diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/allocator/impl/AtomicAllocator.java b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/allocator/impl/AtomicAllocator.java
index 03e5df160..aaccf9a34 100644
--- a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/allocator/impl/AtomicAllocator.java
+++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/allocator/impl/AtomicAllocator.java
@@ -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();
     }
 
 
diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java
index 6f0944c5a..a8f3a0a3b 100644
--- a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java
+++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java
@@ -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;
     }
 
     /**
diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaExecutioner.java b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaExecutioner.java
index 1615e4843..04b86dc02 100644
--- a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaExecutioner.java
+++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaExecutioner.java
@@ -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
diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/DataSetTest.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/DataSetTest.java
index a62dc631e..816003009 100755
--- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/DataSetTest.java
+++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/DataSetTest.java
@@ -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