SPO600 Algorithm Selection Lab
- Digital sound is typically represented, uncompressed, as signed 16-bit integer signal samples. There is one stream of samples for the left and right stereo channels, at typical sample rates of 44.1 or 48 thousand samples per second, for a total of 88.2 or 96 thousand samples per second.
- To change the volume of sound, each sample can be scaled by a volume factor, in the range of 0.0000 to 1.0000 (silence to full volume).
- On a mobile device, the amount of processing required to scale sound will affect battery life.
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
Design of Your Test
- 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. Make sure that you measure the time taken in the 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 test using actual sound data (see the tips section, below) or using generated data. If you're generating data, it is best to use a pseudo-random number generator which is seeded with the same value every time, so that each run processes the same data.
- Be aware of what other tasks the system is handling during your test run.
- What is the impact of various optimization levels on the software performance?
- Does the distribution of data matter?
- If samples are fed at CD rate (44100 samples per second x 2 channels), can both algorithms keep up?
- What is the memory footprint of each approach?
- What is the performance of each approach?
- What is the energy consumption of each approach? (What information do you need to calculate this?)
- 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?