memory-mountain

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memory-mountain

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A script to get a 3D plot of your cache memory performance

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The memory mountain
===================

This program is intended to  help reproduce the "memory mountain" plot
which  illustrates  the  cover  of   the  book  "Computer  Systems:  A
Programmer's Perspective" by Randal E. Bryant and David R. O'Hallaron.

http://csapp.cs.cmu.edu/

(search google for a .pdf version of the book)

# Basic idea

We're measuring the memory bandwidth  of various levels of  the memory
hierarchy, and  the impact of  the (spatial and temporal)  locality of
accesses. For that, the program allocates  a flat data buffer,  walks
through it, then computes the actual read throughput.

This is explained in detail in section 6.6.1 of the book, so go read it.

# Usage

just type `make plot` and enjoy the show.

dependencies:
- gcc
- python3
- matplotlib/numpy

# Implementation

The program `benchmark.c`  runs the actual function from  the book and
measures its execution time:

data_t test()
    {
        data_t result=0;
        size_t i;
        for( i=0; i<count; i += stride )
        {
            result += data[i];
        }
        return result;
    }
    
Notes:

- we actually  have to do  something with the values  (i.e. accumulate
  the sum in the `result` variable) otherwise the compiler and the CPU
  do voodoo optimizations and skew  the results. (hence the `volatile`
  variable in the main program)

- the program allows  for changing the `data_t` type  to various sizes
  (`char`,  `short`,  `int`,  `double`)  but in  my  experience,  only
  `double` (native register size ?) gives interesting results. but YMMV

- for  small  data sizes,  the  execution  time  is  too small  to  be
  significant (we measure time with `clock()`, which has a resolution
  of microseconds). So we repeat the call and measure the execution
  time of
  10 calls,  then 100  calls, and  so on, until  we get  a significant
  measurement (above 1ms). But this is bad, because the processor is able
  to detect the nested loop and accelerate it massively. A better
  approach would be to use hardware counters to get nanosecond
  accuracy, but then the program is not portable any more. (the original
  program, by the book authors does that. It's interesting to compare the
  two)

## Interpreting the output

The program first  reports how it parsed  its command-line parameters.
Then it calls `test()` enough times to measure a significant execution time.
Once a satisfying measure is obtain, we compute the time required for one `test()`
Last, we print out a summary  of the experiments: how much we did read,
in how much time, and the corresponding throughput.

$ ./benchmark 3000 3
    size=3000 (3kB ; 375 items of size 8) stride=3
    1 repeats -> 0us
    10 repeats -> 0us
    100 repeats -> 0us
    1000 repeats -> 0us
    10000 repeats -> 0us
    100000 repeats -> 10ms
    1000000 repeats -> 140ms
    => one repeat -> 0.14us
    read 1kB in 0.14us = 7142.9 MB/s

# Running an experiment

The script  `harness.py` invokes the `benchmark`  program with various
SIZE and STRIDE parameters.

To reduce measurement noise, we run the program multiple times, and
only keep the median value.

You should  redirect the output of  `harness.py` in a file  so that you
can plot them later:

$./harness.py > results.txt

## Interpreting the output

For each series of runs the script prints a line with the
SIZE and STRIDE parameters, followed by the median throughput (in
MBytes/s)

example:

2048 1 6826.7
    2048 5 8500.0
    2048 9 8960.0
    2048 13 8941.2
    2048 17 8571.4

# Plotting the results

The  script `plot.py`  loads  data  from a  text  file  and builds  an
interactive 3D plot using `matplotlib`.

$./plot.py results.txt

You can turn around the mountain with your mouse.

Notes:

- `matplotlib` doesn't  support logarithmic  3D plots,  so we  have to
  jump through  hoops to get figure  looking a similar to  that of the
  book.
- the  color  scheme  is  "rainbow":   it  goes  from  purple  (lowest
  bandwidth) to  red (highest bandwidth) **regardless**  of the actual
  numbers,  so pay  attention to  the vertical  scale when  looking at
  plots from different machines.

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