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DeepStack artifact evaluation

This repository is the artifact for “DeepStack: Facilitating Co-Design Exploration of 3D DRAM-Stacked Accelerators for Distributed LLM Inference.” It provides a CPU-only workflow for rerunning the supported analytical experiments, checking their outputs, and rendering the paper-facing figures. After environment setup, reproduction does not require a GPU, model weights, an external simulator, or an external plotting workspace.

What the artifact provides

  • GPU kernel/operator and end-to-end LLM prefill/decode modeling with DeepStack and TileSight.
  • Distributed-inference exploration across parallelism, collectives, hierarchical NoC, and stacked-DRAM configurations.
  • Bandwidth, throughput, energy, thermal, capacity, and selected DSE evaluations used by the paper.
  • Public NVIDIA and AMD GPU descriptions plus source-visible interfaces for caller-defined architecture, DRAM, topology, per-level latency/bandwidth, energy, and capacity inputs.
  • A standalone DRAM bank oracle with constructive schedule checks, routing traces, and a self-contained HTML dashboard.
  • Automated environment checks, a short smoke run, a 15-stage reproduction, independent result verification, and PNG/PDF plotting.

Figure preview

All 16 pre-rendered figure groups are available as PNG and PDF files under prebuilt/figures/. They are provided only for browsing; the workflow and verifier never use them as inputs.

H100 validation Decode Pareto DSE heatmaps NoC sensitivity

Quick start

The supported host is x86-64 Linux with glibc 2.29 or newer and Conda. setup.sh creates a CPython 3.11 environment and runs the environment doctor. For a GitHub clone, materialize the large DSE table with Git LFS first; this step is not needed for a fully materialized DOI archive.

git lfs pull
./setup.sh
conda run --no-capture-output -n deepstack-ae ./run.sh quick
conda run --no-capture-output -n deepstack-ae \
  ./run.sh reproduce --workers 32

The full run is intended for a host with about 32 CPU cores, 64 GiB RAM, and 5 GiB free space; use a smaller --workers value when needed. Setup may access the network to install dependencies, while the model runs are repository-local.

Results

The quick run writes to results/quick/. The full workflow writes generated CSVs, logs, manifests, verification summaries, and 16 PNG/PDF figure groups to results/reproduce/.

Recheck or redraw an existing full result bundle with:

conda run --no-capture-output -n deepstack-ae \
  ./verify.sh results/reproduce
conda run --no-capture-output -n deepstack-ae \
  ./run.sh plot --result-dir results/reproduce

The workflow reruns the analytical model. Bundled GPU measurements and ASTRA-Sim/NS-3 outputs are checksummed comparison inputs, not fresh hardware or simulator runs. It reports both the submitted-paper compatibility path and the current corrected path. Exact coverage, source versions, expected outputs, and per-result provenance are in the reproducibility matrix.

Repository layout

Path Contents
ae/ workflow, stage runners, verification, and plotting
src/deepstack/ analytical/DSE model and bank oracle
src/tilesight/ GPU kernel and distributed-performance model
data/, configs/ checksummed references and fixed configurations
tests/ functional and reusable-interface tests
docs/ artifact appendix and detailed result/provenance matrix
prebuilt/figures/ checked-in PNG/PDF previews; never verifier inputs
results/ reviewer-generated outputs; not required as an input

Caller-defined studies can use mosaic.arch.custom_profile, mosaic.noc.custom_profile, mosaic.cost.energy, and DramDsePolicy.

The complete reference area model and die-area breakdown are not distributed; the bundled reference interface returns only feasible SM capacity for covered configurations. Contact the authors for additional area-model coverage.

Documentation and licensing

The artifact appendix is the compact reviewer guide. The archived artifact is identified by DOI 10.5281/zenodo.21351389, and citation metadata are in CITATION.cff.

Mixed-license artifact. Source code, scripts, documentation, manifests, and project data are licensed under Apache-2.0. Four bundled precompiled model-support/capacity libraries are proprietary and licensed separately under LicenseRef-DeepStack-AE-Binary-1.0. The repository as a whole is not Apache-2.0; see LICENSING.md and THIRD_PARTY_NOTICES.md.