SegFold: Artifact Evaluation

SegFold is a cycle-accurate simulator for a sparse-sparse matrix multiplication (SpMSpM) accelerator that uses dynamic routing networks with segment-and-fold mapping. This repository contains the C++ simulator, benchmark matrices, and scripts to reproduce the paper's experimental results.

Quick Start

Run everything in one command (includes build + download + experiments):

./scripts/run_all.sh

Results are written to output/ae_<timestamp>/.

The following steps could be used to build the simulator, download the benchmarks, and run the simulation separately.

# 1. Build the simulator
./scripts/setup.sh

# 2. Download SuiteSparse matrices
python3 scripts/download_matrices.py

# 3. Run all experiments and generate plots
./scripts/run_all.sh --skip-build

Reproduce a Single Figure/Table

The e2e task takes around 2 hours with 16 parallelized cores. To do shorter test, each figure/table has a standalone script that handles everything end-to-end (build, download, simulate, collect, plot):

./scripts/run_figure_overall.sh         # Figure 8:  Overall performance (SegFold vs Spada vs Flexagon)
./scripts/run_figure_nonsquare.sh       # Figure 9:  Non-square matrix performance
./scripts/run_figure_mapping.sh         # Figure 10: Ablation mapping strategy comparison
./scripts/run_figure_breakdown.sh       # Figure 11: Speedup breakdown (incremental ablation)
./scripts/run_figure_crossbar_width.sh  # Figure 12(a): Crossbar width sweep
./scripts/run_figure_window_size.sh     # Figure 12(b): Window size sweep
./scripts/run_table_k_reordering.sh     # Table IV:  K-reordering ablation

All scripts support --jobs N, --skip-build, and --output-dir DIR. See the Step-by-Step Guide for detailed command-line options and experiment descriptions.

Docker

A Docker environment is provided for ease of setup. The container comes with all dependencies pre-installed and the simulator pre-built.

# Build the Docker image
docker compose build

# Run all experiments (results are mounted to ./output on the host)
docker compose run artifact ./scripts/run_all.sh 

# Or reproduce a single figure
docker compose run artifact ./scripts/run_figure_overall.sh

Expected Results

After running the experiments, results are written to output/ae_<timestamp>/:

plots/ — Generated figures (PDF and PNG) for each experiment
*.csv — Collected cycle counts and statistics for each experiment
*_stats.json — Raw simulation output per matrix per configuration

Pre-generated reference results are provided in expected_results/ for comparison:

expected_results/plots/ — Reference figures matching the paper
expected_results/data/ — Reference CSV files with expected cycle counts

Since the simulator is deterministic, the generated CSV files should match the expected results exactly. You can verify this with:

diff output/ae_<timestamp>/fig8_overall_results.csv expected_results/data/fig8_overall_results.csv

Hardware Requirements

Resource	Minimum	Recommended
CPU	4 cores	16+ cores
RAM	64 GB	256 GB
Disk	2 GB	5 GB
OS	Ubuntu 22.04	Ubuntu 22.04+

RAM Note: The breakdown experiment (breakdown-base config with dense tiling) requires up to ~50 GB per process. The mapping ablation requires up to ~40 GB per process. With --jobs 1, 64 GB is sufficient. Higher parallelism requires proportionally more RAM (e.g., --jobs 4 with mapping needs ~160 GB).

Python >= 3.8 is required. All dependencies (cmake, g++, Python packages) are checked and installed automatically by setup.sh. See INSTALL.md for detailed dependency and RAM requirements.

Step-by-Step Guide

Step 1: Build the Simulator

./scripts/setup.sh

This script:

Checks system dependencies (CMake >= 3.15, g++ with C++20, Python 3)
Checks Python packages (numpy, scipy, matplotlib, pyyaml, pandas)
Builds the C++ simulator with Ramulator2 HBM2 DRAM backend
Runs a smoke test to verify the build

After building, the simulator binary is at csegfold/build/csegfold.

Step 2: Download SuiteSparse Matrices

python3 scripts/download_matrices.py

Downloads 20 matrices from the SuiteSparse Matrix Collection into benchmarks/data/suitesparse/. Matrices already present are skipped. These cover all the paper experiments.

Step 3: Run Overall Performance Experiment

Reproduces the paper's overall speedup comparison (SegFold vs Spada vs Flexagon) on 11 SuiteSparse matrices.

python3 scripts/run_overall.py output/my_run
python3 scripts/run_overall.py output/my_run --jobs 4  # parallel

Matrices: fv1, flowmeter0, delaunay_n13, ca-GrQc, ca-CondMat, poisson3Da, bcspwr06, tols4000, rdb5000, psse1, gemat1

Configs:

Most matrices use configs/segfold.yaml (16x16 PE array, 1.5 MB L1 cache, HBM2 DRAM)
Irregular matrices (ca-GrQc, ca-CondMat, poisson3Da) use configs/segfold-ir.yaml (row decomposition, larger tiles, demand scheduling)

Output: output/my_run/overall/sim_{matrix}_stats.json

Note: Only SegFold is simulated. Spada and Flexagon cycle counts are pre-computed and stored in data/baselines/overall_baselines.csv for plotting. The Flexagon baseline corresponds to an earlier configuration used during submission; we are updating it for the camera-ready version. While absolute numbers may change, all results are internally consistent and reproducible.

Step 4: Run Non-Square Performance Experiment

Reproduces the paper's non-square matrix evaluation on 6 rectangular SuiteSparse matrices.

python3 scripts/run_nonsquare.py output/my_run

Matrices: lp_woodw (1098x8418), pcb3000 (3960x7732), gemat1 (4929x10595), Franz6 (7576x3016), Franz8 (16728x7176), psse1 (14318x11028)

Output: output/my_run/nonsquare/sim_{matrix}_stats.json

Spada baseline data is in data/baselines/nonsquare_baselines.csv.

Step 5: Run Speedup Breakdown Experiment

Reproduces the paper's ablation study showing incremental contribution of each optimization on 12 SuiteSparse matrices.

python3 scripts/run_breakdown.py output/my_run

Five configurations are run per matrix, progressively enabling features:

Config	SegmentBC	Spatial Folding	IPM LUT	SelectA
`breakdown-base`
`breakdown-plus-tiling`	X
`breakdown-plus-folding`	X	X
`breakdown-plus-dynmap`	X	X	X
`segfold` (full)	X	X	X	X

Matrices: bcsstk03, bcspwr06, ca-GrQc, tols4000, olm5000, fv1, bcsstk18, lp_d2q06c, lp_woodw, gemat1, rosen10, pcb3000

Output: output/my_run/breakdown/{config}/sim_{matrix}_stats.json

Step 6: Run Ablation Mapping Experiment

Evaluates the impact of different memory-to-PE mapping strategies on 16 SuiteSparse matrices.

python3 scripts/run_ablation.py output/my_run --ablation mapping-paper --jobs 4

Three mapping strategies are compared:

Config	Strategy
`zero`	Zero-Offset
`ideal`	Ideal-Network
`segfold`	SegFold (Ours)

Matrices: fv1, flowmeter0, delaunay_n13, ca-GrQc, ca-CondMat, poisson3Da, bcspwr06, tols4000, rdb5000, bcsstk03, bcsstk18, olm5000, lp_d2q06c, lp_woodw, pcb3000, rosen10

Output: output/my_run/ablation/mapping-paper/{config}/sim_{matrix}_stats.json

Step 7: Run Synthetic Ablation Experiments

Evaluates the impact of window size, crossbar width, and K-reordering on synthetic matrices at sizes 256, 512, 1024 with densities 0.05 and 0.1.

# Window size sweep (1, 4, 8, 16, 32, 64)
python3 scripts/run_ablation.py output/my_run --ablation window-size --jobs 4

# Crossbar width sweep (1, 2, 4, 8, 16)
python3 scripts/run_ablation.py output/my_run --ablation crossbar-width --jobs 4

# K-reordering strategies
python3 scripts/run_ablation.py output/my_run --ablation k-reordering --jobs 4

Output: output/my_run/ablation/{window-size,crossbar-width,k-reordering}/{config}/sim_*_stats.json

Step 8: Collect Results

python3 scripts/collect_results.py output/my_run

Parses all *_stats.json files and produces:

fig8_overall_results.csv — Figure 8: SegFold cycle counts for overall performance
fig9_nonsquare_results.csv — Figure 9: SegFold cycle counts for non-square matrices
fig10_ablation_mapping_results.csv — Figure 10: Ablation mapping strategy comparison
fig11_breakdown_results.csv — Figure 11: Cycle counts per config per matrix (pivoted)
fig12a_ablation_crossbar_width_results.csv — Figure 12(a): Crossbar width sweep results
fig12b_ablation_window_size_results.csv — Figure 12(b): Window size sweep results
tab4_k_reordering_results.csv — Table IV: K-reordering strategy results

Step 9: Generate Plots

python3 scripts/plot_overall.py output/my_run
python3 scripts/plot_nonsquare.py output/my_run
python3 scripts/plot_breakdown.py output/my_run
python3 scripts/plot_ablation_mapping.py output/my_run
python3 scripts/plot_ablation.py output/my_run

Generates PDF and PNG figures in output/my_run/plots/:

fig8_overall_speedup.pdf — Figure 8: SegFold vs Spada vs Flexagon (normalized to Spada)
fig9_nonsquare_speedup.pdf — Figure 9: SegFold vs Spada on rectangular matrices
fig10_ablation_mapping.pdf — Figure 10: Mapping strategy comparison
fig11_breakdown_speedup.pdf — Figure 11: Incremental speedup per optimization
fig12a_ablation_crossbar_width.pdf — Figure 12(a): Crossbar width sweep (normalized cycles)
fig12b_ablation_window_size.pdf — Figure 12(b): Window size sweep (normalized cycles)
tab4_k_reordering.txt — Table IV: K-reordering summary

Experiment-to-Paper Mapping

Script	Paper Reference	Output Files	Description
`run_overall.py`	Figure 8	`fig8_overall_results.csv`, `fig8_overall_speedup.pdf`	SegFold vs Spada vs Flexagon on 11 matrices
`run_nonsquare.py`	Figure 9	`fig9_nonsquare_results.csv`, `fig9_nonsquare_speedup.pdf`	SegFold vs Spada on 6 rectangular matrices
`run_ablation.py --ablation mapping-paper`	Figure 10	`fig10_ablation_mapping_results.csv`, `fig10_ablation_mapping.pdf`	Mapping strategy comparison (3 x 16)
`run_breakdown.py`	Figure 11	`fig11_breakdown_results.csv`, `fig11_breakdown_speedup.pdf`	Incremental ablation (5 configs x 12 matrices)
`run_ablation.py --ablation crossbar-width`	Figure 12(a)	`fig12a_ablation_crossbar_width.pdf`	B loader row limit (5 configs, synthetic)
`run_ablation.py --ablation window-size`	Figure 12(b)	`fig12b_ablation_window_size.pdf`	B loader window size (6 configs, synthetic)
`run_ablation.py --ablation k-reordering`	Table IV	`tab4_k_reordering.txt`	K-reorder strategies (3 configs, synthetic)

Configuration

Command-Line Options

All experiment scripts accept these options:

Option	Default	Description
`--jobs N`	2	Max parallel simulations (1 = sequential)
`--config PATH`	`configs/segfold.yaml`	SegFold configuration file
`--matrix-dir PATH`	`benchmarks/data/suitesparse`	SuiteSparse matrix directory
`--timeout SEC`	3600	Timeout per simulation in seconds

run_overall.py also accepts --config-ir PATH (default: configs/segfold-ir.yaml) for irregular matrices.

Running a Single Matrix

./csegfold/build/csegfold \
    --config configs/segfold.yaml \
    --mtx-file benchmarks/data/suitesparse/ca-GrQc/ca-GrQc.mtx

Use --tmp-dir <path> to control where stats/config JSON files are saved (default: csegfold/tmp/).

Repository Structure

SegFold-AE/
├── README.md
├── INSTALL.md                       # Detailed build & dependency guide
├── Dockerfile / docker-compose.yml
├── csegfold/                        # C++ simulator
│   ├── CMakeLists.txt
│   ├── src/
│   │   ├── main.cpp                 # Main simulator (csegfold binary)
│   │   ├── modules/                 # PE, switch, spad, mapper, LUT, ...
│   │   ├── simulator/               # Cycle-accurate core
│   │   ├── matrix/                  # Matrix generation & I/O
│   │   └── memory/                  # Cache + Ramulator2 backend
│   └── include/
├── configs/
│   ├── segfold.yaml                 # Full SegFold config
│   ├── segfold-ir.yaml              # Config for irregular matrices
│   ├── breakdown-base.yaml          # All optimizations OFF
│   ├── breakdown-plus-tiling.yaml   # + dynamic tiling
│   ├── breakdown-plus-folding.yaml  # + spatial folding
│   ├── breakdown-plus-dynmap.yaml   # + dynamic routing
│   └── ramulator2-hbm.yaml         # HBM2 DRAM config
├── benchmarks/data/
│   └── suitesparse/                 # SuiteSparse .mtx files
├── data/baselines/
│   ├── overall_baselines.csv        # Pre-computed Spada/Flexagon cycles
│   └── nonsquare_baselines.csv      # Pre-computed Spada cycles
├── scripts/
│   ├── setup.sh                     # Build & verify
│   ├── run_all.sh                   # One-command full reproduction
│   ├── run_figure_overall.sh        # Standalone: overall performance figure
│   ├── run_figure_nonsquare.sh      # Standalone: non-square performance figure
│   ├── run_figure_breakdown.sh      # Standalone: speedup breakdown figure
│   ├── run_figure_mapping.sh        # Standalone: ablation mapping figure
│   ├── run_figure_window_size.sh    # Standalone: window size ablation figure
│   ├── run_figure_crossbar_width.sh # Standalone: crossbar width ablation figure
│   ├── run_table_k_reordering.sh    # Standalone: k-reordering ablation table
│   ├── download_matrices.py         # Download SuiteSparse matrices
│   ├── run_overall.py               # Overall performance (11 matrices)
│   ├── run_nonsquare.py             # Non-square performance (6 matrices)
│   ├── run_breakdown.py             # Speedup breakdown (5 x 12)
│   ├── run_ablation.py              # Ablation experiments
│   ├── collect_results.py           # JSON stats -> CSV
│   ├── plot_overall.py              # Overall speedup figure
│   ├── plot_nonsquare.py            # Non-square speedup figure
│   ├── plot_breakdown.py            # Breakdown stacked bar figure
│   ├── plot_ablation_mapping.py     # Ablation mapping figure
│   └── plot_ablation.py             # Synthetic ablation figures
├── expected_results/                # Reference outputs
│   ├── plots/                       # Expected figures (PDF + PNG)
│   └── data/                        # Expected CSV results
└── hardware/                        # RTL & synthesis reports
    ├── rtl/
    └── reports/

Expected Runtime

Measured on a 16-core machine with 256 GB RAM (--jobs 16, auto-detected):

Experiment	Script	Runs	Time	Peak RAM / proc
Overall performance	`run_figure_overall.sh`	11	~26 min	5 GB
Non-square performance	`run_figure_nonsquare.sh`	6	~11 min	5 GB
Speedup breakdown	`run_figure_breakdown.sh`	60	~13 min	50 GB
Ablation mapping	`run_figure_mapping.sh`	48	~18 min	40 GB
Window size ablation	`run_figure_window_size.sh`	36	~14 min	4 GB
Crossbar width ablation	`run_figure_crossbar_width.sh`	30	~15 min	4 GB
K-reordering ablation	`run_table_k_reordering.sh`	18	~14 min	2 GB
Total (`run_all.sh`)		209	~2 hours

With fewer cores or lower --jobs, runtimes scale roughly linearly. The breakdown and mapping experiments are the most memory-intensive.

Hardware Synthesis Reports

The hardware/ directory contains RTL source files and synthesis reports for the SegFold architecture modules:

hardware/rtl/ — SystemVerilog source files for all SegFold modules (PE, switch, scratchpad, LUT, memory controller, etc.)
hardware/reports/ — Synthesis reports (area, power, timing, QoR) for each module, generated with Synopsys Design Compiler using the ASAP 7nm standard cell library
hardware/reports/cacti/ — CACTI SRAM modeling results for scratchpad and FIFO buffers (22nm/32nm)

These reports correspond to the area and power numbers presented in the paper. No simulation is required to view them.

License

See LICENSE.

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Repository files navigation

SegFold: Artifact Evaluation

Quick Start

Reproduce a Single Figure/Table

Docker

Expected Results

Hardware Requirements

Step-by-Step Guide

Step 1: Build the Simulator

Step 2: Download SuiteSparse Matrices

Step 3: Run Overall Performance Experiment

Step 4: Run Non-Square Performance Experiment

Step 5: Run Speedup Breakdown Experiment

Step 6: Run Ablation Mapping Experiment

Step 7: Run Synthetic Ablation Experiments

Step 8: Collect Results

Step 9: Generate Plots

Experiment-to-Paper Mapping

Configuration

Command-Line Options

Running a Single Matrix

Repository Structure

Expected Runtime

Hardware Synthesis Reports

License

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Name		Name	Last commit message	Last commit date
Latest commit History 30 Commits
benchmarks/data/suitesparse		benchmarks/data/suitesparse
configs		configs
csegfold		csegfold
data/baselines		data/baselines
expected_results		expected_results
hardware		hardware
scripts		scripts
.dockerignore		.dockerignore
.gitignore		.gitignore
Dockerfile		Dockerfile
INSTALL.md		INSTALL.md
LICENSE		LICENSE
README.md		README.md
abstract.txt		abstract.txt
docker-compose.yml		docker-compose.yml
entrypoint.sh		entrypoint.sh
requirements.txt		requirements.txt

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SegFold: Artifact Evaluation

Quick Start

Reproduce a Single Figure/Table

Docker

Expected Results

Hardware Requirements

Step-by-Step Guide

Step 1: Build the Simulator

Step 2: Download SuiteSparse Matrices

Step 3: Run Overall Performance Experiment

Step 4: Run Non-Square Performance Experiment

Step 5: Run Speedup Breakdown Experiment

Step 6: Run Ablation Mapping Experiment

Step 7: Run Synthetic Ablation Experiments

Step 8: Collect Results

Step 9: Generate Plots

Experiment-to-Paper Mapping

Configuration

Command-Line Options

Running a Single Matrix

Repository Structure

Expected Runtime

Hardware Synthesis Reports

License

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