Add readme for inline asm and unroll cookbook samples

Change-Id: I71b7a5652c3dad181c5df60ab0dd1b81d79f1bfb [ROCm/hip commit: 0154c97ddd]
2017-05-11 18:43:24 +05:30
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+## inline asm  ###
+
+This tutorial is about how to use inline GCN asm in kernel. In this tutorial, we'll explain how to by using the simple Matrix Transpose.
+
+## Introduction:
+
+If you want to take advantage of the extra performance benefits of writing in assembly as well as take advantage of special GPU hardware features that were only available through assemby, then this tutorial is for you. In this tutorial we'll be explaining how to start writing inline asm in kernel.
+
+For more insight Please read the following blogs by Ben Sander 
+[The Art of AMDGCN Assembly: How to Bend the Machine to Your Will](gpuopen.com/amdgcn-assembly)
+[AMD GCN Assembly: Cross-Lane Operations](http://gpuopen.com/amd-gcn-assembly-cross-lane-operations/)
+
+For more information:
+[AMD GCN3 ISA Architecture Manual](http://gpuopen.com/compute-product/amd-gcn3-isa-architecture-manual/)
+[User Guide for AMDGPU Back-end](llvm.org/docs/AMDGPUUsage.html)
+
+## Requirement:
+For hardware requirement and software installation [Installation](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/INSTALL.md) 
+
+## prerequiste knowledge:
+
+Programmers familiar with CUDA, OpenCL will be able to quickly learn and start coding with the HIP API. In case you are not, don't worry. You choose to start with the best one. We'll be explaining everything assuming you are completely new to gpgpu programming.
+
+## Simple Matrix Transpose 
+
+We will be using the Simple Matrix Transpose application from the our very first tutorial.
+
+## asm() Assembler statement
+
+We insert the GCN isa into the kernel using asm() Assembler statement. In the same sourcecode, we used for MatrixTranspose. We'll add the following:
+
+`  asm volatile ("v_mov_b32_e32 %0, %1" : "=v" (out[x*width + y]) : "v" (in[y*width + x]));                    `
+
+## How to build and run:
+Use the make command and execute it using ./exe
+Use hipcc to build the application, which is using hcc on AMD and nvcc on nvidia.
+
+
+## More Info:
+- [HIP FAQ](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_faq.md)
+- [HIP Kernel Language](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_kernel_language.md)
+- [HIP Runtime API (Doxygen)](http://gpuopen-professionalcompute-tools.github.io/HIP)
+- [HIP Porting Guide](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_porting_guide.md)
+- [HIP Terminology](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_terms.md) (including Rosetta Stone of GPU computing terms across CUDA/HIP/HC/AMP/OpenL)
+- [clang-hipify](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/clang-hipify/README.md)
+- [Developer/CONTRIBUTING Info](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/CONTRIBUTING.md)
+- [Release Notes](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/RELEASE.md)
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+## Using Pragma unroll ###
+
+In this tutorial, we'll explain how to use #pragma unroll to improve the performance. 
+
+## Introduction:
+
+Loop unrolling optimization hints can be specified with #pragma unroll and #pragma nounroll. The pragma is placed immediately before a for loop.
+Specifying #pragma unroll without a parameter directs the loop unroller to attempt to fully unroll the loop if the trip count is known at compile time and attempt to partially unroll the loop if the trip count is not known at compile time.
+
+## Requirement:
+For hardware requirement and software installation [Installation](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/INSTALL.md) 
+
+## prerequiste knowledge:
+
+Programmers familiar with CUDA, OpenCL will be able to quickly learn and start coding with the HIP API. In case you are not, don't worry. You choose to start with the best one. We'll be explaining everything assuming you are completely new to gpgpu programming.
+
+## Simple Matrix Transpose 
+
+For this tutorial we will be using MatrixTranspose with shfl operation i.e., our 4_shfl tutorial since it is the only examples where we used loops inside the kernel.
+
+In this tutorial, we'll use `#pragma unroll`. In the same sourcecode, we used for MatrixTranspose. We'll add it just before the for loop as following:
+
+`#pragma unroll                                                        `
+`    for(int i=0;i<width;i++)                                          `
+`    {                                                                 `
+`        for(int j=0;j<width;j++)                                      `
+`            out[i*width + j] = __shfl(val,j*width + i);               `
+`    }                                                                 `
+
+Specifying the optional parameter, #pragma unroll value, directs the unroller to unroll the loop value times. Be careful while using it.
+Specifying #pragma nounroll indicates that the loop should not be unroll. #pragma unroll 1 will show the same behaviour.
+
+## How to build and run:
+Use the make command and execute it using ./exe
+Use hipcc to build the application, which is using hcc on AMD and nvcc on nvidia.
+
+## requirement for nvidia
+please make sure you have a 3.0 or higher compute capable device in order to use warp shfl operations and add `-gencode arch=compute=30, code=sm_30` nvcc flag in the Makefile while using this application.
+
+## More Info:
+- [HIP FAQ](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_faq.md)
+- [HIP Kernel Language](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_kernel_language.md)
+- [HIP Runtime API (Doxygen)](http://gpuopen-professionalcompute-tools.github.io/HIP)
+- [HIP Porting Guide](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_porting_guide.md)
+- [HIP Terminology](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_terms.md) (including Rosetta Stone of GPU computing terms across CUDA/HIP/HC/AMP/OpenL)
+- [clang-hipify](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/clang-hipify/README.md)
+- [Developer/CONTRIBUTING Info](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/CONTRIBUTING.md)
+- [Release Notes](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/RELEASE.md)