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new c940502658 [SYSTEMDS-3773] Fix two matmult kernels w/ parallelization
over rhs
c940502658 is described below
commit c94050265827deab65200c1b2ba4bad60f6d6f96
Author: Matthias Boehm <[email protected]>
AuthorDate: Sun Sep 22 15:50:25 2024 +0200
[SYSTEMDS-3773] Fix two matmult kernels w/ parallelization over rhs
The matmult kernel library parallelizes by default over rows in the
left-hand-side matrix, but for specific size regimes, switches to a
parallelization over the rows or columns of the right-hand-side. The
recently added full-coverage tests found two bug, which this patch fixes
a) dense-dense matrix-vector multiplication w/ large vectors
--> extended implementation to support the other parallelization
b) sparse-dense vector-vector dot product
--> disable parallelization for this specific case to use the
existing kernel without binary searches
---
.../sysds/runtime/matrix/data/LibMatrixMult.java | 21 +++++++++++++--------
.../component/matrix/MatrixMultiplyKernelTest.java | 21 +++++++++++++--------
2 files changed, 26 insertions(+), 16 deletions(-)
diff --git
a/src/main/java/org/apache/sysds/runtime/matrix/data/LibMatrixMult.java
b/src/main/java/org/apache/sysds/runtime/matrix/data/LibMatrixMult.java
index 328b43d6ca..c4eddd90fa 100644
--- a/src/main/java/org/apache/sysds/runtime/matrix/data/LibMatrixMult.java
+++ b/src/main/java/org/apache/sysds/runtime/matrix/data/LibMatrixMult.java
@@ -279,7 +279,7 @@ public class LibMatrixMult
boolean pm2r = !ultraSparse && !sparse &&
checkParMatrixMultRightInputRows(m1, m2, k);
boolean pm2c = !ultraSparse &&
checkParMatrixMultRightInputCols(m1, m2, k, pm2r);
int num = pm2r ? m2.rlen : pm2c ? m2.clen : m1.rlen;
-
+
// core multi-threaded matrix mult computation
// (currently: always parallelization over number of rows)
final ExecutorService pool = CommonThreadPool.get(k);
@@ -1052,7 +1052,7 @@ public class LibMatrixMult
matrixMultDenseDenseMVShortRHS(a, b, c, cd, rl, ru);
}
else if( n==1 ) { //MATRIX-VECTOR (tall rhs)
- matrixMultDenseDenseMVTallRHS(a, b, c, cd, rl, ru);
+ matrixMultDenseDenseMVTallRHS(a, b, c, pm2, cd, rl, ru);
}
else if( pm2 && m==1 ) { //VECTOR-MATRIX
matrixMultDenseDenseVM(a, b, c, n, cd, rl, ru);
@@ -1075,15 +1075,20 @@ public class LibMatrixMult
cvals[i] = dotProduct(a.values(i), bvals, a.pos(i), 0,
cd);
}
- private static void matrixMultDenseDenseMVTallRHS(DenseBlock a,
DenseBlock b, DenseBlock c, int cd, int rl, int ru) {
+ private static void matrixMultDenseDenseMVTallRHS(DenseBlock a,
DenseBlock b, DenseBlock c, boolean pm2, int cd, int rl, int ru) {
final int blocksizeI = 32;
final int blocksizeK = 2*1024; //16KB vector blocks (L1)
double[] bvals = b.valuesAt(0);
double[] cvals = c.valuesAt(0);
- for( int bi=rl; bi<ru; bi+=blocksizeI ) {
- int bimin = Math.min(bi+blocksizeI, ru);
- for( int bk=0; bk<cd; bk+=blocksizeK ) {
- int bkmin = Math.min(bk+blocksizeK, cd);
+ // setup bounds according to parallelization strategy
+ // (default: rows in lhs, pm2: rows in rhs)
+ int cl = pm2 ? rl : 0, cu = pm2 ? ru : cd;
+ int rl2 = pm2 ? 0 : rl, ru2 = pm2 ? a.numRows() : ru;
+ // matrix-vector multication with cache blocking of vector
+ for( int bi=rl2; bi<ru2; bi+=blocksizeI ) {
+ int bimin = Math.min(bi+blocksizeI, ru2);
+ for( int bk=cl; bk<cu; bk+=blocksizeK ) {
+ int bkmin = Math.min(bk+blocksizeK, cu);
for( int i=bi; i<bimin; i++)
cvals[i] += dotProduct(a.values(i),
bvals, a.pos(i,bk), bk, bkmin-bk);
}
@@ -4349,7 +4354,7 @@ public class LibMatrixMult
private static boolean checkParMatrixMultRightInputRows( MatrixBlock
m1, MatrixBlock m2, int k ) {
//parallelize over rows in rhs matrix if number of rows in
lhs/output is very small
double jvmMem = InfrastructureAnalyzer.getLocalMaxMemory();
- return (m1.rlen==1 && !(m1.isUltraSparse()||m2.isUltraSparse()))
+ return (m1.rlen==1 && !(m1.sparse && m2.clen==1) &&
!(m1.isUltraSparse()||m2.isUltraSparse()))
|| (m1.rlen<=16 && m2.rlen > m1.rlen && (!m1.sparse |
m2.clen > 1)
&& ( !m1.isUltraSparse() && !(m1.sparse & m2.sparse)
) //dense-dense / sparse-dense / dense-sparse
&& (long)k * 8 * m1.rlen * m2.clen <
Math.max(MEM_OVERHEAD_THRESHOLD,0.01*jvmMem) );
diff --git
a/src/test/java/org/apache/sysds/test/component/matrix/MatrixMultiplyKernelTest.java
b/src/test/java/org/apache/sysds/test/component/matrix/MatrixMultiplyKernelTest.java
index 9975213762..1d51e6fd8e 100644
---
a/src/test/java/org/apache/sysds/test/component/matrix/MatrixMultiplyKernelTest.java
+++
b/src/test/java/org/apache/sysds/test/component/matrix/MatrixMultiplyKernelTest.java
@@ -51,10 +51,15 @@ public class MatrixMultiplyKernelTest {
testMatrixMultiply(MIN_PAR, 16, 1, 1, 1);
}
-// @Test //FIXME
-// public void testDenseDenseMatrixLargeVector() {
-// testMatrixMultiply(16, MIN_PAR, 1, 1, 1);
-// }
+ @Test //parallelization over rows in lhs
+ public void testDenseDenseMatrixLargeVector() {
+ testMatrixMultiply(4000, 3000, 1, 1, 1);
+ }
+
+ @Test //parallelization over rows in rhs
+ public void testDenseDenseMatrixLargeVectorPm2() {
+ testMatrixMultiply(16, MIN_PAR, 1, 1, 1);
+ }
@Test
public void testDenseDenseVectorMatrix() {
@@ -90,10 +95,10 @@ public class MatrixMultiplyKernelTest {
// sparse-dense kernels
-// @Test FIXME
-// public void testSparseDenseDotProduct() {
-// testMatrixMultiply(1, MIN_PAR, 1, 0.1, 1);
-// }
+ @Test
+ public void testSparseDenseDotProduct() {
+ testMatrixMultiply(1, MIN_PAR, 1, 0.1, 1);
+ }
@Test
public void testSparseDenseMatrixSmallVector() {
