Difference between revisions of "SPO600 Algorithm Selection Lab"
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[[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.}} | [[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.}} | ||
| + | [[Category:SPO600 Labs]] | ||
== Lab 3 == | == Lab 3 == | ||
1. Write two different approaches to adjusting the volume of a sequence of sound samples: | 1. Write two different approaches to adjusting the volume of a sequence of sound samples: | ||
| − | * The first one should scale a signed 16-bit integer by multiplying it by a volume scaling factor expressed as a floating point number in the range of 0-1. | + | * The first one should scale a signed 16-bit integer by multiplying it by a volume scaling factor expressed as a floating point number in the range of 0-1. This should be implemented as a function that accepts the sample (int16) and scaling factor (float) and returns the scaled sample (int16). |
| − | * The second one should do the same thing, using a lookup table (a pre-computed array of all 65536 possible values). The lookup table should be initialized every time a different volume factor is used. | + | * The second one should do the same thing, using a lookup table (a pre-computed array of all 65536 possible values). The lookup table should be initialized every time a different volume factor is used. This should be implemented as a drop-in replacement for the function above (same parameters and return value). |
| + | |||
2. Test which approach is faster. Control the variables and use a large run of data (at least millions of samples). Use both [[SPO600 Servers|Xerxes and Aarchie]] for testing. | 2. Test which approach is faster. Control the variables and use a large run of data (at least millions of samples). Use both [[SPO600 Servers|Xerxes and Aarchie]] for testing. | ||
| + | |||
3. 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). | 3. 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). | ||
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=== Competition === | === Competition === | ||
| − | * How fast can you scale 100 million int16 PCM sound samples? | + | * For discussion in class: How fast can you scale 100 million int16 PCM sound samples? |
| + | |||
| + | {{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.}} | ||
Revision as of 14:34, 2 October 2015
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.
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 3
1. Write two different approaches to adjusting the volume of a sequence of sound samples:
- The first one should scale a signed 16-bit integer by multiplying it by a volume scaling factor expressed as a floating point number in the range of 0-1. This should be implemented as a function that accepts the sample (int16) and scaling factor (float) and returns the scaled sample (int16).
- The second one should do the same thing, using a lookup table (a pre-computed array of all 65536 possible values). The lookup table should be initialized every time a different volume factor is used. This should be implemented as a drop-in replacement for the function above (same parameters and return value).
2. Test which approach is faster. Control the variables and use a large run of data (at least millions of samples). Use both Xerxes and Aarchie for testing.
3. 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
- 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?
- Xerxes and Aarchie 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
- For discussion in class: How fast can you scale 100 million int16 PCM sound samples?
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 sox utility present on most Linux systems and available for a wide range of platforms.
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 sox utility present on most Linux systems and available for a wide range of platforms.
