use of org.apache.sysml.runtime.compress.CompressedMatrixBlock in project incubator-systemml by apache.
the class ParTransposeSelfLeftMatrixMultTest method runTransposeSelfMatrixMultTest.
/**
*
* @param mb
*/
private void runTransposeSelfMatrixMultTest(SparsityType sptype, ValueType vtype, boolean compress) {
try {
//prepare sparsity for input data
double sparsity = -1;
switch(sptype) {
case DENSE:
sparsity = sparsity1;
break;
case SPARSE:
sparsity = sparsity2;
break;
case EMPTY:
sparsity = sparsity3;
break;
}
//generate input data
double min = (vtype == ValueType.CONST) ? 10 : -10;
double[][] input = TestUtils.generateTestMatrix(rows, cols, min, 10, sparsity, 7);
if (vtype == ValueType.RAND_ROUND_OLE || vtype == ValueType.RAND_ROUND_DDC) {
CompressedMatrixBlock.ALLOW_DDC_ENCODING = (vtype == ValueType.RAND_ROUND_DDC);
input = TestUtils.round(input);
}
MatrixBlock mb = DataConverter.convertToMatrixBlock(input);
//compress given matrix block
CompressedMatrixBlock cmb = new CompressedMatrixBlock(mb);
if (compress)
cmb.compress();
//matrix-vector uncompressed
int k = InfrastructureAnalyzer.getLocalParallelism();
MatrixBlock ret1 = mb.transposeSelfMatrixMultOperations(new MatrixBlock(), MMTSJType.LEFT, k);
//matrix-vector compressed
MatrixBlock ret2 = cmb.transposeSelfMatrixMultOperations(new MatrixBlock(), MMTSJType.LEFT, k);
//compare result with input
double[][] d1 = DataConverter.convertToDoubleMatrix(ret1);
double[][] d2 = DataConverter.convertToDoubleMatrix(ret2);
TestUtils.compareMatrices(d1, d2, cols, cols, 0.0000001);
} catch (Exception ex) {
throw new RuntimeException(ex);
} finally {
CompressedMatrixBlock.ALLOW_DDC_ENCODING = true;
}
}
use of org.apache.sysml.runtime.compress.CompressedMatrixBlock in project incubator-systemml by apache.
the class ParUnaryAggregateTest method runUnaryAggregateTest.
/**
*
* @param mb
*/
private void runUnaryAggregateTest(SparsityType sptype, ValueType vtype, AggType aggtype, boolean compress) {
try {
//prepare sparsity for input data
double sparsity = -1;
switch(sptype) {
case DENSE:
sparsity = sparsity1;
break;
case SPARSE:
sparsity = sparsity2;
break;
case EMPTY:
sparsity = sparsity3;
break;
}
//generate input data
double min = (vtype == ValueType.CONST) ? 10 : -10;
double[][] input = TestUtils.generateTestMatrix(rows, cols1, min, 10, sparsity, 7);
if (vtype == ValueType.RAND_ROUND_OLE || vtype == ValueType.RAND_ROUND_DDC) {
CompressedMatrixBlock.ALLOW_DDC_ENCODING = (vtype == ValueType.RAND_ROUND_DDC);
input = TestUtils.round(input);
}
MatrixBlock mb = DataConverter.convertToMatrixBlock(input);
//uc group
mb = mb.appendOperations(MatrixBlock.seqOperations(0.1, rows - 0.1, 1), new MatrixBlock());
//prepare unary aggregate operator
AggregateUnaryOperator auop = null;
switch(aggtype) {
case SUM:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uak+");
break;
case ROWSUMS:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uark+");
break;
case COLSUMS:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uack+");
break;
case SUMSQ:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uasqk+");
break;
case ROWSUMSSQ:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uarsqk+");
break;
case COLSUMSSQ:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uacsqk+");
break;
case MAX:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uamax");
break;
case ROWMAXS:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uarmax");
break;
case COLMAXS:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uacmax");
break;
case MIN:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uamin");
break;
case ROWMINS:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uarmin");
break;
case COLMINS:
auop = InstructionUtils.parseBasicAggregateUnaryOperator("uacmin");
break;
}
auop.setNumThreads(InfrastructureAnalyzer.getLocalParallelism());
//compress given matrix block
CompressedMatrixBlock cmb = new CompressedMatrixBlock(mb);
if (compress)
cmb.compress();
//matrix-vector uncompressed
MatrixBlock ret1 = (MatrixBlock) mb.aggregateUnaryOperations(auop, new MatrixBlock(), 1000, 1000, null, true);
//matrix-vector compressed
MatrixBlock ret2 = (MatrixBlock) cmb.aggregateUnaryOperations(auop, new MatrixBlock(), 1000, 1000, null, true);
//compare result with input
double[][] d1 = DataConverter.convertToDoubleMatrix(ret1);
double[][] d2 = DataConverter.convertToDoubleMatrix(ret2);
int dim1 = (aggtype == AggType.ROWSUMS || aggtype == AggType.ROWSUMSSQ || aggtype == AggType.ROWMINS || aggtype == AggType.ROWMINS) ? rows : 1;
int dim2 = (aggtype == AggType.COLSUMS || aggtype == AggType.COLSUMSSQ || aggtype == AggType.COLMAXS || aggtype == AggType.COLMINS) ? cols1 : 1;
TestUtils.compareMatrices(d1, d2, dim1, dim2, 0.00000000001);
} catch (Exception ex) {
throw new RuntimeException(ex);
} finally {
CompressedMatrixBlock.ALLOW_DDC_ENCODING = true;
}
}
use of org.apache.sysml.runtime.compress.CompressedMatrixBlock in project incubator-systemml by apache.
the class ParMatrixMultChainTest method runMatrixMultChainTest.
/**
*
* @param mb
*/
private void runMatrixMultChainTest(SparsityType sptype, ValueType vtype, ChainType ctype, boolean compress) {
try {
//prepare sparsity for input data
double sparsity = -1;
switch(sptype) {
case DENSE:
sparsity = sparsity1;
break;
case SPARSE:
sparsity = sparsity2;
break;
case EMPTY:
sparsity = sparsity3;
break;
}
//generate input data
double min = (vtype == ValueType.CONST) ? 10 : -10;
double[][] input = TestUtils.generateTestMatrix(rows, cols, min, 10, sparsity, 7);
if (vtype == ValueType.RAND_ROUND_OLE || vtype == ValueType.RAND_ROUND_DDC) {
CompressedMatrixBlock.ALLOW_DDC_ENCODING = (vtype == ValueType.RAND_ROUND_DDC);
input = TestUtils.round(input);
}
MatrixBlock mb = DataConverter.convertToMatrixBlock(input);
MatrixBlock vector1 = DataConverter.convertToMatrixBlock(TestUtils.generateTestMatrix(cols, 1, 0, 1, 1.0, 3));
MatrixBlock vector2 = (ctype == ChainType.XtwXv) ? DataConverter.convertToMatrixBlock(TestUtils.generateTestMatrix(rows, 1, 0, 1, 1.0, 3)) : null;
//compress given matrix block
CompressedMatrixBlock cmb = new CompressedMatrixBlock(mb);
if (compress)
cmb.compress();
//matrix-vector uncompressed
int k = InfrastructureAnalyzer.getLocalParallelism();
MatrixBlock ret1 = (MatrixBlock) mb.chainMatrixMultOperations(vector1, vector2, new MatrixBlock(), ctype, k);
//matrix-vector compressed
MatrixBlock ret2 = (MatrixBlock) cmb.chainMatrixMultOperations(vector1, vector2, new MatrixBlock(), ctype, k);
//compare result with input
double[][] d1 = DataConverter.convertToDoubleMatrix(ret1);
double[][] d2 = DataConverter.convertToDoubleMatrix(ret2);
TestUtils.compareMatrices(d1, d2, cols, 1, 0.0000001);
} catch (Exception ex) {
throw new RuntimeException(ex);
} finally {
CompressedMatrixBlock.ALLOW_DDC_ENCODING = true;
}
}
use of org.apache.sysml.runtime.compress.CompressedMatrixBlock in project incubator-systemml by apache.
the class BasicScalarOperationsSparseUnsafeTest method runScalarOperationsTest.
/**
*
* @param mb
*/
private void runScalarOperationsTest(SparsityType sptype, ValueType vtype, boolean compress) {
try {
//prepare sparsity for input data
double sparsity = -1;
switch(sptype) {
case DENSE:
sparsity = sparsity1;
break;
case SPARSE:
sparsity = sparsity2;
break;
case EMPTY:
sparsity = sparsity3;
break;
}
//generate input data
double min = (vtype == ValueType.CONST) ? 10 : -10;
double[][] input = TestUtils.generateTestMatrix(rows, cols, min, 10, sparsity, 7);
if (vtype == ValueType.RAND_ROUND_OLE || vtype == ValueType.RAND_ROUND_DDC) {
CompressedMatrixBlock.ALLOW_DDC_ENCODING = (vtype == ValueType.RAND_ROUND_DDC);
input = TestUtils.round(input);
}
MatrixBlock mb = DataConverter.convertToMatrixBlock(input);
//compress given matrix block
CompressedMatrixBlock cmb = new CompressedMatrixBlock(mb);
if (compress)
cmb.compress();
//matrix-scalar uncompressed
ScalarOperator sop = new RightScalarOperator(Plus.getPlusFnObject(), 7);
MatrixBlock ret1 = (MatrixBlock) mb.scalarOperations(sop, new MatrixBlock());
//matrix-scalar compressed
MatrixBlock ret2 = (MatrixBlock) cmb.scalarOperations(sop, new MatrixBlock());
if (compress)
ret2 = ((CompressedMatrixBlock) ret2).decompress();
//compare result with input
double[][] d1 = DataConverter.convertToDoubleMatrix(ret1);
double[][] d2 = DataConverter.convertToDoubleMatrix(ret2);
TestUtils.compareMatrices(d1, d2, rows, cols, 0.0000001);
} catch (Exception ex) {
throw new RuntimeException(ex);
} finally {
CompressedMatrixBlock.ALLOW_DDC_ENCODING = true;
}
}
use of org.apache.sysml.runtime.compress.CompressedMatrixBlock in project incubator-systemml by apache.
the class BasicTransposeSelfLeftMatrixMultTest method runTransposeSelfMatrixMultTest.
/**
*
* @param mb
*/
private void runTransposeSelfMatrixMultTest(SparsityType sptype, ValueType vtype, boolean compress) {
try {
//prepare sparsity for input data
double sparsity = -1;
switch(sptype) {
case DENSE:
sparsity = sparsity1;
break;
case SPARSE:
sparsity = sparsity2;
break;
case EMPTY:
sparsity = sparsity3;
break;
}
//generate input data
double min = (vtype == ValueType.CONST) ? 10 : -10;
double[][] input = TestUtils.generateTestMatrix(rows, cols, min, 10, sparsity, 7);
if (vtype == ValueType.RAND_ROUND_OLE || vtype == ValueType.RAND_ROUND_DDC) {
CompressedMatrixBlock.ALLOW_DDC_ENCODING = (vtype == ValueType.RAND_ROUND_DDC);
input = TestUtils.round(input);
}
MatrixBlock mb = DataConverter.convertToMatrixBlock(input);
//compress given matrix block
CompressedMatrixBlock cmb = new CompressedMatrixBlock(mb);
if (compress)
cmb.compress();
//matrix-vector uncompressed
MatrixBlock ret1 = mb.transposeSelfMatrixMultOperations(new MatrixBlock(), MMTSJType.LEFT);
//matrix-vector compressed
MatrixBlock ret2 = cmb.transposeSelfMatrixMultOperations(new MatrixBlock(), MMTSJType.LEFT);
//compare result with input
double[][] d1 = DataConverter.convertToDoubleMatrix(ret1);
double[][] d2 = DataConverter.convertToDoubleMatrix(ret2);
TestUtils.compareMatrices(d1, d2, cols, cols, 0.0000001);
} catch (Exception ex) {
throw new RuntimeException(ex);
} finally {
CompressedMatrixBlock.ALLOW_DDC_ENCODING = true;
}
}
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