This is a tool to extract GEM5 & XS performance counter from the output of GEM5 & XS simulation.

Examples to extract GEM5 & XS performance counter

We use batch.py to extract the performance counter for each checkpoint.

Get full option list of batch.py with

batch.py -h

To use batch.py anywhere, you can add gem5_data_proc to you PATH:

export PATH='/path/to/gem5_data_proc':$PATH

Use batch.py to extract GEM5's cache performance counters:

batch.py -s /path/to/results/top/directory  --cache -f stats.txt

Include only a specific benchmark like gromacs:

batch.py -s /path/to/results/top/directory  --cache -f stats.txt -F gromacs

Use batch.py to extract XS's cache & branch performance counters:

batch.py -s /path/to/results/top/directory --cache --branch --xiangshan -f simulator_err.txt

Example for eval targets

The eval targets trick makes use of Python's eval to avoid creating new options for every new stat group

Use eval target to extract GEM5's memory bandwidth:

batch.py -s /path/to/results/top/directory --eval-stat mem_targets

Using eval target to extract GEM5's memory bandwidth and memory dependency counters:

batch.py -s /path/to/results/top/directory --eval-stat mem_targets#mem_dep_targets

Using eval target to extract XS's memory bandwidth and memory dependency counters:

batch.py -s /path/to/results/top/directory -X --eval-stat mem_targets#mem_dep_targets

Compute weighted performance

Unified weighted metric computation with batch.py

Now we use batch.py to compute the performance for each checkpoint. Then we use simpoint_cpt/compute_weighted.py to compute weighted metrics and scores Example usage here:

export PYTHONPATH=`pwd`

example_stats_dir=/nfs-nvme/home/share/zyy/gem5-results/example-outputs

mkdir -p results

python3 batch.py -s $example_stats_dir -t --topdown-raw -o results/example.csv  # The topdown results for each checkpoint

python3 simpoint_cpt/compute_weighted.py \
    -r results/example.csv \
    -j simpoint_cpt/resources/spec06_rv64gcb_o2_20m.json \
    -o results/example-weighted.csv  # The weighted topdown counters for each benchmark

python3 simpoint_cpt/compute_weighted.py \
    -r results/example.csv \
    -j simpoint_cpt/resources/spec06_rv64gcb_o2_20m.json \
    --score results/example-score.csv  # The SPEC score for each benchmark and overll score

Analysis topdown performance

First, we need to get the topdown outputs for one tests

bash example-scripts/gem5-topdown-tag.sh spec_ideal_numBr6

Then, we can use topdown/draw_new.py to analyze the topdown performance, and draw pictures, save to figure/

python3 topdown/draw_new.py -f=1 -p  -t1=spec_ideal_numBr4 -t2=spec_ideal_numBr6
# -f=1 means the highest level of detail
# -p means print the level 1 percentage and diff two tags outputs
# -t1=spec_ideal_numBr4 means the first tag
# -t2=spec_ideal_numBr6 means the second tag

python3 topdown/draw_new.py -f=3 -c=Frontend -t1=spec_ideal_numBr4 -t2=spec_ideal_numBr6
# -f=3 means the most detailed level
# -c=Frontend means the category, choises: Frontend, Backend, BadSpec

Dual-core performance

stats parser will obtain XS_CORE_ID from environment variables to choose which core to compute score:

export XS_CORE_ID
python3 batch.py -s $example_stats_dir -o results/$tag-core$core.csv -X

Full scripts to obtain dual-core performance is in example-scripts/xs-dual-core.sh

How to add more interested stats

Simple stats target group

See cache_targets defined in utils/target_stats.py and its usage in batch.py.

Simple stats target group contains a list of targets. Each entry of the list is a regex. batch.py will ``search'' for the pattern in given stats file, and name it with the first match group in parentheses. For example

(l3\.demandAcc)esses::total'
        ^
        The first match group, used as name

Complex stats target

Complex stats target group is a dictionary. The key of an entry is the name of the target. The value of an entry has two possible types: list or str.

If str, it is the regex to search. (xs_cache_targets_nanhu in utils/target_stats.py is an example.)

If list, like xs_cache_targets_22_04_nanhu, value[0] is the regex to search, while values[1] is how many times such pattern repeats. This is to handle the case that one pattern repeats multiple times in specific version of XS. The occurs because the performance counter of different banks of L2/L3 caches are named the same. Because this is to handle the buggy behavior in RTL, this type of stats group is rarely used and is out of maintained.

Assumed directory structure

A typical directory structure of GEM5 results looks like:

.
|-- bwaves_1299
|   |-- completed
|   |-- dcache_miss.db
|   |-- dramsim3.json
|   |-- dramsim3.txt
|   |-- dramsim3epoch.json
|   |-- log.txt
|   `-- m5out
|       |-- TableHitCnt.txt
|       |-- altuseCnt.txt
|       |-- config.ini
|       |-- config.json
|       |-- misPredIndirect.txt
|       |-- misPredIndirectStream.txt
|       |-- missHistMap.txt
|       |-- stats.txt
|       |-- topMisPredictHist.txt
|       `-- topMisPredicts.txt
`-- gcc_2000
    |-- completed
    |-- dcache_miss.db
    |-- dramsim3.json
    |-- dramsim3.txt
    |-- dramsim3epoch.json
...

A typical directory structure of XS looks like:

.
|-- GemsFDTD_1041040000000_0.022405
|   |-- simulator_err.txt
|   `-- simulator_out.txt
|-- GemsFDTD_1121140000000_0.004928
|   |-- simulator_err.txt
|   `-- simulator_out.txt
|-- GemsFDTD_1175660000000_0.022268
|   |-- simulator_err.txt
|   `-- simulator_out.txt
...