1,885
edits
Changes
no edit summary
[[Category:SPO600 Labs- Retired]]{{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 three algorithms for adjusting the volume of PCM audio samples based on benchmarking.}}
{{Admon/important|x86_64 and AArch64 Systems|This lab must be performed on both x86_64 and AArch64 systems. You may use the [[SPO600 Servers]] or you may use other system(s) -- it might make sense to use your own x86_64 system and [[SPO600_Servers#AArch64:_israel.cdot.systems|israel.cdot.systems]] for AArch64.}}
=== Background ===
* Digital sound is typically represented, uncompressed, as signed 16-bit integer signal samples. There 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 (kHz)per second per 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 (silence) to 1.00 (full volume).
* On a mobile device, the amount of processing required to scale sound will affect battery life.
=== Don't Compare Across Machines ===
In this lab, ''do not'' compare the relative performance across different machines, because various systems have different microarchitectures, memory configurations, peripheral implementations, and clock speeds, from mobile-class to server-class (e.g. Intel Atom vs. Xeon; AMD APU vs. Threadripper; ARM Cortex-A35 A55 vs. Neoverse-V1V2).
However, ''do'' compare the relative performance of the various algorithms on the ''same'' machine.
** What background operations are being performed?
** How does your login on the machine affect performance (e.g., network activity)?
* <span style="background: #ffff00">Isolate the performance of the volume scaling code. </span> This is one of the most important parts of this lab! There are two practical approaches:
** Subtract the performance of the dummy version of the program from each of the other versions, or
** Add code to the program to measure and report just the performance of the volume-scaling code
Blog about your experiments with a detailed analysis of your results, including memory usage, performance, accuracy, and trade-offs. Include answers to all of the questions marked with Q: in the source code.
'''Make sure you convincingly '''<u>prove''' </u> your results to your reader! ''' Re-read the [[#Important.21|section marked ''Important'' above]] and make sure you address the issues explained there. 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.