In this tutorial, we'll explain how to use the warp shfl operations to improve the performance.
## Introduction:
Let's talk about Warp first. The kernel code is executed in groups of fixed number of threads known as Warp. For nvidia WarpSize is 32 while for AMD, 32 for Polaris architecture and 64 for rest. Threads in a warp are referred to as lanes and are numbered from 0 to warpSize -1. With the help of shfl ops, we can directly exchange values of variable between threads without using any memory ops within a warp. There are four types of shfl ops:
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.
We will be using the Simple Matrix Transpose application from the previous tutorial and modify it to learn how to use shared memory.
## __shfl ops
In this tutorial, we'll use `__shfl()` ops. In the same sourcecode, we used for MatrixTranspose. We'll add the following:
` out[i*width + j] = __shfl(val,j*width + i);`
Be careful while using shfl operations, since all exchanges are possible between the threads of corresponding warp only.
## 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.
- [HIP Terminology](https://github.com/ROCm-Developer-Tools/HIP/blob/master/docs/markdown/hip_terms.md) (including Rosetta Stone of GPU computing terms across CUDA/HIP/HC/AMP/OpenL)
