diff --git a/GPy/inference/latent_function_inference/var_dtc_parallel.py b/GPy/inference/latent_function_inference/var_dtc_parallel.py
index 7b11085e..dc4f45d5 100644
--- a/GPy/inference/latent_function_inference/var_dtc_parallel.py
+++ b/GPy/inference/latent_function_inference/var_dtc_parallel.py
@@ -78,7 +78,7 @@ class VarDTC_minibatch(LatentFunctionInference):
         num_inducing = Z.shape[0]        
         num_data, output_dim = Y.shape
         
-        if self.batchsize == None or self.batchsize>num_data:
+        if self.batchsize == None:
             self.batchsize = num_data
 
         trYYT = self.get_trYYT(Y)
diff --git a/GPy/models/ss_gplvm.py b/GPy/models/ss_gplvm.py
index 8581bf7c..08dc6d95 100644
--- a/GPy/models/ss_gplvm.py
+++ b/GPy/models/ss_gplvm.py
@@ -14,7 +14,6 @@ from ..core.parameterization.variational import SpikeAndSlabPrior, SpikeAndSlabP
 from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch
 from ..inference.latent_function_inference.var_dtc_gpu import VarDTC_GPU
 
-
 class SSGPLVM(SparseGP):
     """
     Spike-and-Slab Gaussian Process Latent Variable Model
@@ -88,7 +87,8 @@ class SSGPLVM(SparseGP):
             self.X_local = self.X[Y_start:Y_end]
             self.Y_range = (Y_start, Y_end)
             self.Y_list = np.array(Y_list)
-            [mpi_comm.Bcast(p, root=0) for p in self.flattened_parameters]
+            print self.mpi_comm.rank, self.Y_range
+            mpi_comm.Bcast(self.param_array, root=0)
         
     def set_X_gradients(self, X, X_grad):
         """Set the gradients of the posterior distribution of X in its specific form."""
@@ -136,20 +136,32 @@ class SSGPLVM(SparseGP):
     def __setstate__(self, state):
         return super(SSGPLVM, self).__setstate__(state)
     
-    def _grads(self, x):
+    #=====================================================
+    # The MPI parallelization 
+    #     - can move to model at some point
+    #=====================================================
+    
+    def _set_params_transformed(self, p):
         if self.mpi_comm != None:
-            self.mpi_comm.Bcast(x, root=0)
-        obj_grads = super(SSGPLVM, self)._grads(x)
-        return obj_grads
-
-    def _objective(self, x):
-        if self.mpi_comm != None:
-            self.mpi_comm.Bcast(x, root=0)
-        obj = super(SSGPLVM, self)._objective(x)
-        return obj
-
-    def _objective_grads(self, x):
-        if self.mpi_comm != None:
-            self.mpi_comm.Bcast(x, root=0)
-        obj_f, obj_grads = super(SSGPLVM, self)._objective_grads(x)
-        return obj_f, obj_grads
+            if self.mpi_comm.rank==0:
+                self.mpi_comm.Bcast(np.int32(1),root=0)
+            self.mpi_comm.Bcast(p, root=0)
+        super(SSGPLVM, self)._set_params_transformed(p)
+        
+    def optimize(self, optimizer=None, start=None, **kwargs):        
+        if self.mpi_comm==None:
+            super(SSGPLVM, self).optimize(optimizer,start,**kwargs)
+        elif self.mpi_comm.rank==0:
+            super(SSGPLVM, self).optimize(optimizer,start,**kwargs)
+            self.mpi_comm.Bcast(np.int32(-1),root=0)
+        elif self.mpi_comm.rank>0:
+            x = self._get_params_transformed().copy()
+            flag = np.empty(1,dtype=np.int32)
+            while True:
+                self.mpi_comm.Bcast(flag,root=0)
+                if flag==1:
+                    self._set_params_transformed(x)
+                elif flag==-1:
+                    break
+                else:
+                    raise Exception("Unrecognizable flag for synchronization!")