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Merge pull request #2 from margarita0303/feature/tensors-performance

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Removed checks for accuracy in benchmarks
This commit is contained in:
Roland Grinis 2022-08-17 08:47:39 +01:00 committed by GitHub
commit 7b693270f0
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1 changed files with 0 additions and 56 deletions
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56
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/SVDBenchmark.kt
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@ -29,13 +29,6 @@ class SVDBenchmark {
@Benchmark
fun svdPowerMethodSmall(blackhole: Blackhole) {
val svd = tensorSmall.svdPowerMethod()
val tensorSVD = svd.first
.dot(
diagonalEmbedding(svd.second)
.dot(svd.third.transpose())
)
assertTrue(tensorSVD.eq(tensorSmall, epsilon))
blackhole.consume(
tensorSmall.svdPowerMethod()
)
@ -43,13 +36,6 @@ class SVDBenchmark {
@Benchmark
fun svdPowerMethodMedium(blackhole: Blackhole) {
val svd = tensorMedium.svdPowerMethod()
val tensorSVD = svd.first
.dot(
diagonalEmbedding(svd.second)
.dot(svd.third.transpose())
)
assertTrue(tensorSVD.eq(tensorMedium, epsilon))
blackhole.consume(
tensorMedium.svdPowerMethod()
)
@ -57,13 +43,6 @@ class SVDBenchmark {
@Benchmark
fun svdPowerMethodLarge(blackhole: Blackhole) {
val svd = tensorLarge.svdPowerMethod()
val tensorSVD = svd.first
.dot(
diagonalEmbedding(svd.second)
.dot(svd.third.transpose())
)
assertTrue(tensorSVD.eq(tensorLarge, epsilon))
blackhole.consume(
tensorLarge.svdPowerMethod()
)
@ -71,13 +50,6 @@ class SVDBenchmark {
@Benchmark
fun svdPowerMethodVeryLarge(blackhole: Blackhole) {
val svd = tensorVeryLarge.svdPowerMethod()
val tensorSVD = svd.first
.dot(
diagonalEmbedding(svd.second)
.dot(svd.third.transpose())
)
assertTrue(tensorSVD.eq(tensorVeryLarge, epsilon))
blackhole.consume(
tensorVeryLarge.svdPowerMethod()
)
@ -85,13 +57,6 @@ class SVDBenchmark {
@Benchmark
fun svdGolubKahanSmall(blackhole: Blackhole) {
val svd = tensorSmall.svdGolubKahan()
val tensorSVD = svd.first
.dot(
diagonalEmbedding(svd.second)
.dot(svd.third.transpose())
)
assertTrue(tensorSVD.eq(tensorSmall, epsilon))
blackhole.consume(
tensorSmall.svdGolubKahan()
)
@ -99,13 +64,6 @@ class SVDBenchmark {
@Benchmark
fun svdGolubKahanMedium(blackhole: Blackhole) {
val svd = tensorMedium.svdGolubKahan()
val tensorSVD = svd.first
.dot(
diagonalEmbedding(svd.second)
.dot(svd.third.transpose())
)
assertTrue(tensorSVD.eq(tensorMedium, epsilon))
blackhole.consume(
tensorMedium.svdGolubKahan()
)
@ -113,13 +71,6 @@ class SVDBenchmark {
@Benchmark
fun svdGolubKahanLarge(blackhole: Blackhole) {
val svd = tensorLarge.svdGolubKahan()
val tensorSVD = svd.first
.dot(
diagonalEmbedding(svd.second)
.dot(svd.third.transpose())
)
assertTrue(tensorSVD.eq(tensorLarge, epsilon))
blackhole.consume(
tensorLarge.svdGolubKahan()
)
@ -127,13 +78,6 @@ class SVDBenchmark {
@Benchmark
fun svdGolubKahanVeryLarge(blackhole: Blackhole) {
val svd = tensorVeryLarge.svdGolubKahan()
val tensorSVD = svd.first
.dot(
diagonalEmbedding(svd.second)
.dot(svd.third.transpose())
)
assertTrue(tensorSVD.eq(tensorVeryLarge, epsilon))
blackhole.consume(
tensorVeryLarge.svdGolubKahan()
)