Clean up Riemann fit in CA (#178)

Reduce the number of blocks used to launch the Riemann fit kernels within the CA.
Rename the kernels to avoid the ambiguiity with the standalone Riemann fit.
Work around spurious warnings in the Eigen test.

RecoPixelVertexing/PixelTrackFitting/test/testEigenGPUNoFit.cu

@@ -83,15 +83,18 @@ void testMultiply() {
   kernelMultiply<<<1,1>>>(JGPU, CGPU, multiply_resultGPU);
   cudaDeviceSynchronize();
 
-  cudaMemcpy(multiply_resultGPUret, multiply_resultGPU, 
+  cudaMemcpy(multiply_resultGPUret, multiply_resultGPU,
       sizeof(Eigen::Matrix<double, row1, col2>), cudaMemcpyDeviceToHost);
   printIt(multiply_resultGPUret);
   assert(isEqualFuzzy(multiply_result, (*multiply_resultGPUret)));
 }
 
 void testInverse3x3() {
   std::cout << "TEST INVERSE 3x3" << std::endl;
-  Matrix3d m = Matrix3d::Random();
+  Matrix3d m;
+  fillMatrix(m);
+  m += m.transpose().eval();
+
   Matrix3d m_inv = m.inverse();
   Matrix3d *mGPU = nullptr;
   Matrix3d *mGPUret = nullptr;
@@ -117,7 +120,10 @@ void testInverse3x3() {
 
 void testInverse4x4() {
   std::cout << "TEST INVERSE 4x4" << std::endl;
-  Matrix4d m = Matrix4d::Random();
+  Matrix4d m;
+  fillMatrix(m);
+  m += m.transpose().eval();
+
   Matrix4d m_inv = m.inverse();
   Matrix4d *mGPU = nullptr;
   Matrix4d *mGPUret = nullptr;
@@ -143,9 +149,10 @@ void testInverse4x4() {
 
 void testEigenvalues() {
   std::cout << "TEST EIGENVALUES" << std::endl;
-  Matrix3d m = Matrix3d::Random();
-  Matrix3d mt = m.transpose();
-  m += mt;
+  Matrix3d m;
+  fillMatrix(m);
+  m += m.transpose().eval();
+
   Matrix3d * m_gpu = nullptr;
   Matrix3d * mgpudebug = new Matrix3d();
   Eigen::SelfAdjointEigenSolver<Matrix3d>::RealVectorType *ret = new Eigen::SelfAdjointEigenSolver<Matrix3d>::RealVectorType;