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[SYSTEMDS-3855] Vector API: Sparse-Sparse Matrix Multiplication Kernels #2553

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[SYSTEMDS-3855] Vector API: Sparse-Sparse Matrix Multiplication Kernels #2553

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14 changes: 14 additions & 0 deletions pom.xml
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Original file line number Diff line number Diff line change
Expand Up @@ -1588,5 +1588,19 @@
<artifactId>fastdoubleparser</artifactId>
<version>0.9.0</version>
</dependency>

<dependency>
<groupId>org.openjdk.jmh</groupId>
<artifactId>jmh-core</artifactId>
<version>1.37</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.openjdk.jmh</groupId>
<artifactId>jmh-generator-annprocess</artifactId>
<version>1.37</version>
<scope>test</scope>
</dependency>

</dependencies>
</project>
8 changes: 4 additions & 4 deletions src/main/java/org/apache/sysds/runtime/matrix/data/LibMatrixMult.java
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Original file line number Diff line number Diff line change
Expand Up @@ -2051,7 +2051,7 @@ private static void matrixMultSparseSparseVM(SparseBlock a, SparseBlock b, Dense
int blen = b.size(aix[k]);
int[] bix = b.indexes(aix[k]);
double[] bvals = b.values(aix[k]);
vectMultiplyAdd(avals[k], bvals, cvals, bix, bpos, 0, blen);
vectMultiplyAddScatter(avals[k], bvals, cvals, bix, bpos, 0, blen);
}
}

Expand All @@ -2069,7 +2069,7 @@ private static long matrixMultSparseSparseSparseMM(SparseBlock a, SparseBlock b,
for(int k = apos; k < apos+alen; k++) {
int aixk = aix[k];
if( b.isEmpty(aixk) ) continue;
vectMultiplyAdd(avals[k], b.values(aixk), tmp,
vectMultiplyAddScatter(avals[k], b.values(aixk), tmp,
b.indexes(aixk), b.pos(aixk), 0, b.size(aixk));
hitNonEmpty = true;
}
Expand Down Expand Up @@ -2100,7 +2100,7 @@ private static void matrixMultSparseSparseMMSmallRHS(SparseBlock a, SparseBlock
for(int k = apos; k < apos+alen; k++) {
int aixk = aix[k];
if( b.isEmpty(aixk) ) continue;
vectMultiplyAdd(avals[k], b.values(aixk), cvals,
vectMultiplyAddScatter(avals[k], b.values(aixk), cvals,
b.indexes(aixk), b.pos(aixk), cix, b.size(aixk));
}
}
Expand Down Expand Up @@ -2132,7 +2132,7 @@ private static void matrixMultSparseSparseMM(SparseBlock a, SparseBlock b, Dense
int k = curk[i-bi] + apos;
for(; k < apos+alen && aix[k]<bkmin; k++) {
if( b.isEmpty(aix[k]) ) continue;
vectMultiplyAdd(avals[k], b.values(aix[k]), cvals,
vectMultiplyAddScatter(avals[k], b.values(aix[k]), cvals,
b.indexes(aix[k]), b.pos(aix[k]), cix, b.size(aix[k]));
}
curk[i-bi] = k - apos;
Expand Down
83 changes: 83 additions & 0 deletions src/test/java/org/apache/sysds/test/component/matrix/BenchmarkMatMult.java
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@@ -0,0 +1,83 @@
package org.apache.sysds.test.component.matrix;

import java.util.concurrent.TimeUnit;
import org.apache.sysds.runtime.matrix.data.LibMatrixMult;
import org.apache.sysds.runtime.matrix.data.MatrixBlock;
import org.apache.sysds.test.TestUtils;
import org.openjdk.jmh.annotations.*;
import org.openjdk.jmh.infra.Blackhole;
import org.openjdk.jmh.results.format.ResultFormatType;
import org.openjdk.jmh.runner.Runner;
import org.openjdk.jmh.runner.RunnerException;
import org.openjdk.jmh.runner.options.Options;
import org.openjdk.jmh.runner.options.OptionsBuilder;

@State(Scope.Thread)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MICROSECONDS)
@Warmup(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS)
@Measurement(iterations = 10, time = 1, timeUnit = TimeUnit.SECONDS)
@Fork(1)
public class BenchmarkMatMult {

private MatrixBlock left;
private MatrixBlock right;
private MatrixBlock result;

@Param({"1024", "2048", "4096", "8192"})
public int m;

@Param({"1"})
public int cd;

@Param({"0.5", "0.75", "1.0"})
public double sparsityLeft;

@Param({"0.001", "0.01", "0.1", "0.2"})
public double sparsityRight;

@Param({"1024", "2048", "4096", "8192"})
public int n;

@Setup(Level.Trial)
public void setup() {
left = TestUtils.ceil(TestUtils.generateTestMatrixBlock(m, cd, -10, 10, sparsityLeft, 13));
if (!left.isInSparseFormat()) {
left.sparseToDense();
left.denseToSparse(true);
}

MatrixBlock rightOriginal = TestUtils.ceil(TestUtils.generateTestMatrixBlock(cd, n, -10, 10, sparsityRight, 14));
right = new MatrixBlock();
right.copy(rightOriginal, false);
if (!right.isInSparseFormat()) {
right.sparseToDense();
right.denseToSparse(true);
}

result = new MatrixBlock(m, n, true);
result.allocateBlock();
}

@Setup(Level.Iteration)
public void clearResult() {
result.reset(m, n, true);
}

@Benchmark
public void benchmarkSparseSparse(Blackhole bh) {
LibMatrixMult.matrixMult(left, right, result);
bh.consume(result);
}

public static void main(String[] args) throws RunnerException {
String tag = "vector-api-mult-sparse-sparse-" + System.currentTimeMillis();
Options opt = new OptionsBuilder()
.include(BenchmarkMatMult.class.getName())
.jvmArgs("--add-modules=jdk.incubator.vector")
.result(tag + ".csv")
.resultFormat(ResultFormatType.CSV)
.build();
new Runner(opt).run();
}
}