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[MINOR] Additional single-/multi-threaded tests for matmult kernels
This patch adds dedicated components tests for all matmult kernels (different kernels for dense-dense, dense-sparse, sparse-dense, sparse-sparse) and checks both single- and multi-threaded implementations because SYSTEMDS-3769 recently shown that some of these kernels invalid parallelization strategies. Interestingly, these tests revealed two additional bugs for (1) dense-dense outer products, and (2) sparse-dense inner products. Furthermore, we also fix the missing license introduced with a recently merged PR.
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‎scripts/perftest/resource/test_ops.dml‎

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#-------------------------------------------------------------
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#
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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#
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#-------------------------------------------------------------
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X = read($X);
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Y = read($Y);

‎src/main/java/org/apache/sysds/runtime/matrix/data/LibMatrixMult.java‎

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{
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//internal configuration
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private static final long MEM_OVERHEAD_THRESHOLD = 2L*1024*1024; //MAX 2 MB
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private static final long PAR_MINFLOP_THRESHOLD1 = 2L*1024*1024; //MIN 2 MFLOP
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public static final long PAR_MINFLOP_THRESHOLD1 = 2L*1024*1024; //MIN 2 MFLOP
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private static final long PAR_MINFLOP_THRESHOLD2 = 128L*1024; //MIN 2 MFLOP
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public static final int L2_CACHESIZE = 256 * 1024; //256KB (common size)
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public static final int L3_CACHESIZE = 16 * 1024 * 1024; //16MB (common size)

‎src/test/java/org/apache/sysds/test/component/matrix/MatrixMultiplyKernelTest.java‎

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/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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package org.apache.sysds.test.component.matrix;
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import org.apache.sysds.runtime.matrix.data.LibMatrixMult;
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import org.apache.sysds.runtime.matrix.data.MatrixBlock;
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import org.apache.sysds.test.TestUtils;
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import org.apache.sysds.utils.stats.InfrastructureAnalyzer;
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import org.junit.Test;
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public class MatrixMultiplyKernelTest {
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private static final int MIN_PAR = (int)LibMatrixMult.PAR_MINFLOP_THRESHOLD1+1;
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private static final int MIN_PAR_SQRT = (int)Math.sqrt(MIN_PAR);
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// dense-dense kernels
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@Test
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public void testDenseDenseDotProduct() {
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testMatrixMultiply(1, MIN_PAR, 1, 1, 1);
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}
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@Test
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public void testDenseDenseOuterProduct() {
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testMatrixMultiply(MIN_PAR_SQRT, 1, MIN_PAR_SQRT, 1, 1);
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}
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@Test
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public void testDenseDenseVectorScalar() {
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testMatrixMultiply(MIN_PAR, 1, 1, 1, 1);
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}
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@Test
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public void testDenseDenseMatrixSmallVector() {
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testMatrixMultiply(MIN_PAR, 16, 1, 1, 1);
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}
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// @Test //FIXME
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// public void testDenseDenseMatrixLargeVector() {
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// testMatrixMultiply(16, MIN_PAR, 1, 1, 1);
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// }
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@Test
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public void testDenseDenseVectorMatrix() {
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testMatrixMultiply(1, MIN_PAR, 16, 1, 1);
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}
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@Test
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public void testDenseDenseSmallMatrixMatrix() {
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testMatrixMultiply(16, MIN_PAR, 16, 1, 1);
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}
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@Test
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public void testDenseDenseMatrixSmallMatrix() {
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testMatrixMultiply(MIN_PAR_SQRT, MIN_PAR_SQRT, 4, 1, 1);
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}
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@Test
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public void testDenseDenseMatrixMatrix() {
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testMatrixMultiply(MIN_PAR_SQRT, MIN_PAR_SQRT, MIN_PAR_SQRT, 1, 1);
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}
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// dense-sparse kernels
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@Test
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public void testDenseSparseVectorMatrix() {
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testMatrixMultiply(1, MIN_PAR, 12, 1, 0.1);
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}
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@Test
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public void testDenseSparseMatrixMatrix() {
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testMatrixMultiply(MIN_PAR_SQRT, MIN_PAR_SQRT, MIN_PAR_SQRT, 1, 0.1);
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}
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// sparse-dense kernels
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// @Test FIXME
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// public void testSparseDenseDotProduct() {
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// testMatrixMultiply(1, MIN_PAR, 1, 0.1, 1);
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// }
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@Test
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public void testSparseDenseMatrixSmallVector() {
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testMatrixMultiply(MIN_PAR_SQRT, 1024, 1, 0.1, 1);
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}
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@Test // see SYSTEMDS-3769
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public void testSparseDenseMatrixLargeVector() {
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testMatrixMultiply(13, 8000, 1, 0.1, 1);
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}
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@Test
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public void testSparseDenseVectorMatrix() {
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testMatrixMultiply(1, MIN_PAR_SQRT, MIN_PAR_SQRT, 0.1, 1);
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}
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@Test
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public void testSparseDenseSmallMatrixMatrix() {
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testMatrixMultiply(9, MIN_PAR_SQRT, MIN_PAR_SQRT, 0.1, 1);
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}
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@Test
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public void testSparseDenseMatrixSmallMatrix() {
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testMatrixMultiply(MIN_PAR_SQRT, MIN_PAR_SQRT, 9, 0.1, 1);
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}
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@Test
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public void testSparseDenseMatrixMatrix() {
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testMatrixMultiply(MIN_PAR_SQRT, MIN_PAR_SQRT, MIN_PAR_SQRT, 0.1, 1);
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}
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// sparse-sparse kernels
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@Test
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public void testSparseSparseVectorMatrix() {
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testMatrixMultiply(1, MIN_PAR_SQRT, MIN_PAR_SQRT, 0.1, 0.1);
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}
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@Test //w/ sparse output
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public void testSparseSparseSparseMatrixMatrix() {
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testMatrixMultiply(MIN_PAR_SQRT, 2, MIN_PAR_SQRT, 0.1, 0.1);
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}
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@Test
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public void testSparseSparseMatrixSmallMatrix() {
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testMatrixMultiply(MIN_PAR_SQRT, 15, 1000, 0.1, 0.1);
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}
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@Test
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public void testSparseSparseMatrixMatrix() {
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testMatrixMultiply(MIN_PAR_SQRT, MIN_PAR_SQRT, MIN_PAR_SQRT, 0.1, 0.1);
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}
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private void testMatrixMultiply(int n, int m, int l, double sp1, double sp2) {
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MatrixBlock mb1 = MatrixBlock.randOperations(n, m, sp1, 0, 0.1, "uniform", 3);
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MatrixBlock mb2 = MatrixBlock.randOperations(m, l, sp2, 0, 0.1, "uniform", 7);
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//run single- and multi-threaded kernels and compare
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MatrixBlock ret1 = LibMatrixMult.matrixMult(mb1, mb2);
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MatrixBlock ret2 = LibMatrixMult.matrixMult(mb1, mb2,
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InfrastructureAnalyzer.getLocalParallelism());
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TestUtils.compareMatrices(ret1, ret2, 1e-8);
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}
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}

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