Difference between revisions of "SPO600 Vectorization Lab"
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[[Category:SPO600 Labs]] | [[Category:SPO600 Labs]] | ||
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{{Admon/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.}} | {{Admon/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 6 == | == Lab 6 == | ||
| − | # Write | + | # 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. |
| − | # Compile this program on [[SPO600 Servers#AArch64|aarchie]]. | + | # Compile this program on [[SPO600 Servers#AArch64|aarchie]] in such a way that the code is auto-vectorized. |
# Annotate the emitted code (i.e., obtain a dissassembly via <code>objdump -d</code> and add comments to the instructions in <code><main></code> explaining what the code does). | # Annotate the emitted code (i.e., obtain a dissassembly via <code>objdump -d</code> and add comments to the instructions in <code><main></code> explaining what the code does). | ||
# '''Write a blog post discussing your findings'''. Include: | # '''Write a blog post discussing your findings'''. Include: | ||
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#* Your annotated dissassembly listing | #* Your annotated dissassembly listing | ||
#* Your reflections on the experience and the results | #* Your reflections on the experience and the results | ||
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=== Resources === | === Resources === | ||
* [https://gcc.gnu.org/projects/tree-ssa/vectorization.html Auto-Vectorization in GCC] - Main project page for the GCC auto-vectorizer. | * [https://gcc.gnu.org/projects/tree-ssa/vectorization.html Auto-Vectorization in GCC] - Main project page for the GCC auto-vectorizer. | ||
* [http://locklessinc.com/articles/vectorize/ 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.''' | * [http://locklessinc.com/articles/vectorize/ 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.''' | ||
Revision as of 10:44, 12 February 2016
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.
This lab is designed to explore single instruction/multiple data (SIMD) vectorization, and the auto-vectorization capabilities of the GCC compiler.
Lab 6
- 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.
- Compile this program on aarchie in such a way that the code is auto-vectorized.
- Annotate the emitted code (i.e., obtain a dissassembly via
objdump -dand add comments to the instructions in<main>explaining what the code does). - 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
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.
