Understanding SIMD and SVE: Harnessing Parallelism for Enhanced Performance

 In this post, I would like to share the research about SIMD and SVE. In modern computing, achieving optimal performance often relies on parallel processing techniques like SIMD (Single Instruction, Multiple Data) and SVE (Scalable Vector Extensions).

Introduction

SIMD executes a single instruction across multiple data elements simultaneously, while SVE offers scalable vector lengths for dynamic adaptation to computational needs.

Evolution and Foundations

Originally designed to accelerate mathematical operations, SIMD and SVE leverage specialized instruction sets to minimize instruction overhead and maximize data throughput.

Real-World Impact

SIMD and SVE find applications in diverse fields such as image processing, data analytics, and machine learning, delivering substantial performance gains compared to traditional scalar processing.

Challenges and Future Directions

Despite their benefits, programming for SIMD and SVE requires expertise due to architectural complexities and compatibility issues. Future advancements may focus on integrating these technologies with emerging AI accelerators and optimizing compiler support.

Conclusion

SIMD and SVE technologies represent pivotal advancements in parallel processing, revolutionizing computational efficiency across various domains.

For further exploration:

Explore these resources for in-depth insights and practical guidance on leveraging SIMD and SVE effectively.

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Lab 1 - Calculate performance & memory usage

Bitmap Code    lda #$00 ; set a pointer in memory location $40 to point to $0200   sta $40 ; ... low byte ($00) goes in address $40   lda #$02   sta $41 ; ... high byte ($02) goes into address $41   lda #$07 ; colour number   ldy #$00 ; set index to 0  loop: sta ($40),y ; set pixel colour at the address (pointer)+Y   iny ; increment index   bne loop ; continue until done the page (256 pixels)   inc $41 ; increment the page   ldx $41 ; get the current page number   cpx #$06 ; compare with 6   bne loop ; continue until done all pages Calculate Performance - Time Performance                                         Cycle      Cycle Count     Alt Cycles     Alt Count     Total 1. lda #$00    ...
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Project Stage - 1 GCC for AArch64

Goal: The primary goal of this project is to add a functioning proof-of-concept prototype of auto-function-multi-versioning capability to the GNU Compiler Collection (GCC) for AArch64 systems. Building GCC on AArch64: Download GCC Source:   Start by cloning the latest GCC source code from the official GCC repository: sangwooshin@sinsang-us-MacBook-Pro % git clone git://gcc.gnu.org/git/gcc.git Cloning into 'gcc'... remote: Enumerating objects: 3044476, done. remote: Counting objects: 100% (224151/224151), done. remote: Compressing objects: 100% (13890/13890), done. remote: Total 3044476 (delta 219673), reused 210438 (delta 210250), pack-reused 2820325 Receiving objects: 100% (3044476/3044476), 1.16 GiB | 13.98 MiB/s, done. Resolving deltas: 100% (2506365/2506365), done. Updating files: 100% (134862/134862), done. Create Separate Build Directory: mkdir project cd project Configure GCC: Configure the build for AArch64: [sshin36@aarch64-001 project]$ ../configure --target=aarch64-l...
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Project stage-3 (testing & reflection)

Introduction In this blog, we will test and validate the prune-clones I made last time. In this process, I will explain how I modified the files passes.cc and passes.def, and why I made prune_clones.h separately because prune_clones.cc was not enough. passes.cc and passes.def modification process 1. Modify passes.cc The passes.cc file defines and implements several optimization passes for GCC, where you might want to add a prune-clones pass: Define and implement prune_clones related classes and functions. Clearly define what role this path plays in the overall optimization process of the compiler. For example, passes.cc needs to declare a prune_clones class and add code to register it with pass_manager. passes.cc 's main task is to initialize and manage each optimization pass. // passes.cc #include "gcc.h"     ... #include "prune_clones.h" // function generating prune_clones pass gimple_opt_pass *  make_pass_prune_clones (gcc::context *ctxt )   {     retur...
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