Winter 2022 SPO600 Weekly Schedule

This is the schedule and main index page for the SPO600 Software Portability and Optimization course for Winter 2022.

This page may be obsolete.
It contains historical information. For current information, please see Current SPO600 Weekly Schedule.

Schedule Summary Table

Please follow the links in each cell for additional detail which will be added below as the course proceeds -- especially for the Deliverables column.

Week	Week of...	Class I Tuesday 11:40-1:30	Class II Friday 9:50-11:40	Deliverables (Summary - click for details)
1	Jan 10	Introduction to the Course / Introduction to the Problem / Computer Architecture Basics	Binary Representation of Data	Set up for the course / Lab 1
2	Jan 17	Introduction to 6502 Assembly	Writing and Debugging 6502 Code / Assembly Language Conventions / Using Macros Effectively	Lab 2
3	Jan 24	6502 Math / Jumps, Branches, and Subroutines	6502 Strings	Lab 3
4	Jan 31	Compiler Optimizations	(No lecture - continue work on Lab 3)	Lab 3, January blog posts
5	Feb 07	Building Code / Make and Makefiles / Autotools and Friends	Introduction to 64-bit Architectures - Registers (x86_64 and AArch64) / Memory Issues	Lab 3
6	Feb 14	64-Bit Assembly Language - Part 1	64-Bit Assembly Language - Part 2	Lab 4
7	Feb 21	Optimization Trade-Off / Algorithm Selection / Benchmarking	SIMD / Algorithm Selection	Lab 5
Reading	Oct 25	Reading Week		February Blog Posts due at 11:59 pm Feb 28
8	Mar 07	Sclable Vector Extensions v2 (SVE2)	Project Stage 1	Lab 6
9	Mar 14	Project Discussion 1	---	Blog about project work
10	Mar 21	Project Discussion 2	GCC and SVE2	Blog about project work
11	Mar 28	Project Discussion 3	SVE2 Examples	Project Stage 1 (March 28), March Blog Posts (March 31)
12	Apr 04	Demo/discussion of SVE 2 Examples	SVE2 Examples Part 2	Blog about project work
13	Apr 11	Memory Systems	Good Friday	Blog about project work
14	Apr 18	Future Directions in Architecture	Project Stage 3	Project Stage 3, April Blog Posts

Week 1

Week 1 - Class I

Video

• Recording of Week 1 Class I (January 11) - with light editing to correct some of the sound and video issues.

Introduction to the Problems

Porting and Portability

• Most software is written in a high-level language which can be compiled into machine code for a specific computer architecture. In many cases, this code can be compiled for multiple architectures. However, there is a lot of existing code that contains some architecture-specific code fragments which contains assumptions based on the architecture written in architecture-specific high-level code or in Assembly Language.
• Reasons that code is architecture-specific:
  • System assumptions that don't hold true on other platforms
    • Variable or word size
    • Endianness
  • Code that takes advantage of platform-specific features
• Reasons for writing code in Assembly Langauge include:
  • Performance
  • Atomic Operations
  • Direct access to hardware features, e.g., CPUID registers
• Most of the historical reasons for including assembler are no longer valid. Modern compilers can out-perform most hand-optimized assembly code, atomic operations can be handled by libraries or compiler intrinsics, and most hardware access should be performed through the operating system or appropriate libraries.
• A new architecture has appeared: AArch64, which is part of ARMv8. This is the first new computer architecture to appear in several years (at least, the first mainstream computer architecture).
• At this point, most key open source software (the software typically present in a Linux distribution such as Ubuntu or Fedora, for example) now runs on AArch64. However, it may not run as well as on older architectures (such as x86_64).

Benchmarking and Profiling

Benchmarking involves testing software performance under controlled conditions so that the performance can be compared to other software, the same software operating on other types of computers, or so that the impact of a change to the software can be gauged.

Profiling is the process of analyzing software performance on finer scale, determining resource usage per program part (typically per function/method). This can identify software bottlenecks and potential targets for optimization.

Optimization

Optimization is the process of evaluating different ways that software can be written or built and selecting the option that has the best performance tradeoffs.

Optimization may involve substituting software algorithms, altering the sequence of operations, using architecture-specific code, or altering the build process. It is important to ensure that the optimized software produces correct results and does not cause an unacceptable performance regression for other use-cases, system configurations, operating systems, or architectures.

The definition of "performance" varies according to the target system and the operating goals. For example, in some contexts, low memory or storage usage is important; in other cases, fast operation; and in other cases, low CPU utilization or long battery life may be the most important factor. It is often possible to trade off performance in one area for another; using a lookup table, for example, can reduce CPU utilization and improve battery life in some algorithms, in return for increased memory consumption.

Most advanced compilers perform some level of optimization, and the options selected for compilation can have a significant effect on the trade-offs made by the compiler, affecting memory usage, execution speed, executable size, power consumption, and debuggability.

Build Process

Building software is a complex task that many developers gloss over. The simple act of compiling a program invokes a process with five or more stages, including pre-proccessing, compiling, optimizing, assembling, and linking. However, a complex software system will have hundreds or even thousands of source files, as well as dozens or hundreds of build configuration options, auto configuration scripts (cmake, autotools), build scripts (such as Makefiles) to coordinate the process, test suites, and more.

The build process varies significantly between software packages. Most software distribution projects (including Linux distributions such as Ubuntu and Fedora) use a packaging system that further wraps the build process in a standardized script format, so that different software packages can be built using a consistent process.

In order to get consistent and comparable benchmark results, you need to ensure that the software is being built in a consistent way. Altering the build process is one way of optimizing software.

Note that the build time for a complex package can range up to hours or even days!

General Course Information

• Course resources are linked from the CDOT wiki, starting at https://wiki.cdot.senecacollege.ca/wiki/SPO600 (Quick find: This page will usually be Google's top result for a search on "SPO600").
• Coursework is submitted by blogging.
• Quizzes will be short (1 page) and will be held without announcement at the start of any synchronous class. There is no opportunity to re-take a missed quiz, but your lowest three quiz scores will not be counted, so do not worry if you miss one or two.
  • Students with test accommodations: an alternate monthly quiz can be made available via the Test Centre. See your professor for details.
• Course marks (see Weekly Schedule for dates):
  • 60% - Project Deliverables
  • 20% - Communication (Blog and Wiki writing)
  • 20% - Labs and Quizzes (10% labs - completed/not completed; 10% for quizzes - lowest 3 scores not counted)

Classes

• Tuesday: synchronous (live) classes on Big Blue Button at 11:40 am - login to learn.senecacollege.ca ("Blackboard"), go to SPO600, and select the "Tuesday Classes" option on the left-hand menu.
• Friday: these classes will usually be asynchronous (pre-recorded) - see this page for details each week.

Course Setup

Follow the instructions on the SPO600 Communication Tools page to set up a blog, create SSH keys, and send your blog URLs and public key to me.

Once this information has been submitted, I will:

1. Update the Current SPO600 Participants page with your information, and
2. Create an account for you on the SPO600 Servers.

This updating is done in batches once or twice a week -- allow some time!

How open source communities work

• Do the Code Review Lab (Lab 1) as homework.

Week 1 - Class II

Video

• SPO600 - Binary Representation of Data

Binary Representation of Data

• Binary
  • Binary is a system which uses "bits" (binary digits) to represent values.
  • Each bit has one of two values, signified by the symbols 0 and 1. These correspond to:
    • Electrically: typically off/on, or low/high voltage, or low/high current. Many other electrical representations are possible.
    • Logically: false or true.
  • Binary numbers are resistant to errors, especially when compared to other systems such as analog voltages.
    • To represent the numbers 0-10 as an analog electical value, we could use a voltage from 0 - 10 volts. However, if we use a long cable, there will be signal loss and the voltage will drop: we could apply 10 volts on one end of the cable, but only observe (say) 9.1 volts on the other end of the cable. Alternately, electromagnetic interference from nearby devices could slightly increase the signal.
    • If we use instead use the same voltages and cable length to carry a binary signal, where 0 volts == off == "0" and 10 volts == on == "1", a signal that had degraded from 10 volts to 9.1 volts would still be counted as a "1" and a 0 volt signal with some stray electromagnetic interference presenting as (say) 0.4 volts would still be counted as "0". However, we will need to use multiple bits to carry larger numbers -- either in parallel (multiple wires side-by-side), or sequentially (multiple bits presented over the same wire in sequence).
• Integers
  • Integers are the basic building block of binary numbering schemes.
  • In an unsigned integer, the bits are numbered from right to left starting at 0, and the value of each bit is 2bit. The value represented is the sum of each bit multiplied by its corresponding bit value. The range of an unsigned integer is 0:2bits-1 where bits is the number of bits in the unsigned integer.
  • Signed integers are generally stored in twos-complement format, where the highest bit is used as a sign bit. If that bit is set, the value represented is -(!value)-1 where ! is the NOT operation (each bit gets flipped from 0→1 and 1→0)
• Fixed-point
  • A fixed-point value is encoded the same as an integer, except that some of the bits are fractional -- they're considered to be to the right of the "binary point" (binary version of "decimal point" - or more generically, the radix point). For example, binary 000001.00 is decimal 1.0, and 000001.11 is decimal 1.75.
  • An alternative to fixed-point values is integer values in a smaller unit of measurement. For example, some accounting software may use integer values representing cents. For input and display purposes, dollar and cent values are converted to/from cent values.
• Floating-point
  • The most commonly-used floating point formats are defined in the IEEE 754 standard.
  • IEEE754 floating point numbers have three parts: a sign bit (0 for positive, 1 for negative), a mantissa or significand, and an exponent. The significand has an implied 1 and radix point preceeding the stored value. The exponent is stored as an unsigned integer to which a bias value has been added; the bias value is 2^{(number of exponent bits - 1)} - 1. The floating point value is interpreted in normal cases as sign mantissa * 2(exponent - bias). Exponent values which are all-zeros or all-ones encode four categories of special cases: zero, infinity, Not a Number (NaN), and subnormal numbers (numbers which are close to zero, where the significand does not have an implied 1 to the left of the radix point); in these special cases, the sign bit and significand values may have special meanings.
  • There are some new floating-point formats appearing, such as Brain Float 16, a 16-bit format with the same dynamic range as 32-bit IEEE 754 floating point but with less accuracy, intended for use in machine learning applications.
• Characters
  • Characters are encoded as integers, where each integer corresponds to one "code point" in a character table (e.g., code 65 in ASCII corresponds to the character "A").
  • Historically, many different coding schemes have been used, but the two most common ones were the American Standard Code for Information Interchange (ASCII), and Extended Binary Coded Decimal Interchange Code (EBCDIC - primarily used on IBM midrange and mainframe systems).
  • ASCII characters occupied seven bits (code points 0-127), and contains only characters used in North American English. ASCII characters are usually encoded in bytes, so many vendors of ASCII-based systems used the remaining codes 128-255 for special characters such as graphics, line symbols (horizontal, vertical, connector, and corner line symbols for drawing tables), and accented characters; these were called "extended ASCII".
  • Several ISO standards exist in an attempt to standardize the "extended ascii" characters, such as ISO8859, which was intended to enable the encoding of European languages by adding currency symbols and accented characters. However, the original version of ISO8859-1 does not include all accented characters and was created before the Euro symbol was standardized, so there are multiple versions of ISO8859, ranging from ISO8859-1 through ISO8859-15.
  • The Unicode and ISO10646 initiatives were initiated to create a single character code set that would encode all symbols used in human writing, both for current and obsolete languages. These initiatives were merged, and the Unicode and ISO10646 standards define a common character set with 2³² potential code points. However, Unicode also describes transformation formats for data interchange, rendering and composition/decomposition recommendations, and font symbol recommendations.
  • The first 127 code points in Unicode correspond to ASCII code points, and the first 255 code points correspond to ISO8869-1 code points. The first 65536 code points form the Basic Multilingual Pane (BMP), which contains most of the characters required to write in all contemporary languages. Therefore, for many applications, it is inefficient to store Unicode as full 32-bit values. To solve this issue, several Unicode Transformation Formats (also known -- technically incorrectly -- as Unicode Transfer Formats) have been defined, including UTF-8, UTF-16, and UTF-32 (32-bit). UTF-8 represents ASCII and some ISO-8859 characters as a single byte, the remainder of the BMP as 2-3 bytes per character, and the remaining characters using 3-4 bytes per character. UTF-16 is similar, encoding much of the BMP in a single 16-bit value, and most other characters as two 16-bit values.
• Sound
  • Sound waves are air pressure vibrations.
  • Digital sound is most often represented in raw form as a series of time-based measurements of air pressure, called Pulse Coded Modulation (PCM).
  • PCM takes a lot of storage, so sound is often compressed in either a lossless (perfectly recoverable) or lossy format (higher compression, but the decompressed data doesn't perfectly match the original data). To permit high compression ratios with minimal impact on quality, psychoacoustic compression is used - sound variations that most people can't perceive are removed.
• Graphics
  • The human eye perceives luminance (brightness) as well as hue (colour). Our main hue receptors ("cones") are generally sensitive to three wavelengths: red, green, and blue (RGB). We can stimulate the eye to perceive most colours by presenting a combination of light at these three wavelengths utilizing metamerism.
  • Digital displays emit RGB colours, which are mixed together and perceived by the viewer. This is called additive colour.
  • For printing, cyan (C)/yellow (Y)/magenta (M) pigmented inks are used, plus black (K) to reduce the amount of colour ink required to represent dark tones; this is known as CYMK colour. These pigments absorb light at specific frequencies, subtracting energy from white or near-white sunlight or artificial light. This is called subtractive colour.
  • Images are broken into picture elements (pixels) and each pixel is usually represented by a group of values for RGB or CYMK channels, where each channel is represented by an integer or floating-point value. For example, using an 8-bit-per-pixel integer scheme (also known as 24-bit colour), the brightest blue could be represented as R=0,G=0,B=255; the brightest yellow would be R=255,G=255,B=0; black would be R=0,G=0,B=0; and white would be R=255,G=255,B=255. With this scheme, the number of unique colours available is 256^3 ~= 16 million.
  • As with sound, the raw storage of sampled data requires a lot of storage space, so various lossy and lossless compression schemes are used. Highest compression is achieved with psychovisual compression (e.g., JPEG).
  • Moving pictures (video, animations) are stored as sequential images, often compressed by encoding only the differences between frames to save storage space. Motion compensation can further compress the data stream by describing how portions of the previous frame should be moved and positioned in the current frame.
• Compression techniques
  • Huffman encoding / Adaptive arithmetic encoding
    • Instead of fixed-length numbers, variable-length numbers are used, with the most common values encoded in the smallest number of bits. This is an effective strategy if the distribution of values in the data set is uneven.
  • Repeated sequence encoding (1D, 2D, 3D)
    • Run length encoding is an encoding scheme that records the number of repeated values. For example, fax messages are encoded as a series of numbers representing the number of white pixels, then the number of black pixels, then white pixels, then black pixels, alternating to the end of each line. These numbers are then represented with adaptive artithmetic encoding.
    • Text data can be compressed by building a dictionary of common sequences, which may represent words or complete phrases, where each entry in the dictionary is numbered. The compressed data contains the dictionary plus a sequence of numbers which represent the occurrence of the sequences in the original text. On standard text, this typically enables 10:1 compression.
  • Decomposition
    • Compound audio wavforms can be decomposed into individual signals, which can then be modelled as repeated sequences. For example, a waveform consisting of two notes being played at different frequencies can be decomposed into those separate notes; since each note consists of a number of repetitions of a particular wave pattern, they can individually be represented in a more compact format by describing the frequency, waveform shape, and amplitude characteristics.
  • Palletization
    • Images often contain repeated colours, and rarely use all of the available colours in the original encoding scheme. For example, a 1920x1080 "full HD" image contains about 2 million pixels, so if every pixel was a different colour, there would be a maximum of 2 million colours. But it's likely that many of the pixels in the image are the same colour, so there might only be (perhaps) 4000 colours in the image. If each pixel is encoded as a 24-bit value, there are potentially 16 million colours available, and there is no possibility that they are all used. Instead, a palette can be provided which specifies each of the 4000 colours used in the picture, and then each pixel can be encoded as a 12-bit number which selects one of the colours from the palette. The total storage requirement for the original 24-bit scheme is 1920*1080*3 bytes per pixel = 5.9 MB. Using a 12-bit pallette, the storage requirement is 3 * 4096 bytes for the palette plus 1920*1080*1.5 bytes for the image, for a total of 3 MB -- a reduction of almost 50%
  • Psychoacoustic and psychovisual compression
    • Much of the data in sound and images cannot be perceived by humans. Psychoacoustic and psychovisual compression remove artifacts which are least likely to be perceived. As a simple example, if two pixels on opposite sides of a large image are almost but not exactly the same, most people won't be able to tell the difference, so these can be encoded as the same colour if that saves space (for example, by reducing the size of the colour palette).

Week 1 Deliverables

1. Follow the SPO600 Communication Tools set-up instructions.
2. Optional (strongly recommended): Set up a personal Linux system.
3. Optional: Purchase an AArch64 development board (such as a Raspberry Pi 4, Raspberry Pi 400, or 96Boards device. (Note: install a 64-bit Linux operating system on it, not a 32-bit version).
4. Start work on Lab 1. Blog your work.

Week 2

Week 2 - Class I

Video

• Summary video recording from class
• Reminder: The Tuesday classes are live. An edited recording is provided for reference only - it is no substitute for attending class, taking notes, and asking questions!

Machine Language, Assembly Language

Follow the Links!
To get the full benefit of the following material, please follow the links embedded within it. For additional detail, see the Category links at the bottom of those pages -- for example, the category linked from many of the following pages has over 30 pages of content.

• Although we program computers in a variety of languages, they can really only execute one langauge: Machine Language, which is encoded in an architecture-specific binary code, sometimes called object code.
• Machine language is not easy to read. Assembly Language corresponds very closely to machine language, but is (sort of!) human-readable.
• Assembly language is converted into machine code by a particular type of compiler called an Assembler (sometimes the language itself is also referred to as "Assembler").

6502

Modern processors are complex - the reference manual for 64-bit ARM processors is over 7000 pages long! - so we 're going to look at assembly lanaguage on a much simpler processor to get started. This processor is the 6502.

• Introduction to the 6502 (note the Resources links on that page)
• Introduction to the 6502 Instructions
• Information about the 6502 Emulator which we will use in this course, and some example code
• Link to the actual 6502 emulator

Week 2 - Class II

Videos

• Week 2 Announcements
• 6502 Emulator and Debugger
• Calculating 6502 Program Execution Time

Lab 2

• 6502 Assembly Language Lab - Lab 2

Week 2 Deliverables

1. If not already completed:
   1. Set up your SPO600 Communication Tools
   2. Complete Lab 1 and blog your work.
2. Study the 6502 Instructions and make sure you understand what each one does
3. Complete Lab 2 and blog your results

Week 3

Week 3 - Class I

Video

• No video is available due to issues with the audio.
• The links below contain the same information.

More 6502 Assembly

• 6502 Math

Week 3 - Class II

Videos

• 6502 Jumps, Branches, and Procedures
• 6502 Characters & Strings

More 6502 Assembly

• 6502 Jumps, Branches, and Procedures

Lab 3

• 6502 Math and Strings Lab (Lab 3)

Week 3 Deliverables

• Perform Lab 3
• Continue to blog
• Make sure you've submitted the form with your blog URL and public key (see Week 1 Deliverables)

Week 4

Week 4 - Class I

Video

• Edited summary video
• Reminder: the videos are a summary/recap only - they're no substitute for attending, taking notes, and asking questions in class!

Compiler Optimizations

• Compiler Optimizations

Week 5 - Class II

• Video content will be delayed due to a storage error
• Please continue work on Lab 3

Week 4 Deliverables

• By the end of the day on Tuesday, February 1: Blogs for January are due (including any labs you're submitting for January). Make sure you've submitted the web form with your blog URL so I can find it!
• Finish Lab 3
• Continue to blog

Week 5

Week 5 - Class I

Video

• No recording of the live session is available (bad audio)
• Pre-recorded lecture/demo: Make and Makefiles

Notes

• Make and Makefiles

Compiler Options

• (Modern compilers are similar in options, for the sake of this discussion I'm focusing on the GNU C Compiler (gcc), part of the GNU Compiler Collection)
• There are hundreds of compiler features available, many of which are optimization options.
• These features can be controlled from the compiler command line:
  • To enable a feature, specify -f and the option name: -fbuiltin
  • To disable a feature, specify -f and then no- and the option name: -fno-builtin
• Example:

gcc -fbuiltin -falign-functions -no-caller-saves foo.c -o foo

• To see the available optimization features and what each does, view the gcc manpage and/or gcc manual
• It's a pain to specify hundreds of -f options on the command line, so these are grouped into commonly-used sets. The sets can be specified with the -O compiler option (note that that is a capital letter "O", not a lowercase "o" nor a zero "0"), followed by an optimization level:
  • -O0 : almost no optimization
  • -O1 : optimizations that can be quickly performed
  • -O2 : all of the normal optimizations that can be safely applied to all programs (this is the usual default optimization level)
  • -O3 : all normal optimization, including some that may in rare cases cause changes to the operation of the program (for example, counting +0 and -0 as the same number -- which is fine in the vast majority of cases, but might interfere with the correct operation of some scientific calculations)
  • -Os : optimize for smallest size (of both the executable and the memory usage while executing)
  • -Ofast : optimize for highest speed, even at the cost of more memory usage
  • -Og : optimize for debugging -- avoid optimizations that will excessively convolute the code, making it harder to see the correlation between the source code and the object code
• Note that the set of optimizations considered "safe" may vary over time - for example, vector optimization were previously considered unsafe (-O3) in the gcc compiler, but with improvements and testing are not considered safe and are therefore included in the -O2 level in newer versions of gcc.
• You can specify a group of options with -O and override the use of individual options with -f by placing the -O group first:

gcc -O2 -fno-builtin foo.c -o foo

• To see the optimizations that will be applied by a given set of command-line options, use -Q --help=optimizers to query the optimization list that the compiler will use:

gcc -O1 -Q --help=optimizers | less

Week 5 - Class II

Video

• x86_64 & AArch64 Introduction - Registers
• Memory Alignment and Performance
• Paged Memory Systems

Resources

• X86 64 Register and Instruction Quick Start
• AArch64 Register and Instruction Quick Start
• Computer Architecture

Week 5 Deliverables

• Finish Lab 3
• Continue to blog

Week 6

Week 6 - Class I

Video

• Week 6 Class I Summary Video

Class Servers

• Student accounts on the SPO600 Servers have been set up
• Please test that you can login to both of these machines as soon as possible. Contact me if you have any issues logging in.

Week 6 - Class II

Videos

• 64-Bit Assembly Language - Part II

Reading

• Assembly Language
• Assembler Basics

Lab

• SPO600 64-bit Assembly Language Lab (Lab 4)

Week 6 Deliverables

• Lab 4
• Continue to Blog

Week 7

Week 7 - Class I

Video

• A summary video will be posted after editing

Week 7 - Class II

Videos

• Single Instruction, Multiple Data (SIMD)
• Inline Assembly Language
• Algorithm Selection
• Linux Tips (This is an older video -- the systems mentioned, such as xerxes, were previous versions of the class servers.)

Reading

• Inline Assembly Language

Lab

• SPO600 Algorithm Selection Lab (Lab 5)

Week 7 Deliverables

• Lab 5
• Note: Blog for February are due at 11:59 pm on February 28 (Tuesday). I'll be looking for an average of 1-2 blog posts per week, or 4-8 blog posts for February. Please review your posts for accuracy and completeness.

Week 8

Week 8 - Class I

Video

• Edited summary video

Reading

• SVE2

Lab

• SPO600 SVE2 Lab (Lab 6)

Week 8 - Class II

Project

• Winter 2022 SPO600 Project - We will discuss this in the next class

Video

• Searching a Codebase

Week 8 Deliverables

• Blog about your Lab 6

Week 9

Week 9 - Class I

Video

• Summary video: Project Discussion 1

Week 9 - Class II

• No content posted.

Week 10

Week 10 - Class I

Video

• Summary video: Project Discussion 2

Week 10 - Class II

Videos

• GCC and SVE2 - A discussion of compiler flags, macros, pre-processor directives, and disassembly analysis that may be useful in project stage 2.
• Bitwise Operations - this video covers AND, OR, XOR/EOR, and NOT operations. It should be review, but I've seen these operations misused a few times lately so it may be useful to you, especially if you are not familiar with the use of masks with these operations.

Week 10 - Deliverables

• Blog about your project work.
• Note: Your Project Stage 1 is due on Monday, March 28 by midnight.

Week 11

Week 11 - Class I

Videos

• Project Discussion 3 - An edited recording of the March 28, 2022 SPO600 class.
• Benchmarking and Profiling - An optional video that may be useful to some projects in Stage 2. This video discusses benchmarking (overall program performance analysis) and profiling (per-function/per-method performance analysis) principles and techniques. (This is an edited version of a previous-semester video. There are a couple of small audio and video glitches in the recording).

Reading

• Auto-vectorization with GCC 4.7 - Although based on an earlier version of GCC (and a number of new features have been added to the GCC autovectorizer since this article was written), it discusses some of the techniques and code adjustments that may be required to get the GCC compiler to vectorize code. Note that the -fopt-info-vec-all or -fopt-info-vec-missed options are useful in conjunction with this technique, as they will cause the compiler to emit information about the vectorization decisions that it is making.
• GCC 12 Enables Auto-Vectorization for -O2 Optimization Level - a short news article from October 2021 regarding the commit that added autovectorization to the -O2 optimization level, which is the default for many projects. GCC12 is expected to ship in April 2022, according to a recent status update.

Week 11 - Class II

SVE2 Demonstration

• Code available here: https://github.com/ctyler/sve2-test
• This is an implementation of a very simple program which takes an image file, adjusts the red/green/blue channels of that file, and then writes an output file. Each channel is adjusted by a factor in the range 0.0 to 2.0 (with saturation).
• The image adjustment is performed in the function adjust_channels() in the file adjust_channels.c. There are three implementations:
  1. A basic (naive) implementation in C. Although this is a very basic implementation, it is potentially subject to autovectorization.
  2. An implementation using inline assembler for SVE2.
  3. (Future) An implementation using ACLE compile intrinsics.
• The implementation built is dependent on the value of the ADJUST_CHANNEL_IMPLEMENTATION macro.
• The provided Makefile will build two versions of the binary, one using implementation 1 (named image_adjust1) and one using implementation 2 (named image_adjust2), and it will run through 3 tests with each binary. The tests use the input image file tests/input/bree.jpg (a picture of a cat) and place the output in the files tests/output/bree[12][abc].jpg. The output files are processed with adjustment factors of 0.5/0.5/0.5, 1.0/1.0/1.0, and 2.0/2.0/2.0.
• Please examine, build, and test the code, compare the implementations, and note how it works - there are extensive comments in the code, especially for implementation 2.
• Your observations about the code might make a good blog post!

Week 11 - Deliverables

• Blog about your project work.

Week 12

Week 12 - Class I

Video

• SVE2 Examples - Summary video of the SPO600 class on Tuesday, April 5.

SVE2 Demonstration

• The SVE2 example code has been extended with an additional inline assembley implementation, plus an ACLE implementation.

Week 12 - Class II

Video

• SVE2 Examples - Part 2 - Part 2 of a look at the example code - A discussion of the bug that existed in the ACLE/intrinsics code discussed in the last class, plus an examination of the disassembly of the naive/autovectorized version of the code (implementation #1).

Week 12 - Deliverables

• Continue to blog about your project work
• Project Stage 2 will be due on Wednesday, April 13 (11:59 pm EDT).

Week 13

Week 13 - Class I

Video

• Memory Systems summary video

Week 13 - Class II

• Good Friday - no class

Week 13 Deliverables

• Continue to post about your project.

Week 14

Week 14 - Class I

Video

• (Not available)

Week 14 Deliverables

• Project Stage 2 due Monday April 18 (by 11:59 pm)
• Project Stage 3 due Friday April 22 (by 11:59 pm)
• April blog posts due Friday April 22 (by 11:59 pm)