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CUDA sync tweaks (#194) 

* ThreadLocal cache for CudaContext

Signed-off-by: raver119 <raver119@gmail.com>

* temp commit

Signed-off-by: raver119 <raver119@gmail.com>

* remove unwanted synchronization

Signed-off-by: raver119 <raver119@gmail.com>
master
raver119 2020-01-28 10:55:06 +03:00 committed by GitHub
parent 7ef0ef907e
commit 9f719488b9
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 60 additions and 60 deletions
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10
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/allocator/impl/AtomicAllocator.java
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@ -353,18 +353,8 @@ public class AtomicAllocator implements Allocator {
*/
@Override
public void synchronizeHostData(DataBuffer buffer) {
// we don't want non-committed ops left behind
Nd4j.getExecutioner().commit();
val oPtr = NativeOpsHolder.getInstance().getDeviceNativeOps().dbPrimaryBuffer(((BaseCudaDataBuffer) buffer).getOpaqueDataBuffer());
// we actually need synchronization only in device-dependant environment. no-op otherwise. managed by native code
NativeOpsHolder.getInstance().getDeviceNativeOps().dbSyncToPrimary(((BaseCudaDataBuffer) buffer).getOpaqueDataBuffer());
val cPtr = NativeOpsHolder.getInstance().getDeviceNativeOps().dbPrimaryBuffer(((BaseCudaDataBuffer) buffer).getOpaqueDataBuffer());
//assert oPtr.address() == cPtr.address();
//assert buffer.address() == oPtr.address();
}

31
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java
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@ -102,6 +102,8 @@ public class CudaZeroHandler implements MemoryHandler {
private final AffinityManager affinityManager = Nd4j.getAffinityManager();
private final transient ThreadLocal<CudaContext> tlContext = new ThreadLocal<>();
/*
table for Thread, Device, Object allocations of device memory. Objects should be used to grab Allocation point from allocationsMap
*/
@ -1018,18 +1020,25 @@ public class CudaZeroHandler implements MemoryHandler {
* @return
*/
public CudaContext getCudaContext() {
val lc = nativeOps.defaultLaunchContext();
var ctx = tlContext.get();
if (ctx == null) {
val lc = nativeOps.defaultLaunchContext();
return CudaContext.builder()
.bufferScalar(nativeOps.lcScalarPointer(lc))
.bufferReduction(nativeOps.lcReductionPointer(lc))
.bufferAllocation(nativeOps.lcAllocationPointer(lc))
.bufferSpecial(nativeOps.lcScalarPointer(lc))
.oldStream(new cudaStream_t(nativeOps.lcExecutionStream(lc)))
.specialStream(new cudaStream_t(nativeOps.lcCopyStream(lc)))
.cublasHandle(getCudaCublasHandle(lc))
.solverHandle(new cusolverDnHandle_t(nativeOps.lcSolverHandle(lc)))
.build();
ctx = CudaContext.builder()
.bufferScalar(nativeOps.lcScalarPointer(lc))
.bufferReduction(nativeOps.lcReductionPointer(lc))
.bufferAllocation(nativeOps.lcAllocationPointer(lc))
.bufferSpecial(nativeOps.lcScalarPointer(lc))
.oldStream(new cudaStream_t(nativeOps.lcExecutionStream(lc)))
.specialStream(new cudaStream_t(nativeOps.lcCopyStream(lc)))
.cublasHandle(getCudaCublasHandle(lc))
.solverHandle(new cusolverDnHandle_t(nativeOps.lcSolverHandle(lc)))
.build();
tlContext.set(ctx);
return ctx;
} else
return ctx;
}
/**

5
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaExecutioner.java
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@ -1610,8 +1610,9 @@ public class CudaExecutioner extends DefaultOpExecutioner {
@Override
public void commit() {
AtomicAllocator.getInstance().getDeviceContext().syncOldStream();
AtomicAllocator.getInstance().getDeviceContext().syncSpecialStream();
val ctx = AtomicAllocator.getInstance().getDeviceContext();
ctx.syncOldStream();
ctx.syncSpecialStream();
}
@Override

74
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/DataSetTest.java
View File

@ -738,52 +738,52 @@ public class DataSetTest extends BaseNd4jTest {
@Test
public void testShuffleNd() {
int numDims = 7;
int nLabels = 3;
Random r = new Random();
int numDims = 7;
int nLabels = 3;
Random r = new Random();
int[] shape = new int[numDims];
int entries = 1;
for (int i = 0; i < numDims; i++) {
//randomly generating shapes bigger than 1
shape[i] = r.nextInt(4) + 2;
entries *= shape[i];
}
int labels = shape[0] * nLabels;
int[] shape = new int[numDims];
int entries = 1;
for (int i = 0; i < numDims; i++) {
//randomly generating shapes bigger than 1
shape[i] = r.nextInt(4) + 2;
entries *= shape[i];
}
int labels = shape[0] * nLabels;
INDArray ds_data = Nd4j.linspace(1, entries, entries, DataType.INT).reshape(shape);
INDArray ds_labels = Nd4j.linspace(1, labels, labels, DataType.INT).reshape(shape[0], nLabels);
INDArray ds_data = Nd4j.linspace(1, entries, entries, DataType.INT).reshape(shape);
INDArray ds_labels = Nd4j.linspace(1, labels, labels, DataType.INT).reshape(shape[0], nLabels);
DataSet ds = new DataSet(ds_data, ds_labels);
ds.shuffle();
DataSet ds = new DataSet(ds_data, ds_labels);
ds.shuffle();
//Checking Nd dataset which is the data
for (int dim = 1; dim < numDims; dim++) {
//get tensor along dimension - the order in every dimension but zero should be preserved
for (int tensorNum = 0; tensorNum < ds_data.tensorsAlongDimension(dim); tensorNum++) {
//the difference between consecutive elements should be equal to the stride
for (int i = 0, j = 1; j < shape[dim]; i++, j++) {
int f_element = ds.getFeatures().tensorAlongDimension(tensorNum, dim).getInt(i);
int f_next_element = ds.getFeatures().tensorAlongDimension(tensorNum, dim).getInt(j);
int f_element_diff = f_next_element - f_element;
assertEquals(f_element_diff, ds_data.stride(dim));
//Checking Nd dataset which is the data
for (int dim = 1; dim < numDims; dim++) {
//get tensor along dimension - the order in every dimension but zero should be preserved
for (int tensorNum = 0; tensorNum < ds_data.tensorsAlongDimension(dim); tensorNum++) {
//the difference between consecutive elements should be equal to the stride
for (int i = 0, j = 1; j < shape[dim]; i++, j++) {
int f_element = ds.getFeatures().tensorAlongDimension(tensorNum, dim).getInt(i);
int f_next_element = ds.getFeatures().tensorAlongDimension(tensorNum, dim).getInt(j);
int f_element_diff = f_next_element - f_element;
assertEquals(f_element_diff, ds_data.stride(dim));
}
}
}
}
//Checking 2d, features
int dim = 1;
//get tensor along dimension - the order in every dimension but zero should be preserved
for (int tensorNum = 0; tensorNum < ds_labels.tensorsAlongDimension(dim); tensorNum++) {
//the difference between consecutive elements should be equal to the stride
for (int i = 0, j = 1; j < nLabels; i++, j++) {
int l_element = ds.getLabels().tensorAlongDimension(tensorNum, dim).getInt(i);
int l_next_element = ds.getLabels().tensorAlongDimension(tensorNum, dim).getInt(j);
int l_element_diff = l_next_element - l_element;
assertEquals(l_element_diff, ds_labels.stride(dim));
//Checking 2d, features
int dim = 1;
//get tensor along dimension - the order in every dimension but zero should be preserved
for (int tensorNum = 0; tensorNum < ds_labels.tensorsAlongDimension(dim); tensorNum++) {
//the difference between consecutive elements should be equal to the stride
for (int i = 0, j = 1; j < nLabels; i++, j++) {
int l_element = ds.getLabels().tensorAlongDimension(tensorNum, dim).getInt(i);
int l_next_element = ds.getLabels().tensorAlongDimension(tensorNum, dim).getInt(j);
int l_element_diff = l_next_element - l_element;
assertEquals(l_element_diff, ds_labels.stride(dim));
}
}
}
}
@Test