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[[Category:SPO600 Labs]]{{Admon/lab|Purpose of this Lab|In this lab, you will investigate the impact of different algorithms which produce the same effect. You will test and select one of two three algorithms for adjusting the volume of PCM audio samples based on benchmarking of two possible approaches.}}{{Chris Tyler Draft}}
== Lab 5 6 ==
=== Background:===* Digital sound is typically represented, uncompressed, as signed 16-bit integer signal samples. There is one stream are two streams of samples , one each for the left and right stereo channels, at typical sample rates of 44.1 or 48 thousand samples per secondper channel, for a total of 88.2 or 96 thousand samples per second(kHz). Since there are 16 bits (2 bytes) per sample, the data rate is 88.2 * 1000 * 2 = 176,400 bytes/second (~172 KiB/sec) or 96 * 1000 * 2 = 192,000 bytes/second (~187.5 KiB/sec).* To change the volume of sound, each sample can be scaled (multiplied) by a volume factor, in the range of 0.00 (silensesilence) to 1.00 (full volume).
* On a mobile device, the amount of processing required to scale sound will affect battery life.
=== Three Approaches ===
# The basic or Naive algorithm (<code>vol1.c</code>). This approach multiplies each sound sample by 0.75, casting from signed 16-bit integer to floating point and back again. Casting between integer and floating point can be [[Expensive|expensive]] operations.# Alookup-based algorithm (<code>vol2.c</code>). Create This approach uses a large pre-calculated table of all 65536 possible results, and looks up each sample in that table instead of multiplying.# A fixed-point algorithm (500M?<code>vol3.c</code>) array of int16_t numbers . This approach uses fixed-point math and bit shifting to represent sound samplesperform the multiplication without using floating-point math.
Get the files for this lab from one of the [[SPO600 Servers]] -- but you can perform the lab wherever you want (feel free to use your laptop or home system). Test on both an x86_64 and an AArch64 system.
Perform these steps:# Multiply Unpack the archive <code>/public/spo600-algorithm-selection-lab.tgz</code># Study each sample by of the source code files and make sure that you understand what the floating point volume factor 0code is doing.75# Pre-calculate '''Make a lookup table (array) prediction''' of all possible sample values multiplied by the volume factor, relative performance of each scaling algorithm.# Build and look up test each sample in that table to get of the scaled valuesprograms.# Convert * Do all of the volume factor 0.75 algorithms produce the same output?#** How can you verify this?#** If there is a difference, is it significant enough to a fix-point integer by multiplying by a binary matter?#* Change the number representing of samples so that each program takes a fixed-point value "reasonable amount of time to execute (suggested minimum 20 seconds, 1". For example, you could use 0b100000000 (= 256 in decimalminute or more is better). Shift # Test the result performance of each program.#* Find a way to measure performance ''without'' the right time taken to perform the required number of bits after test setup pre-processing (generating the multiplication samples) and post-processing (>>8 if summing the results) so that youcan measure 're using 256 as 'only'' the time taken to scale the multiplier)samples.'''This is the hard part!'''#* How much time is spent scaling the sound samples?#* Do multiple runs take the same time? How much variation do you observe? What is the likely cause of this variation?#* Is there any difference in the results produced by the various algorithms?#* Does the difference between the algorithms vary depending on the architecture and implementation on which you test?#* What is the relative memory usage of each program?# Was your prediction accurate?
=== Deliverables ===
Blog about your results. Important! -- explain what you're doing so that experiments with a reader coming across detailed analysis of your blog post understands the context (in other wordsresults, don't just jump into a discussion of optimization results including memory usage, performance, accuracy, and trade-- give your post some context)offs.
Make sure you convincingly prove your results to your reader! Also be sure to explain what you're doing so that a reader coming across your blog post understands the context (in other words, don't just jump into a discussion of optimization results -- give your post some context).
'''Optional - Recommended:''' Compare results across several '''implementations''' of AArch64 and x86_64 systems. Note that on different CPU implementations, the relative performance of different algorithms will vary; for example, table lookup may outperform other algorithms on a system with a fast memory system (cache), but not on a system with a slower memory system.
* For AArch64, you could compare the performance on AArchie against the various class servers, or between the class servers and a Raspberry Pi 3 (in 64-bit mode) or an ARM Chromebook.
* For x86_64, you could compare the performance of different processors, such as xerxes, your own laptop or desktop, and Seneca systems such as Matrix or lab desktops.
=== Things to consider ===
==== Design of Your Test Tests ==== * Most solutions for a problem of this type involve generating a large amount of data in an array, processing that array using the function being evaluated, and then storing that data back into an array. The test setup can take more time than the actual test! Make sure that you measure the time taken in the code under test function only -- you need to be able to remove the rest of the processing time from your evaluation.
* You may need to run a very large amount of sample data through the function to be able to detect its performance.
* If you do not use the output from your calculation (e.g., do something with the output array), the compiler may recognize that, and remove the code you're trying to test. Be sure to process the results in some way so that the optimizer preserves the code you want to test. It is a good idea to calculate some sort of verification value to ensure that both approaches generate the same results.
* You can Be aware of what other tasks the system is handling during your test using actual sound data run, including software running on behalf of other users. === Tips ==={{Admon/tip|Analysis|Do a thorough analysis of the results. Be certain (see and prove!) that your performance measurement ''does not'' include the tips section, below) generation or using generated summarization of the test data. If you're generating data, it is best Do multiple runs and discard the outliers. Decide whether to use a pseudo-random number generator which is seeded with mean, minimum, or maximum time values from the same value every timemultiple runs, and explain why you made that decision. Control your variables well. Show relative performance as percentage change, e.g., so "this approach was NN% faster than that each run processes the same dataapproach".}}* Be aware of what other tasks {{Admon/tip|Non-Decimal Notation|In this lab, the number prefix 0x indicates a hexadecimal number, and 0b indicates a binary number, in harmony with the system is handling during your test runC language.}}
The version of the <code>time</code> command located in <code>/bin/time</code> gives slightly different information than the version built in to bash -- including maximum resident memory usage: <code>/bin/time ''./programName''</code>}}
{{Admon/tip|SOX|If you want to try this with actual sound samples, you can convert a sound file of your choice to raw 16-bit signed integer PCM data using the [http://sox.sourceforge.net/ sox] utility present on most Linux systems and available for a wide range of platforms.}}
{{Admon/tip|Stack Limitstdint.h|Fixed-size, non-static arrays will be placed in the stack space. The size of the stack space is controlled by per-process limits, inherited from the shell, and adjustable with the <code>ulimitstdint.h</code> command. Allocating an array larger than the stack header provides definitions for many specialized integer size limit will cause a segmentation fault, usually on the first write. To see the current stack limit, use <code>ulimit -s</code> (displayed value is in KB; default is usually 8192 KB or 8 MB)types. To set the current stack limit, place a new size in KB or the keyword Use <code>unlimitedint16_t</code>after the <code>for 16-s</code> argument.<br /><br />Alternate (and preferred) approach: allocate the array space with <code>malloc()</code> or <code>calloc()</code>bit signed integers.}}
{{Admon/tip|stdint.hScripting|The <code>stdint.h</code> header provides definitions for many specialized integer size types. Use <code>int16_t</code> for 16-bit signed integers.bash scripting capabilities to reduce tedious manual steps!}}