1 files changed, 172 insertions, 0 deletions
diff --git a/src/routines/level3/xgemm.cc b/src/routines/level3/xgemm.cc
new file mode 100644
index 00000000..f4a9f737
--- /dev/null
+++ b/src/routines/level3/xgemm.cc
@@ -0,0 +1,172 @@
+
+// =================================================================================================
+// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
+// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
+// width of 100 characters per line.
+//
+// Author(s):
+//   Cedric Nugteren <www.cedricnugteren.nl>
+//
+// This file implements the Xgemm class (see the header for information about the class).
+//
+// =================================================================================================
+
+#include "internal/routines/level3/xgemm.h"
+
+#include <string>
+#include <vector>
+
+namespace clblast {
+// =================================================================================================
+
+// Specific implementations to get the memory-type based on a template argument
+template <> const Precision Xgemm<float>::precision_ = Precision::kSingle;
+template <> const Precision Xgemm<double>::precision_ = Precision::kDouble;
+template <> const Precision Xgemm<float2>::precision_ = Precision::kComplexSingle;
+template <> const Precision Xgemm<double2>::precision_ = Precision::kComplexDouble;
+
+// =================================================================================================
+
+// Constructor: forwards to base class constructor
+template <typename T>
+Xgemm<T>::Xgemm(CommandQueue &queue, Event &event):
+    Routine(queue, event, {"Copy", "Pad", "Transpose", "PadTranspose", "Xgemm"}, precision_) {
+}
+
+// =================================================================================================
+
+// The main routine
+template <typename T>
+StatusCode Xgemm<T>::DoGemm(const Layout layout,
+                            const Transpose a_transpose, const Transpose b_transpose,
+                            const size_t m, const size_t n, const size_t k,
+                            const T alpha,
+                            const Buffer &a_buffer, const size_t a_offset, const size_t a_ld,
+                            const Buffer &b_buffer, const size_t b_offset, const size_t b_ld,
+                            const T beta,
+                            const Buffer &c_buffer, const size_t c_offset, const size_t c_ld) {
+
+  // Makes sure all dimensions are larger than zero
+  if ((m == 0) || (n == 0) || (k == 0)) { return StatusCode::kInvalidDimension; }
+
+  // Computes whether or not the matrices are transposed in memory. This is based on their layout
+  // (row or column-major) and whether or not they are requested to be pre-transposed. Note
+  // that the Xgemm kernel expects either matrices A and C (in case of row-major) or B (in case of
+  // col-major) to be transformed, so transposing requirements are not the same as whether or not
+  // the matrix is actually transposed in memory.
+  auto a_rotated = (layout == Layout::kColMajor && a_transpose != Transpose::kNo) ||
+                   (layout == Layout::kRowMajor && a_transpose == Transpose::kNo);
+  auto b_rotated = (layout == Layout::kColMajor && b_transpose != Transpose::kNo) ||
+                   (layout == Layout::kRowMajor && b_transpose == Transpose::kNo);
+  auto c_rotated = (layout == Layout::kRowMajor);
+  auto a_do_transpose =  a_rotated;
+  auto b_do_transpose = !b_rotated;
+  auto c_do_transpose =  c_rotated;
+
+  // In case of complex data-types, the transpose can also become a conjugate transpose
+  auto a_conjugate = (a_transpose == Transpose::kConjugate);
+  auto b_conjugate = (b_transpose == Transpose::kConjugate);
+
+  // Computes the first and second dimensions of the 3 matrices taking into account whether the
+  // matrices are rotated or not
+  auto a_one = (a_rotated) ? k : m;
+  auto a_two = (a_rotated) ? m : k;
+  auto b_one = (b_rotated) ? n : k;
+  auto b_two = (b_rotated) ? k : n;
+  auto c_one = (c_rotated) ? n : m;
+  auto c_two = (c_rotated) ? m : n;
+
+  // Tests three matrices (A, B, C) for validity, first from a perspective of the OpenCL buffers and
+  // their sizes, and then from a perspective of parameter values (e.g. m, n, k). Tests whether the
+  // OpenCL buffers are valid and non-zero and whether the OpenCL buffers have sufficient storage
+  // space. Also tests that the leading dimensions of:
+  //    matrix A cannot be less than K when rotated, or less than M when not-rotated
+  //    matrix B cannot be less than N when rotated, or less than K when not-rotated
+  //    matrix C cannot be less than N when rotated, or less than M when not-rotated
+  auto status = TestMatrixA(a_one, a_two, a_buffer, a_offset, a_ld, sizeof(T));
+  if (ErrorIn(status)) { return status; }
+  status = TestMatrixB(b_one, b_two, b_buffer, b_offset, b_ld, sizeof(T));
+  if (ErrorIn(status)) { return status; }
+  status = TestMatrixC(c_one, c_two, c_buffer, c_offset, c_ld, sizeof(T));
+  if (ErrorIn(status)) { return status; }
+
+  // Calculates the ceiled versions of m, n, and k
+  auto m_ceiled = Ceil(m, db_["MWG"]);
+  auto n_ceiled = Ceil(n, db_["NWG"]);
+  auto k_ceiled = Ceil(k, db_["KWG"]);
+
+  // Allocates space on the device for padded and/or transposed input and output matrices.
+  try {
+    auto temp_a = Buffer(context_, CL_MEM_READ_WRITE, k_ceiled*m_ceiled*sizeof(T));
+    auto temp_b = Buffer(context_, CL_MEM_READ_WRITE, k_ceiled*n_ceiled*sizeof(T));
+    auto temp_c = Buffer(context_, CL_MEM_READ_WRITE, m_ceiled*n_ceiled*sizeof(T));
+
+    // Loads the program from the database
+    auto& program = GetProgramFromCache();
+
+    // Runs the pre-processing kernels. This transposes the matrices, but also pads zeros to fill
+    // them up until they reach a certain multiple of size (kernel parameter dependent).
+    status = PadCopyTransposeMatrix(a_one, a_two, a_ld, a_offset, a_buffer,
+                                    m_ceiled, k_ceiled, m_ceiled, 0, temp_a,
+                                    a_do_transpose, a_conjugate, true, false, false, false, program);
+    if (ErrorIn(status)) { return status; }
+    status = PadCopyTransposeMatrix(b_one, b_two, b_ld, b_offset, b_buffer,
+                                    n_ceiled, k_ceiled, n_ceiled, 0, temp_b,
+                                    b_do_transpose, b_conjugate, true, false, false, false, program);
+    if (ErrorIn(status)) { return status; }
+
+    // Only necessary for matrix C if it used both as input and output
+    if (beta != static_cast<T>(0)) {
+      status = PadCopyTransposeMatrix(c_one, c_two, c_ld, c_offset, c_buffer,
+                                      m_ceiled, n_ceiled, m_ceiled, 0, temp_c,
+                                      c_do_transpose, false, true, false, false, false, program);
+      if (ErrorIn(status)) { return status; }
+    }
+
+    // Retrieves the Xgemm kernel from the compiled binary
+    try {
+      auto kernel = Kernel(program, "Xgemm");
+
+      // Sets the kernel arguments
+      kernel.SetArgument(0, static_cast<int>(m_ceiled));
+      kernel.SetArgument(1, static_cast<int>(n_ceiled));
+      kernel.SetArgument(2, static_cast<int>(k_ceiled));
+      kernel.SetArgument(3, alpha);
+      kernel.SetArgument(4, beta);
+      kernel.SetArgument(5, temp_a());
+      kernel.SetArgument(6, temp_b());
+      kernel.SetArgument(7, temp_c());
+
+      // Computes the global and local thread sizes
+      auto global = std::vector<size_t>{
+        (m_ceiled * db_["MDIMC"]) / db_["MWG"],
+        (n_ceiled * db_["NDIMC"]) / db_["NWG"]
+      };
+      auto local = std::vector<size_t>{db_["MDIMC"], db_["NDIMC"]};
+
+      // Launches the kernel
+      status = RunKernel(kernel, global, local);
+      if (ErrorIn(status)) { return status; }
+
+      // Runs the post-processing kernel
+      status = PadCopyTransposeMatrix(m_ceiled, n_ceiled, m_ceiled, 0, temp_c,
+                                      c_one, c_two, c_ld, c_offset, c_buffer,
+                                      c_do_transpose, false, false, false, false, false, program);
+      if (ErrorIn(status)) { return status; }
+
+      // Successfully finished the computation
+      return StatusCode::kSuccess;
+    } catch (...) { return StatusCode::kInvalidKernel; }
+  } catch (...) { return StatusCode::kTempBufferAllocFailure; }
+}
+
+// =================================================================================================
+
+// Compiles the templated class
+template class Xgemm<float>;
+template class Xgemm<double>;
+template class Xgemm<float2>;
+template class Xgemm<double2>;
+
+// =================================================================================================
+} // namespace clblast