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Clone the RapidChiplet repository:

git clone https://github.com/spcl/rapidchiplet.git

Install all requirements using pip:

cd rc pip install -r requirements.txt

Build the BookSim2 [1,2] simulator:

cd booksim2/srcmakecd ../../

Build Netrace [3,4]

cd netracegcc export_trace.c netrace.c -o export_tracecd ../

python3 reproduce_paper_results.py

• Note that this script runs for almost one day.
• The results might slightly differ from the paper due to different system specifications.
• The plots that appear in the paper in Figure 4 (right), Figure 5, and Figure 6 will be stored in the./plots/ directory.
• The chip visualization that appears in the paper in Figure 4 (left) will be stored in the./images/ directory.

Configure your chip design using the different input files. Check out the example files in./inputs/ to get started.

We provide the following input generation scripts for more complex input-files that cannot easily be written by hand:

generate_routing.py: Generates a routing table for a given chip design

python3 generate_routing.py -df inputs/designs/<design_file> -rtf<routing_table_file> -ra<routing_algorithm>

• The<design file> points to all inputs that the routing table generator needs (chiplets, placement, topology).
• The<routing_table_file> is the name under which the resulting routing table is stored (ininputs/routing_tables/).
• <routing algorithm> specifies the routing algorithm to be used. We currently support two routing algorithms:
  • splif: Shortest Path Lowest ID first
  • sptmr: Shortest Path Turn Model Random

generate_traffic.py: Generate a synthetic traffic pattern for a given chip design

python3 generate_traffic.py -df inputs/designs/<design_file> -tf<traffic_file> -tp<traffic_pattern> -par<parameters>

• The<design file> points to all inputs that the traffic generator needs (chiplets, placement).
• The<traffic_file> is the name under which the resulting traffic pattern is stored (in/inputs/traffic_by_chiplet/ andinputs/traffic_by_unit/).
• <traffic_pattern> specifies the traffic pattern to be generated. We currently support four traffic patterns:random_uniform,transpose,permutation,hotspot.
• <parameters> are specific to the selected traffic pattern.

python3 rapidchiplet.py -df inputs/designs/<design_file> -rf<results_file> [-as] [-ps] [-ls] [-c] [-l] [-t]

• The<design_file> points to all inputs that are required
• The<results_file> specifies the name, under which the results are stored (in/results/).
• The optional flags are used to enable the computation of different metrics: area summary (-as), power summary (-ps), link summary (-ls), manufacturing cost (-c), latency (-l), throughput (-t).

To export a design to BookSim and gather the results, simply runrapidchiplet.py with the-bs flag:

python3 rapidchiplet.py -df inputs/designs/<design_file> -rf<results_file> -bs

• The<design_file> points to all inputs that are required.
• The<results_file> specifies the name, under which the results are stored (in/results/).

Specify parameters and parameter-ranges for your design space exploration in an experiment-file in the./experiments/ directory. Check out the provided example files to get started.

python3 run_experiment.py -e experiments/<experiment_file>

This script generates one results-file for each combination of input parameters. All result-files are stored in./results/.

Download the traces from the netrace website [5] and store them in./netrace/traces_in/.

In a first step, export the traces from the netrace format into an intermediate format:

cd netrace./export_trace traces_in/<trace_name>.tra.bz2<trace_region_id><packet_limit>> traces_out/<trace_name>.jsoncd ../

• Netrace traces contain one or multiple trace regions. Use the<trace_region_id> argument to specify the region to export. If you want to export the whole trace, omit this argument.
• Some Netrace traces are very long. If you only want to export a partial trace region, use the<packet_limit> argument to pass the maximum number of packets that should be exported.

In a second step, the trace is parsed into the RapidChiplet format:

python3 parse_netrace_trace.py -df inputs/designs/<design_file> -if netrace/traces_out/<trace_name>.json -of<trace_name>.json

• The<design file> points to all inputs that the trace parser needs.
• The arguments-if and-of refer to the input-trace-file (in the intermediate format) and the output-trace-file (in the output format). The output trace file is stored ininputs/traces/.

Visualize a complete design by running

python3 visualizer.py -df inputs/designs/<design_name> [-sci] [-spi]

• You can show chiplet-IDs and PHY-IDs by passing the-sci and-spi flags respectively.

You can also visualize a single chiplet by running

python3 visualizer.py -cf inputs/chiplets/<chiplet_file> -cn<chiplet_name>

• <chiplet_file> is an input file which potentially specifies multiple chiplets and<chiplet_name> is the name of one specific chiplet within this file.

Visualize the results by running:

python3 create_plots.py -rf results/<results-file> -pt<plot_type>

• The<results_file> contains the results you want to visualize.
• Currently, only one plot type, namely,latency_vs_load is supported, but more will be added soon.

[1] Jiang, N., Becker, D.U., Michelogiannakis, G., Balfour, J., Towles, B., Shaw, D.E., Kim, J. and Dally, W.J., 2013, April. A detailed and flexible cycle-accurate network-on-chip simulator. In 2013 IEEE international symposium on performance analysis of systems and software (ISPASS) (pp. 86-96). IEEE.

[2]https://github.com/booksim/booksim2

[3] Hestness, J., Grot, B. and Keckler, S.W., 2010, December. Netrace: dependency-driven trace-based network-on-chip simulation. In Proceedings of the Third International Workshop on Network on Chip Architectures (pp. 31-36).

[4]https://github.com/booksim/netrace

[5]https://www.cs.utexas.edu/~netrace/