[[Category:SPO600 Labs]]{{Admon/lab|Purpose of this Lab|In this lab, you will select one of two algorithms for adjusting the volume of PCM audio samples based on benchmarking of two possible approaches.}}
== Lab 5 6 ==
1Background:* Digital sound is typically represented, uncompressed, as signed 16-bit integer signal samples. Write two different approaches to adjusting There is one stream of samples for the volume left and right stereo channels, at typical sample rates of 44.1 or 48 thousand samples per second, for a sequence total of sound 88.2 or 96 thousand samples, using different algorithms, in per second.* To change the C language. In each case, you should take a series volume of signed 16-bit integers representing sound waveform samples and multiply , each sample can be scaled by a floating point "volume scaling factor" , in the range of 0.000-0000 to 1.0000000 (silence to full volume). It is recommended that one approach be * On a mobile device, the naive multiplication amount of the sample by the volume scaling factor, and the second approach be dramatically different (e.g., table lookup, multiplication by bit-shifting, memoization, or another approach)processing required to scale sound will affect battery life.
2. Test which approach is faster. Control the variables and use a large run of data (at least hundreds millions of samples). Use both [[SPO600 Servers|x86 and AArch64]] systems for testing - DO NOT compare results between the architectures (because they are different classes of systems) but DO compare the relative performance of the algorithms on each architecture. For example, you might note that "Algorithm I is NN% faster than Algorithm II on Architecture A, but NN% slower on Architecture B".
3Task: A. Create a large (500M?) array of int16_t numbers to represent sound samples. B. Scale each sample by the volume factor (0.75). Store the results into the original array or into a separate result array. C. Sum the results and display the total (just to keep the optimzer from eliminating the scaling code). D. Determine the time taken for step B of each approach. You can add instrumentation to your program or you can use the 'time' command. Try using each of these three approaches to step B, and compare the results: # Multiply each sample by the floating point volume factor 0.75# Pre-calculate a lookup table (array) of all possible sample values multiplied by the volume factor, and look up each sample in that table to get the scaled values.# Convert the volume factor 0.75 to a fix-point integer by multiplying by a binary number representing a fixed-point value "1". For example, you could use 0b100000000 (= 256 in decimal). Shift the result to the right the required number of bits after the multiplication (>>8 if you're using 256 as the multiplier). Blog about your results. Important! -- 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).
=== Things to consider ===
* What is the performance of each approach?
* What is the energy consumption of each approach? (What information do you need to calculate this?)
* Xerxes Aarchie and Betty have different performance profiles, so it's not reasonable to compare performance between the machines, but it is reasonable to compare the relative performance of the two algorithms in each context. Do you get similar results?
* What other optimizations can be applied to this problem?
=== Competition ===
* How fast can you scale 500 million int16 PCM sound samples?
=== Tips ===
