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[MINOR] Additional tests for single-/multi-threaded matmult and uagg
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‎src/main/java/org/apache/sysds/runtime/matrix/data/LibMatrixAgg.java‎

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@@ -98,7 +98,7 @@ public class LibMatrixAgg {
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//internal configuration parameters
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private static final boolean NAN_AWARENESS = false;
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private static final long PAR_NUMCELL_THRESHOLD1 = 1024*256; //Min 256K elements
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public static final long PAR_NUMCELL_THRESHOLD1 = 1024*256; //Min 256K elements
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private static final long PAR_NUMCELL_THRESHOLD2 = 1024*4; //Min 4K elements
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private static final long PAR_INTERMEDIATE_SIZE_THRESHOLD = 2*1024*1024; //Max 2MB
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@@ -207,7 +207,7 @@ else if( !in.sparse && lastColCorr )
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}
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public static MatrixBlock aggregateUnaryMatrix(AggregateUnaryOperator op,MatrixBlock in, MatrixValue result,
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public static MatrixBlock aggregateUnaryMatrix(AggregateUnaryOperator op, MatrixBlock in, MatrixValue result,
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int blen, MatrixIndexes indexesIn, boolean inCP){
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MatrixBlock ret = LibMatrixAgg.prepareAggregateUnaryOutput(in, op, result, blen);

‎src/test/java/org/apache/sysds/test/component/matrix/MatrixAggKernelTest.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.instructions.InstructionUtils;
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import org.apache.sysds.runtime.matrix.data.LibMatrixAgg;
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import org.apache.sysds.runtime.matrix.data.MatrixBlock;
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import org.apache.sysds.runtime.matrix.operators.AggregateUnaryOperator;
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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 MatrixAggKernelTest {
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private static final int MIN_PAR = 2*(int)LibMatrixAgg.PAR_NUMCELL_THRESHOLD1;
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private static final int MIN_PAR_SQRT = (int)Math.sqrt(MIN_PAR);
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private static final int K = InfrastructureAnalyzer.getLocalParallelism();
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@Test
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public void testDenseKahanSum() {
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testMatrixAggregation("uak+", "uark+", "uack+", 0.95);
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}
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@Test
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public void testSparseKahanSum() {
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testMatrixAggregation("uak+", "uark+", "uack+", 0.1);
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}
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@Test
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public void testDenseSum() {
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testMatrixAggregation("ua+", "uar+", "uac+", 0.95);
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}
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@Test
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public void testSparseSum() {
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testMatrixAggregation("ua+", "uar+", "uac+", 0.1);
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}
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@Test
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public void testDenseSqSum() {
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testMatrixAggregation("uasqk+", "uarsqk+", "uacsqk+", 0.95);
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}
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@Test
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public void testSparseSqSum() {
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testMatrixAggregation("uasqk+", "uarsqk+", "uacsqk+", 0.1);
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}
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@Test
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public void testDenseMean() {
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testMatrixAggregation("uamean", "uarmean", "uacmean", 0.95);
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}
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@Test
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public void testSparseMean() {
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testMatrixAggregation("uamean", "uarmean", "uacmean", 0.1);
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}
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@Test
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public void testDenseVar() {
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testMatrixAggregation("uavar", "uarvar", "uacvar", 0.95);
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}
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@Test
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public void testSparseVar() {
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testMatrixAggregation("uavar", "uarvar", "uacvar", 0.1);
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}
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@Test
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public void testDenseMin() {
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testMatrixAggregation("uamin", "uarmin", "uacmin", 0.95);
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}
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@Test
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public void testSparseMin() {
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testMatrixAggregation("uamin", "uarmin", "uacmin", 0.1);
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}
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@Test
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public void testDenseMax() {
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testMatrixAggregation("uamax", "uarmax", "uacmax", 0.95);
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}
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@Test
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public void testSparseMax() {
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testMatrixAggregation("uamax", "uarmax", "uacmax", 0.1);
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}
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private void testMatrixAggregation(String opcode1, String opcode2, String opcode3, double sp) {
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testMatrixAggregation(getOp(opcode1,1), getOp(opcode1,K), MIN_PAR, 1, sp);
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testMatrixAggregation(getOp(opcode2,1), getOp(opcode2,K), 1, MIN_PAR, sp);
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testMatrixAggregation(getOp(opcode3,1), getOp(opcode3,K), MIN_PAR, 1, sp);
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testMatrixAggregation(getOp(opcode1,1), getOp(opcode1,K), MIN_PAR_SQRT, MIN_PAR_SQRT, sp);
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testMatrixAggregation(getOp(opcode2,1), getOp(opcode2,K), MIN_PAR_SQRT, MIN_PAR_SQRT, sp);
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testMatrixAggregation(getOp(opcode3,1), getOp(opcode3,K), MIN_PAR_SQRT, MIN_PAR_SQRT, sp);
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}
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private void testMatrixAggregation(AggregateUnaryOperator uaop1,
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AggregateUnaryOperator uaopk, int n, int m, double sp)
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{
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MatrixBlock mb1 = MatrixBlock.randOperations(n, m, sp, 0, 0.1, "uniform", 7);
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//run single- and multi-threaded kernels and compare
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MatrixBlock ret1 = mb1.aggregateUnaryOperations(uaop1);
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MatrixBlock ret2 = mb1.aggregateUnaryOperations(uaopk);
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TestUtils.compareMatrices(ret1, ret2, 1e-8);
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}
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private AggregateUnaryOperator getOp(String opcode, int threads) {
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return InstructionUtils.parseBasicAggregateUnaryOperator(opcode, threads);
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}
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}

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

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@@ -151,6 +151,33 @@ 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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//ultra-sparse vs all
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@Test
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public void testUltraSparseDense() {
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testMatrixMultiply(MIN_PAR_SQRT, MIN_PAR_SQRT, MIN_PAR_SQRT, 1e-5, 0.95);
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}
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@Test
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public void testUltraSparseSparse() {
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testMatrixMultiply(MIN_PAR_SQRT, MIN_PAR_SQRT, MIN_PAR_SQRT, 1e-5, 0.1);
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}
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@Test
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public void testUltraSparseUltraSparse() {
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testMatrixMultiply(MIN_PAR_SQRT, MIN_PAR_SQRT, MIN_PAR_SQRT, 1e-5, 1e-5);
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}
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@Test
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public void testSparseUltraSparse() {
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testMatrixMultiply(MIN_PAR_SQRT, MIN_PAR_SQRT, MIN_PAR_SQRT, 0.1, 1e-5);
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}
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@Test
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public void testDenseUltraSparse() {
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testMatrixMultiply(MIN_PAR_SQRT, MIN_PAR_SQRT, MIN_PAR_SQRT, 0.95, 1e-5);
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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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