From 69ed46c8da69ee18338eca5102ead43410cc01b5 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Fri, 2 Feb 2018 21:18:37 +0100
Subject: Implemented the XHAD Hadamard product routine

---
 src/routines/levelx/xhad.cpp | 58 +++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 57 insertions(+), 1 deletion(-)

(limited to 'src/routines')

diff --git a/src/routines/levelx/xhad.cpp b/src/routines/levelx/xhad.cpp
index 46ae8031..da416cc7 100644
--- a/src/routines/levelx/xhad.cpp
+++ b/src/routines/levelx/xhad.cpp
@@ -24,7 +24,7 @@ template <typename T>
 Xhad<T>::Xhad(Queue &queue, EventPointer event, const std::string &name):
     Routine(queue, event, name, {"Xaxpy"}, PrecisionValue<T>(), {}, {
 #include "../../kernels/level1/level1.opencl"
-#include "../../kernels/level1/xaxpy.opencl"
+#include "../../kernels/level1/xhad.opencl"
     }) {
 }
 
@@ -45,6 +45,62 @@ void Xhad<T>::DoHad(const size_t n, const T alpha,
   TestVectorY(n, y_buffer, y_offset, y_inc);
   TestVectorY(n, z_buffer, z_offset, z_inc); // TODO: Make a TestVectorZ function with error codes
 
+  // Determines whether or not the fast-version can be used
+  const auto use_faster_kernel = (x_offset == 0) && (x_inc == 1) &&
+                                 (y_offset == 0) && (y_inc == 1) &&
+                                 (z_offset == 0) && (z_inc == 1) &&
+                                 IsMultiple(n, db_["WPT"]*db_["VW"]);
+  const auto use_fastest_kernel = use_faster_kernel &&
+                                  IsMultiple(n, db_["WGS"]*db_["WPT"]*db_["VW"]);
+
+  // If possible, run the fast-version of the kernel
+  const auto kernel_name = (use_fastest_kernel) ? "XhadFastest" :
+                           (use_faster_kernel) ? "XhadFaster" : "Xhad";
+
+  // Retrieves the Xhad kernel from the compiled binary
+  auto kernel = Kernel(program_, kernel_name);
+
+  // Sets the kernel arguments
+  if (use_faster_kernel || use_fastest_kernel) {
+    kernel.SetArgument(0, static_cast<int>(n));
+    kernel.SetArgument(1, GetRealArg(alpha));
+    kernel.SetArgument(2, GetRealArg(beta));
+    kernel.SetArgument(3, x_buffer());
+    kernel.SetArgument(4, y_buffer());
+    kernel.SetArgument(5, z_buffer());
+  }
+  else {
+    kernel.SetArgument(0, static_cast<int>(n));
+    kernel.SetArgument(1, GetRealArg(alpha));
+    kernel.SetArgument(2, GetRealArg(beta));
+    kernel.SetArgument(3, x_buffer());
+    kernel.SetArgument(4, static_cast<int>(x_offset));
+    kernel.SetArgument(5, static_cast<int>(x_inc));
+    kernel.SetArgument(6, y_buffer());
+    kernel.SetArgument(7, static_cast<int>(y_offset));
+    kernel.SetArgument(8, static_cast<int>(y_inc));
+    kernel.SetArgument(9, z_buffer());
+    kernel.SetArgument(10, static_cast<int>(z_offset));
+    kernel.SetArgument(11, static_cast<int>(z_inc));
+  }
+
+  // Launches the kernel
+  if (use_fastest_kernel) {
+    auto global = std::vector<size_t>{CeilDiv(n, db_["WPT"]*db_["VW"])};
+    auto local = std::vector<size_t>{db_["WGS"]};
+    RunKernel(kernel, queue_, device_, global, local, event_);
+  }
+  else if (use_faster_kernel) {
+    auto global = std::vector<size_t>{Ceil(CeilDiv(n, db_["WPT"]*db_["VW"]), db_["WGS"])};
+    auto local = std::vector<size_t>{db_["WGS"]};
+    RunKernel(kernel, queue_, device_, global, local, event_);
+  }
+  else {
+    const auto n_ceiled = Ceil(n, db_["WGS"]*db_["WPT"]);
+    auto global = std::vector<size_t>{n_ceiled/db_["WPT"]};
+    auto local = std::vector<size_t>{db_["WGS"]};
+    RunKernel(kernel, queue_, device_, global, local, event_);
+  }
 }
 
 // =================================================================================================
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