PBBCache is a cache-partitioning simulator that relies on offline-collected application performance data (e.g., instructions per cycle, memory bandwidth consumption, etc.) to determine the degree of throughput, fairness or other relevant metrics for a workload under a particular partitioning algorithm. A key feature of the simulator is its ability to efficiently determine the optimal cache-partitioning solution for different optimizations objectives (e.g. throughput or fairness) using a parallel branch-and-bound algorithm.
The main purpose of the simulator is twofold. Firstly, it has been designed as a tool to efficiently assess the real potential of existing cache-partitioning approaches and to identify their limitations, by comparing their effectiveness with that of the optimal solution provided by the simulator. Secondly, the ability to study the optimal solution for various optimization metrics makes the simulator a powerful tool to aid in the design of new cache-partitioning techniques.
PBBCache supports Python 3 or greater. We recomend the following steps:
- Create virtual env and activate it:
> cd proyectos/pbbcache/
> cd simulator/
> python3.8 -m venv venv3
> . venv3/bin/activate- Install dependencies inside venv:
> pip install --upgrade pip
> pip install matplotlib pandas numpy sympy ipyparallel sklearn datetime pytz- For some systems, you will also need to install BLIS, as numpy depends on it:
> sudo apt install libblis3 libblis3-pthreadA regular use of the simulator would include the following steps:
Include the project common functions module:
$ cd simulator; . shrc
Start 4 slave (engine) processes:
$ ipcluster start -n 4 --daemonize
Launch the simulator:
$ ./test/sim.py -s input/metrics.csv input/workloads.csv -a opt-stp-bf,ucp -f table -b 35000 -p -m simple -r 1
W# Algorithm BenchID Name Cluster Mask/NR_WAYS SlowdownNB/ANTT STP Slowdown
W1 opt-stp-bf 1 sphinx306 1 0x700(3) 1.07895 0.91197 1.09653
W1 opt-stp-bf 2 lbm06 2 0x80(1) 1.0097 0.957 1.04493
W1 opt-stp-bf 3 libquantum06 3 0x40(1) 1.16813 0.807 1.23916
W1 opt-stp-bf 4 applu00 4 0x20(1) 1.04627 0.89354 1.11914
W1 opt-stp-bf 5 soplex06 5 0x1e(4) 1.18749 0.81842 1.22186
W1 opt-stp-bf 6 milc06 6 0x1(1) 1.02608 0.88443 1.13067
W1 opt-stp-bf 7 OVERALL #88 3.087099s 1.14205 5.27237 1.18588
W1 ucp 1 sphinx306 1 0x600(2) 1.11045 0.8617 1.1605
W1 ucp 2 lbm06 2 0x180(2) 1.00697 0.96071 1.0409
W1 ucp 3 libquantum06 3 0x40(1) 1.16813 0.80426 1.24338
W1 ucp 4 applu00 4 0x20(1) 1.04627 0.89029 1.12323
W1 ucp 5 soplex06 5 0x1e(4) 1.18749 0.81715 1.22376
W1 ucp 6 milc06 6 0x1(1) 1.02608 0.87991 1.13648
W1 ucp 7 OVERALL #1 0.001563s 1.15471 5.21402 1.19452
There are many available command line options, in this example we have used the most basic ones:
- -s specifies a metrics file, that contains a table where each row store the values of various runtime metrics gathered with performance monitoring counters when a specific application runs alone with a fixed number of cache ways.
- The following parameter (required always) is the workloads file, which specifies the different workloads (one per line) that will be considered for the simulation; each workload is encoded as a comma-separated list of program names.
- -a defines the set of partitioning schemes to simulate.
- -f is the output mode
- -p activates the parallel execution of the branch and bound algorithm to calculate the optimal partitioning.
- -m specifies the bandwidth model to be applied, in this case the one based on the stall metric.
- -r allows to only execute a defined range of workloads. It is defined as a comma-separated list and allows to define ranges with - (e.g., if we have 10 workloads and define the flag -r 1,3,5-7,9- we will end up running the workloads 1,3,5,6,7,9,10).
To find all of them you can run the following command:
$ ./test/sim.py -h
usage: sim.py [-h] [-s SUMMARY_FILE] [-b MAX_BANDWIDTH] [-p] [-P] [-H] [-C]
[-W] [-L] [-m BW_MODEL] [-a ALGORITHMS] [-f FORMAT] [-d]
[-r USE_RANGE] [-w FORCE_WAYS] [-O key=val]
WORKLOAD_FILE
Test main file for simulator (UCP, Whirlpool, etc.)
positional arguments:
WORKLOAD_FILE a workload file
optional arguments:
-h, --help show this help message and exit
-s SUMMARY_FILE, --summary-file SUMMARY_FILE
File with the data collected offline for every
benchmark
-b MAX_BANDWIDTH, --max-bandwidth MAX_BANDWIDTH
Max bandwidth observed for the target machine
-p, --parallel Enable parallel search for the optimal
-P, --parsim Launch all simulations in parallel
-H, --harness Use harness workload file as input
-C, --generate-chart Enable the STP-vs-UNF chart generator
-W, --show-window Show matplotlib window with chart (must be used along
with -C)
-L, --list-algorithms
List algorithms implemented in the simulator.
-m BW_MODEL, --bw-model BW_MODEL
Select BW model to be applied (['simple', 'stalls'])
-a ALGORITHMS, --algorithms ALGORITHMS
Comma-separated algorithms (['opt-stp', 'yu-petrov',
'opt-unf', 'ucp', 'ucp-slowdown', 'kpart',
'whirlpool', 'whirlpool-c', 'equal-part', 'optc-stp',
'optc-unf', 'opt-unf-np', 'opt-stp-np', 'yu-petrov',
'user', 'kpart-opt', 'opt-stp-bf', 'opt-unf-bf',
'optc-stp-bf', 'optc-unf-bf'])
-f FORMAT, --format FORMAT
Format style for output
-d, --debugging Enable debugging mode (assume normal input files) and
keep workload 0
-r USE_RANGE, --use-range USE_RANGE
Pick selected workloads only by specifying a range
-w FORCE_WAYS, --force-ways FORCE_WAYS
Force number of ways of the cache (it must be smaller
or equal than the maximum number of ways inferred from
input file)
-O key=val, --option key=val
Use generic algorithm-specific options
- Jorge Casas Hernan (jorcasas@ucm.es)
- Adrian Garcia Garcia (adriagar@ucm.es)
- Juan Carlos Saez Alcaide (jcsaezal@ucm.es)
- Carlos Bilbao (cbilbao@ucm.es)