Difference between revisions of "Fall 2021 SPO600 Weekly Schedule"
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** Psychoacoustic and psychovisual compression | ** 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). | *** 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 2 - Class II === | ||
==== Computer Architecture Overview ==== | ==== Computer Architecture Overview ==== | ||
Revision as of 22:07, 19 September 2021
This is the schedule and main index page for the SPO600 Software Portability and Optimization course for Fall 2021.
This SPO600 weekly schedule will be updated as the course proceeds - dates and content are subject to change. The cells in the summary table will be linked to relevant resources and labs as the course progresses.
Schedule Summary Table
This is a summary/index 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 1
Week 1 - Class II
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 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
- System assumptions that don't hold true on other platforms
- 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
- Monday: asynchronous (pre-recorded) resources will be made available - see this page for details each week
- Thursay: synchronous (live) classes will be held 8:00-9:50 am Eastern Time. See the announcement on Blackboard for details.
Course and Setup: Accounts, agreements, servers, and more
- SPO600 Communication Tools
- SPO600 Participants page
- Key generation for SSH to the SPO600 Servers.
How open source communities work
- Do the Code Review Lab (Lab 1) as homework.
Week 1 Deliverables
- Course setup:
- Set up your SPO600 Communication Tools - in particular, set up a blog.
- Add yourself to the Current SPO600 Participants page (leave the projects columns blank).
- Generate a pair of keys for SSH and email the public key to your professor, so that he can set up your access to the class servers.
- Optional (strongly recommended): Set up a personal Linux system.
- Optional: Purchase an AArch64 development board (such as a 96Boards HiKey or Raspberry Pi 3 or 4. (If you use a Pi, install a 64-bit Linux operating system on it, not a 32-bit version).
- Start work on Lab 1.
Week 2
Week 2 - Class I
Video Lecture
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 0-5 as an analog electical value, we could use a voltage from 0 - 5 volts. However, if we use a long cable, there will be signal loss the voltage will drop: we could apply 5 volts on one end of the cable, but only observe (say) 4.1 volts on the other end of the cable. Alternately, electromagnetic interference from nearby devices could slight increase the signal.
- If we use instead use the same voltages and cable length to carry a binary signal, where 0 volts = off and 5 volts = on, a signal that had degraded from 5 volts to 4.1 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 is0:2bits-1where 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)-1where ! 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
- Floating point numbers have three parts: a sign bit (0 for positive, 1 for negative), a mantissa or significand, and an exponent. The value is interpreted as
sign mantissa * 2exponent. - The most commonly-used floating point formats are defined in the IEEE 754 standard.
- Floating point numbers have three parts: a sign bit (0 for positive, 1 for negative), a mantissa or significand, and an exponent. The value is interpreted as
- 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 hue receptors 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.
- Digital displays emit RGB colours, which are mixed together and perceived by the viewer. For printing, cyan/yellow/magenta inks are used, plus black to reduce the amount of colour ink required to represent dark tones; this is known as CYMK 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.
- 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, the 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 frequence, waveform shape, and amplitude characteristics.
- Pallettization
- Images often contain repeated colours, and rarely use all of the available colours in the original encoding scheme. For example, a 1920x1080 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).
- Huffman encoding / Adaptive arithmetic encoding
Week 2 - Class II
Computer Architecture Overview
Introduction to Assembly Language on the 6502 Processor
To understand basic assembly/machine language concepts, we're going to start with a very simple processor: the 6502.
- Resources
- 6502 Emulator for this course
6502 Assembly Language Lab
- Lab 2: 6502 Assembly Language Lab (start in class, finish and write up as homework)
Week 2 Deliverables
