diff --git a/GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.py b/GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.py
index 12c39e16..a21a15e3 100644
--- a/GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.py
+++ b/GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.py
@@ -410,7 +410,7 @@ class PSICOMP_SSRBF(object):
         dpsi1_dl_gpu = self.gpuCache['dpsi1_dl_gpu']
         dpsi2_dl_gpu = self.gpuCache['dpsi2_dl_gpu']
         psi1_comb_gpu = self.gpuCache['psi1_neq_gpu']
-        psi2_comb_gpu = self.gpuCache['psi1_neq_gpu']
+        psi2_comb_gpu = self.gpuCache['psi2_neq_gpu']
         grad_dl_gpu = self.gpuCache['grad_l_gpu']
         
         # variance
diff --git a/GPy/util/linalg_gpu.py b/GPy/util/linalg_gpu.py
index 6f5dc45b..60eb8101 100644
--- a/GPy/util/linalg_gpu.py
+++ b/GPy/util/linalg_gpu.py
@@ -28,8 +28,8 @@ try:
     # log(1.0-X)
     logOne = ElementwiseKernel("double *in, double *out", "out[i] = log(1.-in[i])", "logOne_element")
     
-    # multiplication with broadcast on the last dimension (a has to be smaller than b)
-    mul_bcast = ElementwiseKernel("double *out, double *a, double *b, int a_size", "out[i] = b[i]*a[i % a_size ]", "mul_bcast")
+    # multiplication with broadcast on the last dimension
+    mul_bcast = ElementwiseKernel("double *out, double *shorter, double *longer, int shorter_size", "out[i] = longer[i]*shorter[i%shorter_size]", "mul_bcast")
     
     # sum through the middle dimension (size_2) of a 3D matrix (size_1, size_2, size_3) 
     sum_axis = ElementwiseKernel("double *out, double *in, int size_1, int size_2", "out[i] += sum_axis_element(in, size_1, size_2, i)", "sum_axis",preamble="""