SPO-Project concepts and tools.(Part 2)

In this blog I will discuss the concept of Auto vectorization, SIMD, SVE and SVE2. This are the core concept of software optimization.

Auto-vectorization:

In early days, computers used to have one logic unit that was capable of executing one instruction on one pair of operands at a given time. For this reason computer programs and languages were built to execute sequentially. However modern computers have the capability to perform many task at a time. There are many optimizing compilers who perform automatic vectorization which enables to do some parallel operations where possible instead of only sequential operations. This concept is called vector implementation which can process one operation in multiple pair of operands at a given time.

For AArch64 system there are three extensions for the Auto vectorization. These are SIMD, SVE, SVE2.

For Auto vectorization to be in effect the flags need to be used. This includes –O3, -ftree-vectorize etc.

For more detailed explanation with examples of codes you can follow this link

SIMD:

SIMD is one type of extension for auto vectorization. It stands for Single instruction multiple data. As the name suggest it enables processing of multiple data with a single instruction instead of the conventional sequential approach where one data is process at a time. SIMD operations cannot be used while processing multiple data in different ways.

We can build the program on armv8 system using the following command:

gcc –g –O3 –c march=armv8-a

For more detailed explanation with examples of operation you can follow this link

SVE and SVE2:

SVE stands for Scalable vector extension. Whereas SVE2 is just the armv9 extension of it which is practically not available to any system as of now. SVE is a new SIMD instruction set which is used as an extension to AArch64 in order to allow flexible vector length implementation. SVE2 is the combination of SVE and Neon. SVE2 has more functional domain in terms of data level parallelism.

The main difference between SVE and SVE2 is in functional coverage of the instruction set. While SVE was designed for HPC and ML applications, SVE2 has the capability of data processing beyond this applications.

To use the SVE capability the following command should be used:

gcc –g –O3 –c march=armv8-a+sve

To use the SVE2 capability the following command should be used:

gcc –g –O3 –c march=armv8-a+sve2

One interesting thing to note is that, for the older system which does not support SVE or SVE2 capability we can use emulator for the part only which requires that capability. To use the emulator we can use:

qemu-aarch64

For further detailed learning you can follow this link

Comments

Post a Comment

Popular posts from this blog

SPO600-Lab2 (Experimenting with 6502 emulator)

Image
In this lab, I have been given a bitmap code which will fill the emulator's bitmap display with the yellow color. Later I was given some task to modify the code in such a way to get different output color for the entire display. I was also asked to show four different color in four different pages. In this display one quarter is refer to as one page. I was encouraged to do some further experiment then. So I will start from the very first task. I assembled and run the initial code on the emulator and below is my raw code and output. 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
Read more

Naziur Rahman khan Fall 2022 SPO600 Project (Algorithm-part1)

  Implementation of ifunc-capable Software in Python. Python interpreter has a great module called ifunc, which provides multi-threaded, compile-time function selection. It replaces some built-in functions with custom implementations at compile time. This allows the replacement function to specialize on a particular type or set of types. ifunc is implemented as an extension to the CPython interpreter and can be used with any language that can be compiled into Python bytecode, like Cython and PyPy. This blog post will show how useful it is to use ifunc-capable software in Python. We will also give some examples of code that uses ifunc. Plan for the project This project aims to develop an understanding of the implementation of ifunc-capable software in Python. If you're a Python programmer, then you know that ifunc is a powerful tool that can be used to great effect in your programs. However, implementing ifunc can be tricky, and it often needs to be clarified how to get starte
Read more

SPO600-Lab 2 (Continued)

Image
 Experiments: Now I will perform some additional experiments which was asked for the lab. First I will add a instruction tya in the loop of the code. See the output below that I got from it. Here instruction tya means transfer y to a. Here I can see 11 colors on the display.  Now I was told to add instruction lsr after tya as like 'tya:lsr' . Let's see the output on the below screenshot: Experimenting with the emulator helping me gradually to adapt with this language. I will continue my experiments and post more blogs of these later.
Read more