Markdown fixes & Whitespace cleanup for samples (#1096)

* Fix multiline code blocks in README's * Whitespace cleanup [ROCm/hip commit: 5257b54a39]
2019-05-12 08:57:44 -05:00
parent 2302345dd5
commit 376e19308b
9 changed files with 103 additions and 77 deletions
@@ -7,13 +7,13 @@ Earlier we learned how to use static shared memory. In this tutorial, we'll expl
 As we mentioned earlier  that Memory bottlenecks is the main problem why we are not able to get the highest performance, therefore minimizing the latency for memory access plays prominent role in application optimization. In this tutorial, we'll learn how to use dynamic shared memory.

 ## Requirement:
-For hardware requirement and software installation [Installation](https://github.com/ROCm-Developer-Tools/HIP/INSTALL.md) 
+For hardware requirement and software installation [Installation](https://github.com/ROCm-Developer-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 
+## Simple Matrix Transpose

 We will be using the Simple Matrix Transpose application from the previous tutorial and modify it to learn how to use shared memory.

@@ -25,11 +25,13 @@ Shared memory is way more faster than that of global and constant memory and acc
 here the first parameter is the data type while the second one is the variable name.

 The other important change is:
-`  hipLaunchKernelGGL(matrixTranspose,                                                   `
+```
+    hipLaunchKernelGGL(matrixTranspose,
                  dim3(WIDTH/THREADS_PER_BLOCK_X, WIDTH/THREADS_PER_BLOCK_Y),
                  dim3(THREADS_PER_BLOCK_X, THREADS_PER_BLOCK_Y),
                  sizeof(float)*WIDTH*WIDTH, 0,
                  gpuTransposeMatrix , gpuMatrix, WIDTH);
+```
 here we replaced 4th parameter with amount of additional shared memory to allocate when launching the kernel.

 ## How to build and run: