_t kInvalidFileNumber = numeric_limits<size_t>::max();
if( ! dirEntry.IsFile() ) {
return;
}
CFile file(dirEntry);
// skip the README file
if( file.GetName() == "README.txt" ) {
return;
}
// skip .svn files
if( NStr::Find(file.GetPath(), ".svn") != NPOS )
{
return;
}
const string sFilePath = file.GetPath();
cout << "Parsing file name: " << sFilePath << endl;
// extract info from the file name
const string sFileName = file.GetName();
vector<CTempString> vecFileNamePieces;
NStr::Tokenize( sFileName, ".", vecFileNamePieces );
BOOST_REQUIRE( vecFileNamePieces.size() == 2 ||
vecFileNamePieces.size() == 3 );
CTempString tsTestName = vecFileNamePieces[0];
BOOST_REQUIRE( ! tsTestName.empty() );
CTempString tsFileType = vecFileNamePieces[1];
size_t iFileNumber = kInvalidFileNumber;
if( vecFileNamePieces.size() > 2 ) {
iFileNumber = NStr::StringToUInt(vecFileNamePieces[2]);
}
STestInfo & test_info_to_load =
(*m_pTestNameToInfoMap)[vecFileNamePieces[0]];
// figure out what type of file we have and set appropriately
if( tsFileType == "agp" && iFileNumber == kInvalidFileNumber ) {
// handle agp file
// (and make sure we don't have duplicates)
BOOST_REQUIRE( test_info_to_load.m_AGPFile.GetPath().empty() );
test_info_to_load.m_AGPFile = file;
} else if( tsFileType == "expected_seq_entry" && iFileNumber != kInvalidFileNumber )
{
// handle expected seq-entry file
// (Note that the files could come in in any order)
vector<CFile> & vecExpectedSeqEntryFiles =
test_info_to_load.m_vecExpectedSeqEntryFiles;
if( vecExpectedSeqEntryFiles.size() <= iFileNumber ) {
// expand the vector to include this file number
vecExpectedSeqEntryFiles.resize( 1 + iFileNumber );
}
// make sure no duplicates
BOOST_REQUIRE( vecExpectedSeqEntryFiles[iFileNumber].GetPath().empty() );
vecExpectedSeqEntryFiles[iFileNumber] = file;
} else if( tsFileType == "flags" && iFileNumber == kInvalidFileNumber ) {
// handle flags file
BOOST_REQUIRE( test_info_to_load.m_FlagFile.GetPath().empty() );
test_info_to_load.m_FlagFile = file;
} else {
BOOST_FAIL("Unknown file type");
}
}
CassError wait_and_return_error(CassFuture* future, cass_duration_t timeout) {
if (!cass_future_wait_timed(future, timeout)) {
BOOST_FAIL("Timed out waiting for result");
}
return cass_future_error_code(future);
}
int test_main(int argc, char* argv[]) {
/** Initialize Elemental */
El::Initialize (argc, argv);
/** Initialize MPI */
boost::mpi::environment env(argc, argv);
boost::mpi::communicator world;
MPI_Comm mpi_world(world);
El::Grid grid(mpi_world);
/** Example parameters */
int height = 20;
int width = 10;
int sketch_size = 5;
dist_CIRC_CIRC_dense_matrix_t A_CIRC_CIRC(grid);
input_matrix_t A(grid);
El::Uniform(A_CIRC_CIRC, height, width);
A = A_CIRC_CIRC;
int size;
/** Sketch transform rowwise (rw) */
size = width;
skylark::base::context_t context_rw(0);
output_matrix_t sketched_A_rw(height, sketch_size, grid);
sketch_transform_t sketch_transform_rw(size, sketch_size, context_rw);
sketch_transform_rw.apply(A, sketched_A_rw,
skylark::sketch::rowwise_tag());
dist_CIRC_CIRC_dense_matrix_t sketched_A_rw_CIRC_CIRC = sketched_A_rw;
if(world.rank() == 0) {
dense_matrix_t sketched_A_rw_gathered =
sketched_A_rw_CIRC_CIRC.Matrix();
skylark::base::context_t context_rw_local(0);
dense_matrix_t A_rw_local = A_CIRC_CIRC.Matrix();
dense_matrix_t sketched_A_rw_local(height, sketch_size);
sketch_transform_local_t sketch_transform_rw_local(size, sketch_size,
context_rw_local);
sketch_transform_rw_local.apply(A_rw_local, sketched_A_rw_local,
skylark::sketch::rowwise_tag());
if (!test::util::equal(sketched_A_rw_gathered, sketched_A_rw_local))
BOOST_FAIL("Rowwise sketching resuts are not equal");
}
/** Sketch transform columnwise (cw) */
size = height;
skylark::base::context_t context_cw(0);
output_matrix_t sketched_A_cw(sketch_size, width, grid);
sketch_transform_t sketch_transform_cw(size, sketch_size, context_cw);
sketch_transform_cw.apply(A, sketched_A_cw,
skylark::sketch::columnwise_tag());
dist_CIRC_CIRC_dense_matrix_t sketched_A_cw_CIRC_CIRC = sketched_A_cw;
if(world.rank() == 0) {
dense_matrix_t sketched_A_cw_gathered =
sketched_A_cw_CIRC_CIRC.Matrix();
skylark::base::context_t context_cw_local(0);
dense_matrix_t A_cw_local = A_CIRC_CIRC.Matrix();
dense_matrix_t sketched_A_cw_local(sketch_size, width);
sketch_transform_local_t sketch_transform_cw_local(size, sketch_size,
context_cw_local);
sketch_transform_cw_local.apply(A_cw_local, sketched_A_cw_local,
skylark::sketch::columnwise_tag());
if (!test::util::equal(sketched_A_cw_gathered, sketched_A_cw_local))
BOOST_FAIL("Columnwise sketching resuts are not equal");
}
El::Finalize();
return 0;
}
