SPO600 Vectorization Lab

Purpose of this Lab
This lab is designed to explore single instruction/multiple data (SIMD) vectorization, and the auto-vectorization capabilities of the GCC compiler.

Lab 5

1. Write a short program that creates two 1000-element integer arrays and fills them with random numbers, then sums those two arrays to a third array, and finally sums the third array to a long int and prints the result.
2. Compile this program on aarchie in such a way that the code is auto-vectorized.
3. Annotate the emitted code (i.e., obtain a dissassembly via objdump -d and add comments to the instructions in <main> explaining what the code does).
4. Review the vector instructions for AArch64. Find a way to scale an array of sound samples (see Lab 5) by a factor between 0.000-1.000 using SIMD. (Note: you may need to convert some data types). You DO NOT need to code this solution (but feel free if you want to!).
5. Write a blog post discussing your findings. Include:
   • The source code
   • The compiler command line used to build the code
   • Your annotated dissassembly listing
   • Your reflections on the experience and the results
   • Your proposed volume-sampling-via-SIMD solution.

Resources

• Auto-Vectorization in GCC - Main project page for the GCC auto-vectorizer.
• Auto-vectorization with gcc 4.7 - An excellent discussion of the capabilities and limitations of the GCC auto-vectorizer, intrinsics for providing hints to GCC, and other code pattern changes that can improve results. Note that there has been some improvement in the auto-vectorizer since this article was written. This article is strongly recommended.
• Intel (Auto)Vectorization Tutorial - this deals with the Intel compiler (ICC) but the general technical discussion is valid for other compilers such as gcc and llvm