COMPMID-734: CLTuner rework

Change-Id: I8f20d6ea8a09869d71003e7b08e0d33775282f6c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125802
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 0 insertions, 73 deletions

diff --git a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
index 56ac0c7250..b5526c4fca 100644
--- a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
+++ b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
@@ -315,79 +315,6 @@ void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICL
 
         kernel_name << "_f32_bifrost";
         _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), build_options.options()));
-
-        // Through extensive experimentation with over 30 representative tensor
-        // shapes, we found a small number of local work size configurations
-        // that result in nearly optimal execution times. Selecting the right
-        // lws for a given shape, however, required a complex decision tree,
-        // until we constructed a simple feature as described below.
-        //
-        // We started from the number of multiply-accumulate operations for a
-        // convolution layer, which is equal to the product of the input
-        // dimensions 0..2 and the weights dimensions 0..2.  Unfortunately,
-        // this resulted in ties between distinct shapes that required distinct
-        // lws configurations. Replacing the width of the input with the kernel
-        // size, however, resulted in nearly optimal predictions. We use underscores
-        // in variable names to indicate when they are intentionally misleading.
-        const size_t product_of_weights_dimensions = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2);
-        const size_t product_of_input_dimensions_  = input->info()->dimension(0) * weights->info()->dimension(1) * input->info()->dimension(2);
-        const float  mega_ops_                     = 1e-6 * product_of_weights_dimensions * product_of_input_dimensions_;
-
-        switch(kernel_size)
-        {
-            case 1:
-            {
-                if(mega_ops_ < 1.f)
-                {
-                    _lws_hint = cl::NDRange(1, 1, 8);
-                }
-                else if(mega_ops_ < 7.f)
-                {
-                    _lws_hint = cl::NDRange(1, 1, 4);
-                }
-                else
-                {
-                    _lws_hint = cl::NDRange(1, 1, 2);
-                }
-                break;
-            }
-            case 3:
-            {
-                if(mega_ops_ < 1.f)
-                {
-                    _lws_hint = cl::NDRange(1, 1, 8);
-                }
-                else if(mega_ops_ < 13.f)
-                {
-                    _lws_hint = cl::NDRange(2, 1, 4);
-                }
-                else if(mega_ops_ < 50.f)
-                {
-                    _lws_hint = cl::NDRange(3, 1, 4);
-                }
-                else
-                {
-                    _lws_hint = cl::NDRange(2, 1, 6);
-                }
-                break;
-            }
-            case 5:
-            {
-                if(mega_ops_ < 2.f || mega_ops_ > 80.f)
-                {
-                    _lws_hint = cl::NDRange(2, 1, 4);
-                }
-                else
-                {
-                    _lws_hint = cl::NDRange(2, 1, 8);
-                }
-                break;
-            }
-            default:
-            {
-                ARM_COMPUTE_ERROR("Kernel size not optimized for Bifrost");
-            }
-        }
     }
     else
     {